Quantification of Retinal Neural Loss in Patients with Neuromyelitis Optica and Multiple Sclerosis with or without Optic Neuritis Using Fourier-Domain Optical Coherence Tomography

PURPOSE. We compared retinal nerve fiber layer (RNFL) and macular thickness measurements in patients with multiple sclerosis (MS) and neuromyelitis optica (NMO) with or without a history of optic neuritis, and in controls using Fourier-domain (FD) optical coherence tomography (OCT). METHODS. Patients with MS (n = 60), NMO (n = 33), longitudinal extensive transverse myelitis (LETM, n = 28) and healthy controls (n = 41) underwent ophthalmic examination, including automated perimetry, and FD-OCT RNFL and macular thickness measurements. Five groups of eyes were compared: MS with or without previous optic neuritis, NMO, LETM, and controls. Correlation between OCT and visual field (VF) findings was investigated. RESULTS. With regard to most parameters, RNFL and macular thickness measurements were significantly smaller in eyes of each group of patients compared to controls. MS eyes with optic neuritis did not differ significantly from MS eyes without optic neuritis, but measurements were smaller in NMO eyes than in all other groups. RNFL (but not macular thickness) measurements were significantly smaller in LETM eyes than in controls. While OCT abnormalities were correlated significantly with VF loss in NMO/LETM and MS, the correlation was much stronger in the former. CONCLUSIONS. Although FD-OCT RNFL and macular thickness measurements can reveal subclinical or optic neuritis-related abnormalities in NMO-spectrum and MS patients, abnormalities are predominant in the macula of MS patients and in RFNL measurements in NMO patients. The correlation between OCT and VF abnormalities was stronger in NMO than in MS, suggesting the two conditions differ regarding structural and functional damage. (ClinicalTrials.gov number, NCT01024985.) Invest Ophthalmol Vis Sci. 2012;53:3959-3966) DOI:10.1167/iovs.11-9324

Influence of Clinically Invisible, but Optical Coherence Tomography Detected, Optic Disc Margin Anatomy on Neuroretinal Rim Evaluation

PURPOSE. We previously demonstrated that most eyes have regionally variable extensions of Bruch's membrane (BM) inside the clinically identified disc margin (DM) that are clinically and photographically invisible. We studied the impact of these findings on DM- and BM opening (BMO)-derived neuroretinal rim parameters. METHODS. Disc stereo-photography and spectral domain optical coherence tomography (SD-OCT, 24 radial B-scans centered on the optic nerve head) were performed on 30 glaucoma patients and 10 age-matched controls. Photographs were colocalized to SD-OCT data such that the DM and BMO could be visualized in each B-scan. Three parameters were computed: (1) DM-horizontal rim width (HRW), the distance between the DM and internal limiting membrane (ILM) along the DM reference plane; (2) BMO-HRW, the distance between BMO and ILM along the BMO reference plane; and (3) BMO-minimum rim width (MRW), the minimum distance between BMO and ILM. Rank-order correlations of sectors ranked by rim width and spatial concordance measured as angular distances between equivalently ranked sectors were derived. RESULTS. The average DM position was external to BMO in all quadrants, except inferotemporally. There were significant sectoral differences among all three rim parameters. DM- HRW and BMO-HRW sector ranks were better correlated (median rho = 0.84) than DM- HRW and BMO-MRW (median rho = 0.55), or BMO-HRW and BMO-MRW (median rho = 0.60) ranks. Sectors with the narrowest BMO-MRW were infrequently the same as those with the narrowest DM-HRW or BMO-HRW. CONCLUSIONS. BMO-MRW quantifies the neuroretinal rim from a true anatomical outer border and accounts for its variable trajectory at the point of measurement. (Invest Ophthalmol Vis Sci. 2012;53:1852-1860) DOI:10.1167/iovs.11-9309

Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.

Effective number of accessed nodes in complex networks

The measurement called accessibility has been proposed as a means to quantify the efficiency of the communication between nodes in complex networks. This article reports results regarding the properties of accessibility, including its relationship with the average minimal time to visit all nodes reachable after h steps along a random walk starting from a source, as well as the number of nodes that are visited after a finite period of time. We characterize the relationship between accessibility and the average number of walks required in order to visit all reachable nodes (the exploration time), conjecture that the maximum accessibility implies the minimal exploration time, and confirm the relationship between the accessibility values and the number of nodes visited after a basic time unit. The latter relationship is investigated with respect to three types of dynamics: traditional random walks, self-avoiding random walks, and preferential random walks.

Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index

Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely "graphenes" and "complex networks", thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two - in-degree (i.e. citation counts) and age of publication - had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness. (C) 2012 Elsevier Ltd. All rights reserved.

Magnetic and optical investigation of 40SiO(2)center dot 30Na(2)O center dot 1Al(2)O(3)center dot(29-x) B2O3 center dot xFe(2)O(3) glass matrix

Samples of 40SiO(2)center dot 30Na(2)O center dot 1Al(2)O(3)center dot(29 - x)B2O3 center dot xFe(2)O(3) (mol%), with 0.0 <= x <= 17.5, were prepared by the fusion method and investigated by electron paramagnetic resonance (EPR), optical absorption (OA) and Mossbauer spectroscopy (MS). The EPR spectra of the as-synthesized samples exhibit two well-defined EPR signals around g = 4.27 and g = 2.01 and a visible EPR shoulder around g = 6.4, assigned to isolated Fe3+ ion complexes (g = 4.27 and g = 6.4) and Fe3+-based clusters (g = 2.01). Analyses of both EPR line intensity and line width support the model picture of Fe3+-based clusters built in from two sources of isolated ions, namely Fe2+ and Fe3+; the ferrous ion being used to build in iron-based clusters at lower x-content (below about x = 2.5%) whereas the ferric ion is used to build in iron-based clusters at higher x-content (above about x = 2.5%). The presence of Fe2+ ions incorporated within the glass template is supported by OA data with a strong band around 1100 nm due to the spin-allowed E-5(g)-T-5(2g) transition in an octahedral coordination with oxygen. Additionally, Mossbauer data (isomer shift and quadrupole splitting) confirm incorporation of both Fe2+ and Fe3+ ions within the template, more likely in tetrahedral-like environments. We hypothesize that ferrous ions are incorporated within the glass template as FeO4 complex resulting from replacing silicon in non-bridging oxygen (SiO3O-) sites whereas ferric ions are incorporated as FeO4 complex resulting from replacing silicon in bridging-like oxygen silicate groups (SiO4). (C) 2012 Elsevier Masson SAS. All rights reserved.

Optical chemical sensors using polythiophene derivatives as active layer for detection of volatile organic compounds

Several studies on polythiophene gas sensors, based mainly on electrochemical and gravimetric principles can be found in the literature. However, other principles of gas detection, such as optical and thermal, are still little studied. Optical sensing is suitable for remote detection and offers great versatility at low cost. Here,we report on the use of thin films of seven polythiophene derivatives as active layer in optical sensors for the detection of six volatile organic compounds (n-hexane, toluene, tetrahydrofuran, chloroform, dichloromethane and methanol) and water vapor, in concentration range of 500-30,000 ppm. The results showed that it is possible to use different polythiophene derivatives to differentiate VOCs by optical sensing. Differentiation can be performed based on the presence or not of response to an analyte and the sensitivity value of the sensors for the analytes. Another important feature is the lack of the effect of humidity on the response of most films, which could be a major drawback in the application of these sensors. (C) 2011 Elsevier B.V. All rights reserved.

Structure-function Relationships Using the Cirrus Spectral Domain Optical Coherence Tomograph and Standard Automated Perimetry

Purpose: To evaluate the relationship between glaucomatous structural damage assessed by the Cirrus Spectral Domain OCT (SDOCT) and functional loss as measured by standard automated perimetry (SAP). Methods: Four hundred twenty-two eyes (78 healthy, 210 suspects, 134 glaucomatous) of 250 patients were recruited from the longitudinal Diagnostic Innovations in Glaucoma Study and from the African Descent and Glaucoma Evaluation Study. All eyes underwent testing with the Cirrus SDOCT and SAP within a 6-month period. The relationship between parapapillary retinal nerve fiber layer thickness (RNFL) sectors and corresponding topographic SAP locations was evaluated using locally weighted scatterplot smoothing and regression analysis. SAP sensitivity values were evaluated using both linear as well as logarithmic scales. We also tested the fit of a model (Hood) for structure-function relationship in glaucoma. Results: Structure was significantly related to function for all but the nasal thickness sector. The relationship was strongest for superotemporal RNFL thickness and inferonasal sensitivity (R(2) = 0.314, P < 0.001). The Hood model fitted the data relatively well with 88% of the eyes inside the 95% confidence interval predicted by the model. Conclusions: RNFL thinning measured by the Cirrus SDOCT was associated with correspondent visual field loss in glaucoma.

On the optical properties of copper nanocubes as a function of the edge length as modeled by the discrete dipole approximation

This Letter reports an investigation on the optical properties of copper nanocubes as a function of size as modeled by the discrete dipole approximation. In the far-field, our results showed that the extinction resonances shifted from 595 to 670 nm as the size increased from 20 to 100 nm. Also, the highest optical efficiencies for absorption and scattering were obtained for nanocubes that were 60 and 100 nm in size, respectively. In the near-field, the electric-field amplitudes were investigated considering 514, 633 and 785 nm as the excitation wavelengths. The E-fields increased with size, being the highest at 633 nm. (c) 2012 Elsevier B.V. All rights reserved.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.

Predicting the connectivity of primate cortical networks from topological and spatial node properties

Abstract Background The organization of the connectivity between mammalian cortical areas has become a major subject of study, because of its important role in scaffolding the macroscopic aspects of animal behavior and intelligence. In this study we present a computational reconstruction approach to the problem of network organization, by considering the topological and spatial features of each area in the primate cerebral cortex as subsidy for the reconstruction of the global cortical network connectivity. Starting with all areas being disconnected, pairs of areas with similar sets of features are linked together, in an attempt to recover the original network structure. Results Inferring primate cortical connectivity from the properties of the nodes, remarkably good reconstructions of the global network organization could be obtained, with the topological features allowing slightly superior accuracy to the spatial ones. Analogous reconstruction attempts for the C. elegans neuronal network resulted in substantially poorer recovery, indicating that cortical area interconnections are relatively stronger related to the considered topological and spatial properties than neuronal projections in the nematode. Conclusion The close relationship between area-based features and global connectivity may hint on developmental rules and constraints for cortical networks. Particularly, differences between the predictions from topological and spatial properties, together with the poorer recovery resulting from spatial properties, indicate that the organization of cortical networks is not entirely determined by spatial constraints.

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Abstract Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Influence of nanoparticle size on the nonlinear optical properties of magnetite ferrofluids

The nonlinear index of refraction (n(2)) and the two-photon absorption coefficient (beta) of water-based ferrofluids made of magnetite nanocrystals of different sizes and with different coatings have been measured through the Z-scan technique, with ultrashort (femtoseconds) laser pulses. Their third-order susceptibility is calculated from the values of n(2) and beta. The influence of different particles' coatings and sizes on these nonlinear optical properties are investigated. The values of n(2) and beta depend more significantly on the nanoparticles' size than on the particular coating. We observe a decrease of beta as the nanoparticles' diameters decrease, although the optical gap is found to be the same for all samples. The results are interpreted considering modifications in the electronic orbital shape due to the particles' nanosize effect.