Dynamic attentional modulation of vision across space and time after right hemisphere stroke and in ageing

This article is available open access and is shared under a Creative Commons licence (http://creativecommons.org/licenses/by/3.0/). Copyright @ 2012 Elsevier Ltd.Introduction - Attention modulates the availability of sensory information to conscious perception. In particular, there is evidence of pathological, spatial constriction of the effective field of vision in patients with right hemisphere damage when a central task exhausts available attentional capacity. In the current study we first examined whether this constriction might be modulated across both space and time in right hemisphere stroke patients without neglect. Then we tested healthy elderly people to determine whether non-pathological ageing also leads to spatiotemporal impairments of vision under conditions of high attention load. Methods - Right hemisphere stroke patients completed a task at fixation while attempting to discriminate letters appearing in the periphery. Attentional load of the central task was modulated by increasing task difficulty. Peripheral letters appeared simultaneously with the central task or at different times (stimulus onset asynchronies, SOAs) after it. In a second study healthy elderly volunteers were tested with a modified version of this paradigm. Results - Under conditions of high attention load right hemisphere stroke patients have a reduced effective visual field, over a significantly extended ‘attentional blink’, worse for items presented to their left. In the second study, older participants were unable to discriminate otherwise salient items across the visual field (left or right) when their attention capacity was loaded on the central task. This deficit extended temporally, with peripheral discrimination ability not returning to normal for up to 450 msec. Conclusions - Dynamically tying up attention resources on a task at fixation can have profound effects in patient populations and in normal ageing. These results demonstrate that items can escape conscious detection across space and time, and can thereby impact significantly on visual perception in these groups.The European Commission, Brunel University and the Wellcome Trust

Geomorphic floodplain mapping in small Mediterranean catchments using LiDAR data

Recent advances in remote sensing technologies along with the increased availability of topographic data have lately encouraged the development of automatic DEM (Digital Elevation Model)-based procedures for floodplain delineation. Geomorphic methods, establishing relationships between flood descriptors and morphologic catchment characteristics, appear particularly suitable to be implemented within a GIS algorithm. In the present work, four simplified geomorphic approaches based on “flow-depth scaling laws” (FD) or “flow-cross-sectional area scaling laws” (FA) with contributing area and two methods employing two different flood descriptors (Hydro-Geomorphic Method, HGM and Geomorphic Flood Index method, GFIM) have been applied for the preliminary evaluation of floodplain extent using high resolution DEMs (i.e. LiDAR at 1 and 2 m resolution) as the main input. Taking as a case study six of the largest basins located in southern Italy, the performances of these methods were evaluated and critically compared using government agency derived flood hazard maps as benchmarks. Results show that the adoption of FD especially when combined with morphology to formulate the GFIM, allows to efficiently predict the flood-prone areas with low computational costs. At the same time, performances of the flood mapping procedures based on “flow-area scaling laws”, although in principle more appealing, seem to be slightly lower. Overall, the proposed approaches can be applied for rough mapping of floodplains in ungauged basins or in data-scarce regions where standard flood hazard maps are unavailable

Inversion of electrical conductivity data with Tikhonov regularization approach: some considerations

Electromagnetic induction measurements, which are generally used to determine lateral variations of apparent electrical conductivity, can provide quantitative estimates of the subsurface conductivity at different depths. Quantitative inference about the Earth’s interior from experimental data is, however, an ill-posed problem. Using the generalised McNeill’s theory for the EM38 ground conductivity meter, we generated synthetic apparent conductivity curves (input data vector) simulating measurements at different heights above the soil surface. The electrical conductivity profile (the Earth model) was then estimated solving a least squares problem with Tikhonov regularization optimised with a projected conjugate gradient algorithm. Although the Tikhonov approach improves the conditioning of the resulting linear system, profile reconstruction can be surprisingly far from the desired true one. On the contrary, the projected conjugate gradient provided the best solution without any explicit regularization (a = 0) of the objective function of the least squares problem. Also, if the initial guess belongs to the image of the system matrix, Im(A), we found that it provides a unique solution in the same subspace Im(A)

Inversion of electrical conductivity data with Tikhonov regularization approach: some considerations

Electromagnetic induction measurements, which are generally used to determine lateral variations of apparent electrical conductivity, can provide quantitative estimates of the subsurface conductivity at different depths. Quantitative inference about the Earth's interior from experimental data is, however, an ill-posed problem. Using the generalised McNeill's theory for the EM38 ground conductivity meter, we generated synthetic apparent conductivity curves (input data vector) simulating measurements at different heights above the soil surface. The electrical conductivity profile (the Earth model) was then estimated solving a least squares problem with Tikhonov regularization optimised with a projected conjugate gradient algorithm. Although the Tikhonov approach improves the conditioning of the resulting linear system, profile reconstruction can be surprisingly far from the desired true one. On the contrary, the projected conjugate gradient provided the best solution without any explicit regularization ( a= 0) of the objective function of the least squares problem. Also, if the initial guess belongs to the image of the system matrix, Im(A), we found that it provides a unique solution in the same subspace Im(A)

Importance of plants with extremely small populations (Psesps) in endemic-rich areas, elements often forgotten in conservation strategies

The distribution of the threatened fern Ophioglossum vulgatum L., a plant with extremely small populations (PSESPs) in Sardinia, is characterized by small disjunct populations with only a few individuals, and little is known about its status in the wild. To provide information for the conservation of O. vulgatum and with the aim to develop an in situ conservation strategy, we investigated its distribution, population size, and habitat. Field surveys confirmed that the species grows in only five localities. Two representative populations were selected for this study (Funtanamela and Gedili), and in each population, all plants were mapped and monitored monthly from April to August over an 8-year period. During the study, the populations had a very low number of reproductive plants and the populations appeared to be in decline, with the total number of plants per population slightly decreased in Gedili while a sharp reduction was recorded in Funtanamela due to wild boar threat. A fence was built in order to protect the site from further damage, but no noticeable signals of recovery were observed. The most urgent conservation requirement for this species is to preserve the threatened habitat of the remnant populations. Further field surveys and research are also required for an improved understanding of the species’ status

Dietary Nitrate: Effects on the health of weaning pigs and Antimicrobial activity on seven probiotic Bifidobacterium spp. strains

The potential role of nitrite as an antimicrobial substance in the stomach may be of some importance in the ecology of the gastrointestinal tract and in host physiology. It has been shown that nitrite, under the acidic conditions of the stomach, may kill gut pathogens like Salmonella enteritidis, Escherichia coli, Salmonella typhimurium, and Yersinia enterocolitica, whereas acid alone has only a bacteriostatic effect. An in vivo study was conducted in order to assess the effects of dietary nitrate on microbiota and on the health of the gut (particularly in the stomach and small intestine). 96 weaning pigs were fed a diet containing high nitrate levels (15 mg and 150 mg) and then challenged with Salmonella enterica serovar typhimurium. Differences in composition of the gut microbiota were assessed by analysing samples from the pigs: To date analysis of 48 pigs has been completed.. Preliminary results demonstrated no effect on the population densities of microbial groups either from the challenge or from nitrate intake. However, increasing the time from challenge decreased either the counts of LAB in the stomach and jejunum or of clostridia in the stomach. Bifidobacteria also decreased in the stomach contents as nitrate supplementation increased. Supplementing the feedstuff with high dietary nitrate intake and then challenging with Salmonella did not affect the gastric pH or the degree of ulceration in the pigs. The synergistic bactericidal effects of pH, nitrite and thiocyanate on seven probiotic Bifidobacterium spp. strains were also investigated in an in vitro study. The results of the in vitro study demonstrated that an inhibitory effect exists on the seven probiotic bifidobacteria investigated with an exposure longer than 2 hours and pH values < 5.0. Addition of thiocyanate also increased the susceptibility of the tested strains. In this in vitro study, the most resistant strains at all conditions were B. animalis subsp. lactis Ra 18 and P32 and B. choerinum Su 877, Su 837 and Su 891

Comparative evaluation of image reconstruction methods for the siemens PET-MR scanner using the stir library

With the introduction of Positron Emission Tomography - Magnetic Resonance (PET-MR) scanners the development of new algorithms and the comparison of the performance of different iterative reconstruction algorithms and the characteristics of the reconstructed images data is relevant. In this work, we perform a quantitative assessment of the currently used ordered subset (OS) algorithms for low-counts PET-MR data taken from a Siemens Biograph mMR scanner using the Software for Tomographic Image Reconstruction (STIR, stir.sf.net). A comparison has been performed in terms of bias and coefficient of variation (CoV). Within the STIR library different algorithms are available, such as Order Subsets Expectation Maximization (OSEM), OS Maximum A Posteriori One Step Late (OSMAPOSL) with Quadratic Prior (QP) and with Median Root Prior (MRP), OS Separable Paraboloidal Surrogate (OSSPS) with QP and Filtered Back-Projection (FBP). In addition, List Mode (LM) reconstruction is available. Corrections for attenuation, scatter and random events are performed using STIR instead of using the scanner. Data from the Hoffman brain phantom are acquired, processed and reconstructed. Clinical data from the thorax of a patient have also been reconstructed with the same algorithms. The number of subsets does not appreciably affect the bias nor the coefficient of variation (CoV=11%) at a fixed sub-iteration number. The percentage relative bias and CoV maximum values for OSMAPOSL-MRP are 10% and 15% at 360 s acquisition and 12% and 15% for the 36 s, whilst for OSMAPOSL-QP they are 6% and 16% for 360 s acquisition and 11% and 23% at 36 s and for OSEM 6% and 11% for the 360 s acquisition and 10% and 15% for the 36 s. Our findings demonstrate that when it comes to low-counts, noise and bias become significant. The methodology for reconstructing Siemens mMR data with STIR is included in the CCP-PET-MR website