934 resultados para Capture probability


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Tick-borne encephalitis (TBE) is one of the most dangerous human neurological infections occurring in Europe and Northern parts of Asia with thousands of cases and millions vaccinated against it. The risk of TBE might be assessed through analyses of the samples taken from wildlife or from animals which are in close contact with humans. Dogs have been shown to be a good sentinel species for these studies. Serological assays for diagnosis of TBE in dogs are mainly based on purified and inactivated TBEV antigens. Here we describe novel dog anti-TBEV IgG monoclonal antibody (MAb)-capture assay which is based on TBEV prME subviral particles expressed in mammalian cells from Semliki Forest virus (SFV) replicon as well as IgG immunofluorescence assay (IFA) which is based on Vero E6 cells transfected with the same SFV replicon. We further demonstrate their use in a small-scale TBEV seroprevalence study of dogs representing different regions of Finland. Altogether, 148 dog serum samples were tested by novel assays and results were compared to those obtained with a commercial IgG enzyme immunoassay (EIA), hemagglutination inhibition test and IgG IFA with TBEV infected cells. Compared to reference tests, the sensitivities of the developed assays were 90-100% and the specificities of the two assays were 100%. Analysis of the dog serum samples showed a seroprevalence of 40% on Åland Islands and 6% on Southwestern archipelago of Finland. In conclusion, a specific and sensitive EIA and IFA for the detection of IgG antibodies in canine sera were developed. Based on these assays the seroprevalence of IgG antibodies in dogs from different regions of Finland was assessed and was shown to parallel the known human disease burden as the Southwestern archipelago and Åland Islands in particular had considerable dog TBEV antibody prevalence and represent areas with high risk of TBE for humans.

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This presentation was made at the Connecticut State Library Service Center, Willimantic, CT, April 14, 2009. It focused on digital capture workflows for both archival and derivative image creation using accepted current standards. Tools used were inexpensive by choice and focused towards the needs of small to mid-sized cultural heritage institutions who wish to begin digital capture in their own facilities.

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Lake La Thuile, in the Northern French Prealps (874 m a.s.l.), provides an 18 m long sedimentary sequence spanning the entire Lateglacial/Holocene period. The high resolution multi-proxy (sedimentological, palynological, geochemical) analysis of the uppermost 6.2 meters reveals the Holocene dynamics of erosion in the catchment in response to landscape modifications. The mountain belt is at relevant altitude to study past human activities and the watershed is sufficiently disconnected from large valleys to capture a local sedimentary signal. From 12,000 to 10,000 cal. BP (10 to 8 ka cal. BC), the onset of hardwood species triggered a drop in erosion following the Lateglacial/Holocene transition. From 10,000 to 4500 cal. BP (8 to 2.5 ka cal. BC), the forest became denser and favored slope stabilization while erosion processes were very weak. A first erosive phase was initiated at ca . 4500 cal. BP without evidence of human presence in the catchment. Then, the forest declined at approximately 3000 cal. BP, suggesting the first human influence on the landscape. Two other erosive phases are related to anthropic activities: approximately 2500 cal. BP (550 cal. BC) during the Roman period and after 1600 cal. BP (350 cal. AD) with a substantial accentuation in the Middle Ages. In contrast, the lower erosion produced during the Little Ice Age, when climate deteriorations are generally considered to result in an increased erosion signal in this region, suggests that anthropic activities dominated the erosive processes and completely masked the natural effects of climate on erosion in the late Holocene.

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reduce costs and labor associated with predicting the genotypic mean (GM) of a synthetic variety (SV) of maize (Zea mays L.), breeders can develop SVs from L lines and s single crosses (SynL,SC) instead of L+2s lines (SynL). The objective of this work was to derive and study formulae for the inbreeding coefficient (IC) and GM of SynL,SC, SynL, and the SV derived from (L+2s)/2 single crosses (SynSC). All SVs were derived from the same L+2s unrelated lines whose IC is FL, and each parent of a SV was represented by m plants. An a priori probability equation for the IC was used. Important results were: 1) the largest and smallest GMs correspond to SynL and SynL,SC, respectively; 2) the GM predictors with the largest and intermediate precision are those for SynL and SynL,SC, respectively; 3) only when FL=1, or m is large, SynL and SynSC are the same population, but only with SynSC prediction costs and labor undergo the maximum decrease, although its prediction precision is the lowest. To determine the SV to be developed, breeders should also consider the availability of lines, single crosses, manpower and land area; besides budget, target farmers, target environments, etc.

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Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.