Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals.

An empirical comparison of the performance of alternative option pricing models

Published as an article in: Investigaciones Economicas, 2005, vol. 29, issue 3, pages 483-523.

Pravastatin inhibits cell proliferation and increased MAT1A expression in hepatocarcinoma cells and in vivo models

Background: Statins may have therapeutic effects on hepatocarcinoma (HCC). This type of disorder is the most common malignant primary tumour in the liver. Our objective was to determine whether pravastatin had a therapeutic effect in vitro and in vivo models. Method: We design in vitro and in vivo model. In vitro we used PLC and determine cell proliferation. In vivo, we used and animal model to determined, PCNA and MAT1A expression and transaminases levels. Results: We found that pravastatin decreases cell proliferation in vitro (cell proliferation in pravastatin group was 82%, in sorafenib group 51% and in combined group 40%) and in vivo (in pravastatin group 80%, in sorafenib group 76.4% and in combined group 72.72%). The MAT1A levels, was significantly higher in Pravastatin group (D 62%, P 94%, S 71%, P + S 91%). The transaminases levels, decreased significantly in Pravastatin group (GOT and GPT levels D 619.5 U/L; 271 U/L) (P 117.5 U/L; 43.5 U/L) (S 147 U/L; 59 U/L) (P + S 142 U/L; 59 U/L). Conclusion: The combination of pravastatin + sorafenib were more effective than Sorafenib alone.

Algorithms for colour image processing based on neurological models

373 p. : il., gráf., fot., tablas

GFAP Isoforms in Adult Mouse Brain with a Focus on Neurogenic Astrocytes and Reactive Astrogliosis in Mouse Models of Alzheimer Disease

24 p.

Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance

[EN]The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper we deal with the problems of sampling and learning (estimating) such distributions when the metric on permutations is the Cayley distance. We propose new methods for both operations, whose performance is shown through several experiments. We also introduce novel procedures to count and randomly generate permutations at a given Cayley distance both with and without certain structural restrictions. An application in the field of biology is given to motivate the interest of this model.

Use of generalised additive models to categorise continuous variables in clinical prediction

13 P.

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

Unveiling the factors shaping the distribution of widely distributed alpine vertebrates, using multi-scale ecological niche modelling of the bat Plecotus macrobullaris

Several alpine vertebrates share a distribution pattern that extends across the South-western Palearctic but is limited to the main mountain massifs. Although they are usually regarded as cold-adapted species, the range of many alpine vertebrates also includes relatively warm areas, suggesting that factors beyond climatic conditions may be driving their distribution. In this work we first recognize the species belonging to the mentioned biogeographic group and, based on the environmental niche analysis of Plecotus macrobullaris, we identify and characterize the environmental factors constraining their ranges. Distribution overlap analysis of 504 European vertebrates was done using the Sorensen Similarity Index, and we identified four birds and one mammal that share the distribution with P. macrobullaris. We generated 135 environmental niche models including different variable combinations and regularization values for P. macrobullaris at two different scales and resolutions. After selecting the best models, we observed that topographic variables outperformed climatic predictors, and the abruptness of the landscape showed better predictive ability than elevation. The best explanatory climatic variable was mean summer temperature, which showed that P. macrobullaris is able to cope with mean temperature ranges spanning up to 16 degrees C. The models showed that the distribution of P. macrobullaris is mainly shaped by topographic factors that provide rock-abundant and open-space habitats rather than climatic determinants, and that the species is not a cold-adapted, but rather a cold-tolerant eurithermic organism. P. macrobullaris shares its distribution pattern as well as several ecological features with five other alpine vertebrates, suggesting that the conclusions obtained from this study might be extensible to them. We concluded that rock-dwelling and open-space foraging vertebrates with broad temperature tolerance are the best candidates to show wide alpine distribution in the Western Palearctic.

Apaf-1 Inhibitors Protect from Unwanted Cell Death in In Vivo Models of Kidney Ischemia and Chemotherapy Induced Ototoxicity

Background: Excessive apoptosis induces unwanted cell death and promotes pathological conditions. Drug discovery efforts aimed at decreasing apoptotic damage initially targeted the inhibition of effector caspases. Although such inhibitors were effective, safety problems led to slow pharmacological development. Therefore, apoptosis inhibition is still considered an unmet medical need. Methodology and Principal Findings: The interaction between Apaf-1 and the inhibitors was confirmed by NMR. Target specificity was evaluated in cellular models by siRNa based approaches. Cell recovery was confirmed by MTT, clonogenicity and flow cytometry assays. The efficiency of the compounds as antiapoptotic agents was tested in cellular and in vivo models of protection upon cisplatin induced ototoxicity in a zebrafish model and from hypoxia and reperfusion kidney damage in a rat model of hot ischemia. Conclusions: Apaf-1 inhibitors decreased Cytc release and apoptosome-mediated activation of procaspase-9 preventing cell and tissue damage in ex vivo experiments and in vivo animal models of apoptotic damage. Our results provide evidence that Apaf-1 pharmacological inhibition has therapeutic potential for the treatment of apoptosis-related diseases.

Dobutamine in Paediatric Population: A Systematic Review in Juvenile Animal Models

Objective: Although dobutamine is widely used in neonatal clinical practice, the evidence for its use in this specific population is not clear. We conducted a systematic review of the use of dobutamine in juvenile animals to determine whether the evidence from juvenile animal experiments with dobutamine supported the design of clinical trials in neonatal/ paediatric population. Methods: Studies were identified by searching MEDLINE (1946-2012) and EMBASE (1974-2012). Articles retrieved were independently reviewed by three authors and only those concerning efficacy and safety of the drug in juvenile animals were included. Only original articles published in English and Spanish were included. Results: Following our literature search, 265 articles were retrieved and 24 studies were included in the review: 17 focused on neonatal models and 7 on young animal models. Although the aims and design of these studies, as well as the doses and ages analysed, were quite heterogeneous, the majority of authors agree that dobutamine infusion improves cardiac output in a dose dependent manner. Moreover, the cardiovascular effects of dobutamine are influenced by postnatal age, as well as by the dose used and the duration of the therapy. There is inadequate information about the effects of dobutamine on cerebral perfusion to draw conclusions. Conclusion: There is enough preclinical evidence to ensure that dobutamine improves cardiac output, however to better understand its effects in peripheral organs, such as the brain, more specific and well designed studies are required to provide additional data to support the design of clinical trials in a paediatric population.

Robust Sliding Control of SEIR Epidemic Models

This paper is aimed at designing a robust vaccination strategy capable of eradicating an infectious disease from a population regardless of the potential uncertainty in the parameters defining the disease. For this purpose, a control theoretic approach based on a sliding-mode control law is used. Initially, the controller is designed assuming certain knowledge of an upper-bound of the uncertainty signal. Afterwards, this condition is removed while an adaptive sliding control system is designed. The closed-loop properties are proved mathematically in the nonadaptive and adaptive cases. Furthermore, the usual sign function appearing in the sliding-mode control is substituted by the saturation function in order to prevent chattering. In addition, the properties achieved by the closed-loop system under this variation are also stated and proved analytically. The closed-loop system is able to attain the control objective regardless of the parametric uncertainties of the model and the lack of a priori knowledge on the system.

Digital Quantum Simulation of Spin Models with Circuit Quantum Electrodynamics

Systems of interacting quantum spins show a rich spectrum of quantum phases and display interesting many-body dynamics. Computing characteristics of even small systems on conventional computers poses significant challenges. A quantum simulator has the potential to outperform standard computers in calculating the evolution of complex quantum systems. Here, we perform a digital quantum simulation of the paradigmatic Heisenberg and Ising interacting spin models using a two transmon-qubit circuit quantum electrodynamics setup. We make use of the exchange interaction naturally present in the simulator to construct a digital decomposition of the model-specific evolution and extract its full dynamics. This approach is universal and efficient, employing only resources that are polynomial in the number of spins, and indicates a path towards the controlled simulation of general spin dynamics in superconducting qubit platforms.

Stability analysis and observer design for discrete-time SEIR epidemic models

This paper applies Micken's discretization method to obtain a discrete-time SEIR epidemic model. The positivity of the model along with the existence and stability of equilibrium points is discussed for the discrete-time case. Afterwards, the design of a state observer for this discrete-time SEIR epidemic model is tackled. The analysis of the model along with the observer design is faced in an implicit way instead of obtaining first an explicit formulation of the system which is the novelty of the presented approach. Moreover, some sufficient conditions to ensure the asymptotic stability of the observer are provided in terms of a matrix inequality that can be cast in the form of a LMI. The feasibility of the matrix inequality is proved, while some simulation examples show the operation and usefulness of the observer.

Cosmological constrains on fast transition Unified Dark Matter models

In this work we show the results obtained applying a Unified Dark Matter (UDM) model with a fast transition to a set of cosmological data. Two different functions to model the transition are tested, and the feasibility of both models is explored using CMB shift data from Planck [1], Galaxy Clustering data from [2] and [3], and Union2.1 SNe Ia [4]. These new models are also statistically compared with the ACDM and quiessence models using Bayes factor through evidence. Bayesian inference does not discard the UDM models in favor of ACDM.