Line bisection in simulated homonymous hemianopia

Hemianopic patients make a systematic error in line bisection, showing a contra-lesional bias towards their blind side, which is the opposite of that in hemineglect patients. This error has been attributed variously to the visual field defect, to long-term strategic adaptation, or to independent effects of damage to extrastriate cortex. To determine if hemianopic bisection error can occur without the latter two factors, we studied line bisection in healthy subjects with simulated homonymous hemianopia using a gaze-contingent display, with different line-lengths, and with or without markers at both ends of the lines. Simulated homonymous hemianopia did induce a contra-lesional bisection error and this was associated with increased fixations towards the blind field. This error was found with end-marked lines and was greater with very long lines. In a second experiment we showed that eccentric fixation alone produces a similar bisection error and eliminates the effect of line-end markers. We conclude that a homonymous hemianopic field defect alone is sufficient to induce both a contra-lesional line bisection error and previously described alterations in fixation distribution, and does not require long-term adaptation or extrastriate damage.

Neocartilage formation in 1 g, simulated, and microgravity environments: implications for tissue engineering

The aim of this study was to analyze and compare the deposition of cartilage-specific extracellular matrix components and cellular organization in scaffold-free neocartilage produced in microgravity and simulated microgravity.

Rapid adaptation of visual search in simulated hemianopia

Patients with homonymous hemianopia have altered visual search patterns, but it is unclear how rapidly this develops and whether it reflects a strategic adaptation to altered perception or plastic changes to tissue damage. To study the temporal dynamics of adaptation alone, we used a gaze-contingent display to simulate left or right hemianopia in 10 healthy individuals as they performed 25 visual search trials. Visual search was slower and less accurate in hemianopic than in full-field viewing. With full-field viewing, there were improvements in search speed, fixation density, and number of fixations over the first 9 trials, then stable performance. With hemianopic viewing, there was a rapid shift of fixation into the blind field over the first 5-7 trials, followed by continuing gradual improvements in completion time, number of fixations, and fixation density over all 25 trials. We conclude that in the first minutes after onset of hemianopia, there is a biphasic pattern of adaptation to altered perception: an early rapid qualitative change that shifts visual search into the blind side, followed by more gradual gains in the efficiency of using this new strategy, a pattern that has parallels in other studies of motor learning.

CT patterns of fungal pulmonary infections of the lung: Comparison of standard-dose and simulated low-dose CT

To assess the effect of radiation dose reduction on the appearance and visual quantification of specific CT patterns of fungal infection in immuno-compromised patients.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.