Comparison of Quantitative Imaging Devices and Subjective Optic Nerve Head Assessment by General Ophthalmologists to Differentiate Normal From Glaucomatous Eyes

Purpose: To compare the ability of Subjective assessment of optic nerve head (ONH) and retinal nerve fiber layer (RNFL) by general ophthalmologists and by a glaucoma expert with objective measurements by optical coherence tomography (Stratus OCT, Carl Zeiss Meditec Inc), confocal scanning laser ophthalmoscope (HRT III; Heidelberg Engineering, Heidelberg. Germany), and scanning laser polarimetry (GDx enhanced corneal compensation; Carl Zeiss Meditec Inc, Dublin, CA) in discriminating glaucomatous and normal eyes. Methods: Sixty-one glaucomatous and 57 normal eyes or 118 subjects Were included in the study. Three independent general ophthalmologists and I glaucoma expert evaluated ONH stereo-photographs. Receiver operating characteristic curves were constructed for each imaging technique and sensitivity at fixed specificity was estimated. Comparisons or areas under these curves (aROCs) and agreement (k) were determined between stereophoto grading and best parameter from each technique. Results: Best parameter from each technique showed larger aROC (Stratus OCT RNFL 0.92; Stratus OCT ONH vertical integrated area = 0.86; Stratus OCT macular thickness = 0.82; GDx enhanced corneal compensation = 0.91, HRT3 global cup-to-disc ratio = 0.83; HRT3 glaucoma probability score numeric area score 0.83) compared with stereophotograph grading by general ophthalmologists (0.80) in separating glaucomatous and normal eyes. Glaucoma expert stereophoto grading provided equal or larger aROC (0.92) than best parameter of each computerized imaging device. Stereophoto evaluated by a glaucoma expert showed better agreement with best parameter of each quantitative imaging technique in classifying eyes either as glaucomatous or normal compared with stereophoto grading by general ophthalmologists, The combination Of Subjective assessment of the optic disc by general ophthalmologists with RNFL objective parameters improved identification of glaucoma patients in a larger proportion than the combination of these objective parameters with Subjective assessment of the optic disc by a glaucoma expert (29.5% vs. 19.7%, respectively). Conclusions: Diagnostic ability of all imaging techniques showed better performance than subjective assessment of the ONH by general ophthalmologists, but not by It glaucoma expert, Objective RNFL measurements may provide improvement in glaucoma detection when combined with subjective assessment of the optic disc by general ophthalmologists or by a glaucoma expert.

Comparison of HRT-3 glaucoma probability score and subjective stereophotograph assessment for prediction of progression in glaucoma

PURPOSE. To assess whether baseline Glaucoma Probability Score (GPS; HRT-3; Heidelberg Engineering, Dossenheim, Germany) results are predictive of progression in patients with suspected glaucoma. The GPS is a new feature of the confocal scanning laser ophthalmoscope that generates an operator-independent, three-dimensional model of the optic nerve head and gives a score for the probability that this model is consistent with glaucomatous damage. METHODS. The study included 223 patients with suspected glaucoma during an average follow-up of 63.3 months. Included subjects had a suspect optic disc appearance and/or elevated intraocular pressure, but normal visual fields. Conversion was defined as development of either repeatable abnormal visual fields or glaucomatous deterioration in the appearance of the optic disc during the study period. The association between baseline GPS and conversion was investigated by Cox regression models. RESULTS. Fifty-four (24.2%) eyes converted. In multivariate models, both higher values of GPS global and subjective stereophotograph assessment ( larger cup-disc ratio and glaucomatous grading) were predictive of conversion: adjusted hazard ratios (95% CI): 1.31 (1.15 - 1.50) per 0.1 higher global GPS, 1.34 (1.12 - 1.62) per 0.1 higher CDR, and 2.34 (1.22 - 4.47) for abnormal grading, respectively. No significant differences ( P > 0.05 for all comparisons) were found between the c-index values ( equivalent to area under ROC curve) for the multivariate models (0.732, 0.705, and 0.699, respectively). CONCLUSIONS. GPS values were predictive of conversion in our population of patients with suspected glaucoma. Further, they performed as well as subjective assessment of the optic disc. These results suggest that GPS could potentially replace stereophotograph as a tool for estimating the likelihood of conversion to glaucoma.

Derivation of the probability distribution function for the local density of states of a disordered quantum wire via the replica trick and supersymmetry

We consider the statistical properties of the local density of states of a one-dimensional Dirac equation in the presence of various types of disorder with Gaussian white-noise distribution. It is shown how either the replica trick or supersymmetry can be used to calculate exactly all the moments of the local density of states.' Careful attention is paid to how the results change if the local density of states is averaged over atomic length scales. For both the replica trick and supersymmetry the problem is reduced to finding the ground state of a zero-dimensional Hamiltonian which is written solely in terms of a pair of coupled spins which are elements of u(1, 1). This ground state is explicitly found for the particular case of the Dirac equation corresponding to an infinite metallic quantum wire with a single conduction channel. The calculated moments of the local density of states agree with those found previously by Al'tshuler and Prigodin [Sov. Phys. JETP 68 (1989) 198] using a technique based on recursion relations for Feynman diagrams. (C) 2001 Elsevier Science B.V. All rights reserved.

Cavity QED analog of the harmonic-oscillator probability distribution function and quantum collapses

We establish a connection between the simple harmonic oscillator and a two-level atom interacting with resonant, quantized cavity and strong driving fields, which suggests an experiment to measure the harmonic-oscillator's probability distribution function. To achieve this, we calculate the Autler-Townes spectrum by coupling the system to a third level. We find that there are two different regions of the atomic dynamics depending on the ratio of the: Rabi frequency Omega (c) of the cavity field to that of the Rabi frequency Omega of the driving field. For Omega (c)

Laying date and laying sequence influence the sex ratio of crimson rosella broods

We examine the patterns of sex allocation in crimson rosellas Platycercus elegans, a socially monogamous Australian parrot. Overall, 41.8% of nestlings were male, a significant female bias. However underlying this population-level bias were non-random patterns of sex allocation within broods. Broods produced early in the season were female-biased, but the proportion of males in a brood increased as the breeding season progressed. Female rosellas may obtain greater fitness benefits from early-fledging daughters than sons because daughters can breed as 1-year-olds whereas sons do not breed until they are at least 2 years old. Laying date and laying sequence also interacted to influence the sex ratio of eggs. The sex of early-laid eggs strongly followed the brood level pattern, whereas the sex of middle- and late-laid eggs did not change significantly as the season progressed. Nevertheless, late-laid eggs were very unlikely to be male at the end of the season. We argue these differing seasonal patterns reflect the relative costs and benefits to producing early-hatched males and females at different times of the season. Female rosellas appear to maximise the probability that daughters are able to breed early but to minimise competitive asymmetries within the brood. In particular, late-hatched male chicks are disadvantaged if their oldest sibling is male, explaining the dearth of broods containing late-hatched males at the end of the breeding season.

Pediculus capitis infestation according to sex and social factors in Argentina

OBJECTIVE: To determine the intensity of Pediculus capitis infestation (abundance) among Argentinean schoolchildren. Children's sex and social stratum were analyzed as modifiers of the general prevalence and degree of parasitism. METHODS: The study included 1,370 schoolchildren (692 girls, 678 boys) from 26 schools of the province of La Rioja (21 public schools, five private schools). Classic prevalence was obtained as the percentage of children with nits and/or lice. Moreover, five degrees of parasitism were classified: 0) children with no signs of pediculosis; 0+) children with evidence of past infestation; 1) children with a recent infestation and low probability of active parasitism; 2) children with a recent infestation and high probability of active parasitism; 3) children with mobile lice (active pediculosis). RESULTS: The general prevalence was 61.4% (girls: 79%; boys: 44%, p<0.001). Private schools showed lower prevalence than public schools (p=0.02), especially due to the low prevalence in boys. Fifty percent of children were classified in classes 0 and 0+, 22% in class 1; and 28% in grades 2 and 3. The proportion of children in grade 3 was higher in public schools than in private schools. There were significant sexual differences in the intensity of parasitism for grades 2 and 3, where girls' rates exceeded twice those of boys'. CONCLUSIONS: Sex and social stratum are important modifiers of P. capitis general prevalence and degree of infestation. The classification of children by intensity of infestation allowed a more precise delimitation of this condition, which is especially important for disease surveillance and application of control measures.

Potential distribution and cost estimation of the damage caused by Cryptotermes brevis (Isoptera: Kalotermitidae) in the Azores

Copyright © 2014 Entomological Society of America.

Social capital, social participation and life satisfaction among Chilean older adults

OBJECTIVE To examine factors associated with social participation and their relationship with self-perceived well-being in older adults. METHODS This study was based on data obtained from the National Socioeconomic Characterization (CASEN) Survey conducted in Chile, in 2011, on a probability sample of households. We examined information of 31,428 older adults living in these households. Descriptive and explanatory analyses were performed using linear and multivariate logistic regression models. We assessed the respondents’ participation in different types of associations: egotropic, sociotropic, and religious. RESULTS Social participation increased with advancing age and then declined after the age of 80. The main finding of this study was that family social capital is a major determinant of social participation of older adults. Their involvement was associated with high levels of self-perceived subjective well-being. We identified four settings as sources of social participation: home-based; rural community-based; social policy programs; and religious. Older adults were significantly more likely to participate when other members of the household were also involved in social activities evidencing an intergenerational transmission of social participation. Rural communities, especially territorial associations, were the most favorable setting for participation. There has been a steady increase in the rates of involvement of older adults in social groups in Chile, especially after retirement. Religiosity remains a major determinant of associativism. The proportion of participation was higher among older women than men but these proportions equaled after the age of 80. CONCLUSIONS Self-perceived subjective well-being is not only dependent upon objective factors such as health and income, but is also dependent upon active participation in social life, measured as participation in associations, though its effects are moderate.

Contextual and individual indicators associated with the presence of teeth in adults

OBJECTIVE The objective of this study was to analyze whether socioeconomic conditions and the period of availability of fluoridated water are associated with the number of teeth present.METHODSThis cross-sectional study analyzed data from 1,720 adults between 20 and 59 years of age who resided in Florianópolis, SC, Southern Brazil, in 2009. The outcome investigated was the self-reported number of teeth present. The individual independent variables included gender, age range, skin color, number of years of schooling, and per capita household income. The duration of residence was used as a control variable. The contextual exposures included the period of availability of fluoridated water to the households and the socioeconomic variable for the census tracts, which was created from factor analysis of the tract’s mean income, education level, and percentage of households with treated water. Multilevel logistic regression was performed and inter-level interactions were tested.RESULTS Residents in intermediate and poorer areas and those with fluoridated water available for less time exhibited the presence of fewer teeth compared with those in better socioeconomic conditions and who had fluoridated water available for a longer period (OR = 1.02; 95%CI 1.01;1.02). There was an association between the period of availability of fluoridated water, per capita household income and number of years of education. The proportion of individuals in the poorer and less-educated stratum, which had fewer teeth present, was higher in regions where fluoridated water had been available for less time.CONCLUSIONS Poor socioeconomic conditions and a shorter period of availability of fluoridated water were associated with the probability of having fewer teeth in adulthood. Public policies aimed at reducing socioeconomic inequalities and increasing access to health services such as fluoridation of the water supply may help to reduce tooth loss in the future.

Fractional derivatives: probability interpretation and frequency response of rational approximations

The theory of fractional calculus (FC) is a useful mathematical tool in many applied sciences. Nevertheless, only in the last decades researchers were motivated for the adoption of the FC concepts. There are several reasons for this state of affairs, namely the co-existence of different definitions and interpretations, and the necessity of approximation methods for the real time calculation of fractional derivatives (FDs). In a first part, this paper introduces a probabilistic interpretation of the fractional derivative based on the Grünwald-Letnikov definition. In a second part, the calculation of fractional derivatives through Padé fraction approximations is analyzed. It is observed that the probabilistic interpretation and the frequency response of fraction approximations of FDs reveal a clear correlation between both concepts.

Reusability assessment of thermoset polymeric composite wastes as reinforcement and filler replacement for polymer concrete materials

The development and applications of thermoset polymeric composites, namely fiber reinforced polymers (FRP), have shifted in the last decades more and more into the mass market [1]. Production and consume have increased tremendously mainly for the construction, transportation and automobile sectors [2, 3]. Although the many successful uses of thermoset composite materials, recycling process of byproducts and end of lifecycle products constitutes a more difficult issue. The perceived lack of recyclability of composite materials is now increasingly important and seen as a key barrier to the development or even continued used of these materials in some markets.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Hierarchical Reinforcement Learning in Behavior and the Brain

Dissertation presented to obtain the Ph.D degree in Biology, Neuroscience

High performance fiber reinforced concrete for the shear reinforcement: experimental and numerical research

High performance fiber reinforced concrete (HPFRC) is developing rapidly to a modern structural material with unique rheological and mechanical characteristics. Despite applying several methodologies to achieve self15 compacting requirements, some doubts still remain regarding the most convenient strategy for developing a HPFRC. In the present study, an innovative mix design method is proposed for the development of high17 performance concrete reinforced with a relatively high dosage of steel fibers. The material properties of the developed concrete are assessed, and the concrete structural behavior is characterized under compressive, flexural and shear loading. This study better clarifies the significant contribution of fibers for shear resistance of concrete elements. This paper further discusses a FEM-based simulation, aiming to address the possibility of calibrating the constitutive model parameters related to fracture modes I and II.