Psychophysical analysis of contrast processing segregated into magnocellular and parvocellular systems in asymptomatic carriers of 11778 Leber`s hereditary optic neuropathy

We examined achromatic contrast discrimination in asymptomatic carriers of 11778 Leber`s hereditary optic neuropathy (LHON 18 controls) and 18 age-match were also tested. To evaluate magnocellular (MC) and Parvocellular (PC) contrast discrimination, we used a version of Pokorny and Smith`s (1997) Pulsed/steady-pedestal paradigms (PPP/SPP) thought to be detected via PC and MC pathways, respectively. A luminance pedestal (four 1 degrees x 1 degrees squares) was presented on a 12 cd/m(2) surround. The luminance of one of the squares (trial square, TS) was randomly incremented for either 17 or 133 ms. Observers had to detect the TS, in a forced-choice task, at each duration, for three pedestal levels: 7, 12, 19 cd/m(2). In the SPP, the pedestal was fixed, and the TS was modulated. For the PPP, all four pedestal squares pulsed for 17 or 133 ms, and the TS was simultaneously incremented or decremented. We found that contrast discrimination thresholds of LHON carriers were significantly higher than controls` in the condition with the highest luminance of both paradigms, implying impaired contrast processing with no evidence of differential sensitivity losses between the two systems. Carriers` thresholds manifested significantly longer temporal integration than controls in the SPP, consistent with slowed MC responses. The SPP and PPP paradigms can identify contrast and temporal processing deficits in asymptomatic LHON carriers, and thus provide an additional tool for early detection and characterization of the disease.

Integration of spatial and temporal models to predict extreme water table depths

The objective of this work was to evaluate extreme water table depths in a watershed, using methods for geographical spatial data analysis. Groundwater spatio-temporal dynamics was evaluated in an outcrop of the Guarani Aquifer System. Water table depths were estimated from monitoring of water levels in 23 piezometers and time series modeling available from April 2004 to April 2011. For generation of spatial scenarios, geostatistical techniques were used, which incorporated into the prediction ancillary information related to the geomorphological patterns of the watershed, using a digital elevation model. This procedure improved estimates, due to the high correlation between water levels and elevation, and aggregated physical sense to predictions. The scenarios showed differences regarding the extreme levels - too deep or too shallow ones - and can subsidize water planning, efficient water use, and sustainable water management in the watershed.

Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures.