No abstract

All the primary indices proposed in this work exhibit very good performance in discriminating between normal and irregular corneas. The accuracy of the combined indices is optimal within the test group (perfect classification), allowing their use in clinical practice as corneal markers of a disease. All these indices are fast to compute and can be easily implemented in any corneal topography system.

The aim of this study was to evaluate the long-term efficacy and safety of the Artiflex® lens implant and to follow the evolution of the number of corneal endothelial cells over time. Design It was a retrospective study of an observational case series of patients who underwent surgery at "The INVISION Ophthalmic Hospital" (Almería, Spain) in 2007 and who were followed for 10 years. Methods Setting: Clinical practice. Study population included 53 eyes of 30 patients who underwent an Artiflex® lens implant for the correction of myopia from −4 to −14 D. Each patient included in this study had stable myopia for at least 2 years and a contraindication for corneal refractive surgery. The efficacy index was defined as the quotient between uncorrected distance visual acuity postoperative and best-corrected distance visual acuity (BCDVA) preoperative. The safety index was calculated as the quotient between BCDVA postop and BCDVA preop. Results The average efficacy and safety indices of the lenses implanted were 1.1 (SD 0.30) and 1.06 (SD 0.2) at 10 years of follow-up. In this period of time there has been a loss of 12% of the corneal endothelial cells. The postoperative complications were pigment dispersion in four eyes (7%) of four patients and decentration of phakic intraocular lens in two eyes (4%) of another two patients. Conclusions The Artiflex® foldable phakic lens could be a safe and effective long-term alternative for myopic patients in whom laser surgery was contraindicated.

The aim of this paper is to assess and highlight the significance of cultural landscapes in protected areas, considering both biodiversity and the delivery of provisioning ecosystem services. In order to do that, we analyzed 26 protected areas in Andalusia (Spain), all of them Natural or National Parks, regarding some of their ecosystem services (agriculture, livestock grazing, microclimate regulation, environmental education and tourism) and diversity of the four terrestrial vertebrate classes: amphibians, reptiles, mammals, and birds. A cluster analysis was also run in order to group the 26 protected areas according to their dominant landscape. The results show that protected areas dominated by dehesa (a heterogeneous system containing different states of ecological maturity), or having strong presence of olive groves, present a larger area of delivery of provisioning ecosystem services, on average. These cultural landscapes play an essential role not only for biodiversity conservation but also as providers of provisioning ecosystem services.

In many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons. First, it requires continuous model updating and the ability to handle a posterior distribution conditioned on an unbounded data set. Secondly, the underlying data distribution may drift from one time step to another, and the classic i.i.d. (independent and identically distributed), or data exchangeability assumption does not hold anymore. In this paper, we present an approximate Bayesian inference approach using variational methods that addresses these issues for conjugate exponential family models with latent variables. Our proposal makes use of a novel scheme based on hierarchical priors to explicitly model temporal changes of the model parameters. We show how this approach induces an exponential forgetting mechanism with adaptive forgetting rates. The method is able to capture the smoothness of the concept drift, ranging from no drift to abrupt drift. The proposed variational inference scheme maintains the computational efficiency of variational methods over conjugate models, which is critical in streaming settings. The approach is validated on four different domains (energy, finance, geolocation, and text) using four real-world data sets.

Direct analysis of the digitized images of the Placido mires projected on the cornea is a valid and effective tool for detection of corneal irregularities. Although based only on the data from the anterior surface of the cornea, the new indices performed well even when applied to the KC suspect eyes. They have the advantage of simplicity of calculation combined with high sensitivity in corneal irregularity detection and thus can be used as supplementary criteria for diagnosing and grading KC that can be added to the current keratometric classifications.

The AMIDST Toolbox is a software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data sets using parallel or distributed implementations of Bayesian learning algorithms for either streaming or batch data. These algorithms are based on a flexible variational message passing scheme, which supports discrete and continuous variables from a wide range of probability distributions. AMIDST also leverages existing functionality and algorithms by interfacing to software tools such as Flink, Spark, MOA, Weka, R and HUGIN. AMIDST is an open source toolbox written in Java and available at http://www.amidsttoolbox.com under the Apache Software License version 2.0.

The methodology can be used for the real-time reconstruction of both altimetric data and corneal power maps from the data collected by keratoscopes, such as the Placido ring-based topographers, that will be decisive in early detection of corneal diseases such as keratoconus.