Javier Portela

Prevalence of primary open-angle glaucoma in a Spanish population: the Segovia study.

PurposeTo determine the prevalence of primary open-angle glaucoma (POAG) in Segovia, Spain. MethodsWe conducted a cross-sectional, population-based epidemiologic study, the target population of which was residents of Segovia, Spain, aged 40 to 79 years. A sample of 569 subjects was randomly selected in a stratified manner according to gender and age groups. All participants underwent a complete ophthalmic examination that included measurement of visual acuity and refraction, tonometry, anterior segment biomicroscopy, funduscopy, stereoscopic photographs of the optic nerve head, and automated white-on-white visual field testing. Two independent observers evaluated the optic nerve photographs and visual fields. The diagnosis of POAG was established when any eye had an open angle and a glaucomatous optic nerve and glaucomatous visual field. The prevalence of POAG in the population was estimated from the prevalence in the complete sample and the patients already diagnosed at the only glaucoma service in the city. ResultsThe estimated prevalences (99% confidence interval) in the population were, respectively, 2.1% (1.9–2.3%), and 1.7% (1.6–1.8%) for POAG and ocular hypertension. The prevalence of POAG increased with age (P < 0.005) and tended to be greater (P = 0.054) in men (2.4%) than women (1.7%). ConclusionThe prevalence of POAG in this Segovia population is 2.1%, similar to that estimated in previous studies performed in predominantly Caucasian populations.

Epidemiology of Refractive Errors in an Adult European Population: The Segovia Study

ABSTRACT Purpose: To determine the prevalence of refractive errors in Segovia, Spain. Methods: A cohort of 569 subjects was randomly selected in a stratified manner according to gender and age in a cross-sectional, population-based epidemiologic study, the target population of which was urban residents aged 40 to 79 years. All participants underwent an ophthalmic examination that included measurement of visual acuity (VA) and refraction, tonometry, anterior segment biomicroscopy, funduscopy, optic nerve head photography, and visual field testing. Of those, 417 subjects were enrolled who met the inclusion criteria of a phakic right eye and VA over 20/40. The prevalence of spherical errors was assessed after calculating the spherical equivalent and defining myopia as −0.5 diopters (D) or less and hyperopia as +0.50 D or more. The prevalence of astigmatism over 0.50 D was evaluated in minus cylinder form. Results: The estimated prevalences (95% confidence interval) of myopia, hyperopia, and astigmatism, in the population were 25.4% (21.5–29.8%) 43.6% (39–48.4%), and 53.5% (48.7–58.2%), respectively. No significant gender difference was found in the prevalence of any refractive errors. The prevalence of myopia or the mean value did not change significantly with age. The mean hyperopia and the mean astigmatism (p < 0.01 for both) and the prevalence increased with increasing age (p < 0.01 for both). Anisometropia of 1 D or more was present in 12.3% (49/396 subjects). Conclusion: More than 60% of the Segovia population over 40 years of age has a refractive error, with 25.4% myopic and 43.6% hyperopic. Astigmatism is present in over half of the population and the types change with age.

Extracting association patterns in network communications.

In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense.

Disclosing user relationships in email networks

To reveal patterns of communications of users in a network, an attacker may repeatedly obtain partial information on behavior and finally derive relationships between pairs of users through the modeling of this statistical information. This work is an enhancement of a previously presented statistical disclosure attack. The improvement of the attack is based on the use of the EM algorithm to improve the estimation of messages sent by users and to derive what pairs of users really communicate. Two methods are presented using the EM algorithm and the best method is used over real email data over 32 different network domains. Results are encouraging with high classification and positive predictive value rates.

Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks

Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.

Clustering Discrete Data Through the Multinomial Mixture Model

In this work, the multinomial mixture model is studied, through a maximum likelihood approach. The convergence of the maximum likelihood estimator to a set with characteristics of interest is shown. A method to select the number of mixture components is developed based on the form of the maximum likelihood estimator. A simulation study is then carried out to verify its behavior. Finally, two applications on real data of multinomial mixtures are presented.

Implementation of a robust bayesian method

In this work we study robustness in Bayesian models through a generalization of the Normal distribution. We show new appropriate techniques in order to deal with this distribution in Bayesian inference. Then we propose two approaches to decide, in some applications, if we should replace the usual Normal model by this generalization. First, we pose this dilemma as a model rejection problem, using diagnostic measures. In the second approach we evaluate the models predictive efficiency. We illustrate those perspectives with a simulation study, a non linear model and a longitudinal data model.

Privacy in Data Centers: A Survey of Attacks and Countermeasures

A Data Center collects, stores, and transmits huge dimensions of sensitive information of many types. Data Center security has become one of the highest network priorities as data thieves and crime cells look to infiltrate perimeter defenses through increasingly complex attack vectors with alarming success and devastating effects.

A Bayesian Test for the Mean of the Power Exponential Distribution

In this article, we deal with the problem of testing a point null hypothesis for the mean of a multivariate power exponential distribution. We study the conditions under which Bayesian and frequentist approaches can match. In this comparison it is observed that the tails of the model are the key to explain the reconciliability or irreconciliability between the two approaches.