Markov chain montecarlo method applied to rounding zeros of compositional data: first approach

As stated in Aitchison (1986), a proper study of relative variation in a compositional data set should be based on logratios, and dealing with logratios excludes dealing with zeros. Nevertheless, it is clear that zero observations might be present in real data sets, either because the corresponding part is completely absent –essential zeros– or because it is below detection limit –rounded zeros. Because the second kind of zeros is usually understood as “a trace too small to measure”, it seems reasonable to replace them by a suitable small value, and this has been the traditional approach. As stated, e.g. by Tauber (1999) and by Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000), the principal problem in compositional data analysis is related to rounded zeros. One should be careful to use a replacement strategy that does not seriously distort the general structure of the data. In particular, the covariance structure of the involved parts –and thus the metric properties– should be preserved, as otherwise further analysis on subpopulations could be misleading. Following this point of view, a non-parametric imputation method is introduced in Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000). This method is analyzed in depth by Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2003) where it is shown that the theoretical drawbacks of the additive zero replacement method proposed in Aitchison (1986) can be overcome using a new multiplicative approach on the non-zero parts of a composition. The new approach has reasonable properties from a compositional point of view. In particular, it is “natural” in the sense that it recovers the “true” composition if replacement values are identical to the missing values, and it is coherent with the basic operations on the simplex. This coherence implies that the covariance structure of subcompositions with no zeros is preserved. As a generalization of the multiplicative replacement, in the same paper a substitution method for missing values on compositional data sets is introduced

Identification of plasmodium vivax proteins with potential role in invasion using sequence redundancy reduction and profile hidden markov models

Background: This study describes a bioinformatics approach designed to identify Plasmodium vivax proteins potentially involved in reticulocyte invasion. Specifically, different protein training sets were built and tuned based on different biological parameters, such as experimental evidence of secretion and/or involvement in invasion-related processes. A profile-based sequence method supported by hidden Markov models (HMMs) was then used to build classifiers to search for biologically-related proteins. The transcriptional profile of the P. vivax intra-erythrocyte developmental cycle was then screened using these classifiers. Results: A bioinformatics methodology for identifying potentially secreted P. vivax proteins was designed using sequence redundancy reduction and probabilistic profiles. This methodology led to identifying a set of 45 proteins that are potentially secreted during the P. vivax intra-erythrocyte development cycle and could be involved in cell invasion. Thirteen of the 45 proteins have already been described as vaccine candidates; there is experimental evidence of protein expression for 7 of the 32 remaining ones, while no previous studies of expression, function or immunology have been carried out for the additional 25. Conclusions: The results support the idea that probabilistic techniques like profile HMMs improve similarity searches. Also, different adjustments such as sequence redundancy reduction using Pisces or Cd-Hit allowed data clustering based on rational reproducible measurements. This kind of approach for selecting proteins with specific functions is highly important for supporting large-scale analyses that could aid in the identification of genes encoding potential new target antigens for vaccine development and drug design. The present study has led to targeting 32 proteins for further testing regarding their ability to induce protective immune responses against P. vivax malaria.

Un modelo matemático para el aprendizaje de la conservación del número : cadenas de Markov.

Comprobar que el aprendizaje sigue el proceso descrito por Markov. Muestra tomada al azar entre los alumnos del Centro de Educación Especial 'Santísimo Cristo de la Misericordia' de Espinardo (Murcia). Está formada por dos grupos de edades comprendidas entre los 6 y 14 años y con características intelectuales distintas. Grupo I: 20 sujetos que se encuentran en el estadio de no conservación. Grupo II: 10 sujetos que se encuentran en estadio intermedio. La investigación se llevó a cabo en el grupo I siguiendo los pasos siguientes: A/ Diagnóstico operatorio para situar a los sujetos en el nivel de no conservación, aunque en distintos subniveles; B/ Aprendizaje operatorio mediante ejercicios con materiales discretos y aprendizaje con materiales continuos a través de 5 situaciones. A lo largo de este aprendizaje se utilizó el test-postest para establecer la adquisición de cada paso y los ejercicios concretos para alcanzar el siguiente. El grupo II sirvió para construir el vector de probabilidad I en la matriz inicial. Material operatorio. El estudio se sitúa en el modelo cognitivo, desde el punto de vista de la Psicología Genética y describe una situación experimental utilizando para el análisis de los datos matrices de transición 3 x 3 con los vectores de probabilidad y vectores de probabilidad obtenidos directamente del proceso de aprendizaje. Las diferencias más altas encontradas entre los vectores de probabilidad de los aprendizajes y los vectores de probabilidad de la matriz de transición, fueron menores del 3. En este estudio se han encontrado hasta 11 conductas en la adquisición de la conservación de la cantidad. Se ha comprobado con esta investigación que el aprendizaje de la conservación del número sigue un modelo markoviano, y se termina exponiendo que es posible establecer una matriz de transición 11 x 11 con estimadores de máxima probabilidad para los parámetros con el fin de poder establecer si los 11 niveles son genéticamente diferentes por donde han de pasar los individuos en la adquisición del concepto de número, o son conductas distintas del mismo nivel genético.

Uso de modelos de Markov para recomendaci??n de actividades en Hipermedia Adaptativa

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The use of pulse amplitude modulated fluorescence techniques for metal toxicity assessment in fluvial biofilms

Metal pollution in rivers is in great concern with human activities in the fluvial watershed. This thesis aims to investigate the potential use of chl-a fluorescence parameters as biomarkers of metal toxicity, and to find cause-effect relationships between metal exposures, other environmental factor (i.e. light), and functional and structural biofilm responses. This thesis demonstrates that the use of chl-a fluorescence parameters allows detect early effects on biofilms caused by zinc toxicity, both in the laboratory as in polluted rivers. In microcosm experiments, the use of chl-a fluorescence parameters allows evaluates structural changes on photosynthetic apparatus and in algal groups’ composition of biofilms long-term exposed to zinc. In order to evaluate the effects of chronic metal pollution in rivers, it is recommended the use of biofilm translocation experiments and the use of a multi-biomarker approach.

Life cycle of the QBO-modulated 11-year solar cycle signals in the Northern Hemispheric winter

This paper provides some insights on the quasi-biennial oscillation (QBO) modulated 11-year solar cycle (11-yr SC) signals in Northern Hemisphere (NH) winter temperature and zonal wind. Daily ERA-40 Reanalysis and ECMWF Operational data for the period of 1958-2006 were used to examine the seasonal evolution of the QBO-solar cycle relationship at various pressure levels up to the stratopause. The results show that the solar signals in the NH winter extratropics are indeed QBO-phase dependent, moving poleward and downward as winter progresses with a faster descent rate under westerly QBO than under easterly QBO. In the stratosphere, the signals are highly significant in late January to early March and have a life span of 30-50 days. Under westerly QBO, the stratospheric solar signals clearly lead and connect to those in the troposphere in late March and early April where they have a life span of 10 days. As the structure changes considerably from the upper stratosphere to the lower troposphere, the exact month when the maximum solar signals occur depends largely on the altitude chosen. For the low-latitude stratosphere, our analysis supports a vertical double-peaked structure of positive signature of the 11-yr SC in temperature, and demonstrates that this structure is further modulated by the QBO. These solar signals have a longer life span (3-4 months) in comparison to those in the extratropics. The solar signals in the lower stratosphere are stronger in early winter but weaker in late winter, while the reverse holds in the upper stratosphere.

Modelling heterotachy in phylogenetic inference by reversible-jump Markov chain Monte Carlo

The rate at which a given site in a gene sequence alignment evolves over time may vary. This phenomenon-known as heterotachy-can bias or distort phylogenetic trees inferred from models of sequence evolution that assume rates of evolution are constant. Here, we describe a phylogenetic mixture model designed to accommodate heterotachy. The method sums the likelihood of the data at each site over more than one set of branch lengths on the same tree topology. A branch-length set that is best for one site may differ from the branch-length set that is best for some other site, thereby allowing different sites to have different rates of change throughout the tree. Because rate variation may not be present in all branches, we use a reversible-jump Markov chain Monte Carlo algorithm to identify those branches in which reliable amounts of heterotachy occur. We implement the method in combination with our 'pattern-heterogeneity' mixture model, applying it to simulated data and five published datasets. We find that complex evolutionary signals of heterotachy are routinely present over and above variation in the rate or pattern of evolution across sites, that the reversible-jump method requires far fewer parameters than conventional mixture models to describe it, and serves to identify the regions of the tree in which heterotachy is most pronounced. The reversible-jump procedure also removes the need for a posteriori tests of 'significance' such as the Akaike or Bayesian information criterion tests, or Bayes factors. Heterotachy has important consequences for the correct reconstruction of phylogenies as well as for tests of hypotheses that rely on accurate branch-length information. These include molecular clocks, analyses of tempo and mode of evolution, comparative studies and ancestral state reconstruction. The model is available from the authors' website, and can be used for the analysis of both nucleotide and morphological data.

Inference on microsatellite mutation processes in the invasive mite, Varroa destructor, using reversible jump Markov chain Monte Carlo

Varroa destructor is a parasitic mite of the Eastern honeybee Apis cerana. Fifty years ago, two distinct evolutionary lineages (Korean and Japanese) invaded the Western honeybee Apis mellifera. This haplo-diploid parasite species reproduces mainly through brother sister matings, a system which largely favors the fixation of new mutations. In a worldwide sample of 225 individuals from 21 locations collected on Western honeybees and analyzed at 19 microsatellite loci, a series of de novo mutations was observed. Using historical data concerning the invasion, this original biological system has been exploited to compare three mutation models with allele size constraints for microsatellite markers: stepwise (SMM) and generalized (GSM) mutation models, and a model with mutation rate increasing exponentially with microsatellite length (ESM). Posterior probabilities of the three models have been estimated for each locus individually using reversible jump Markov Chain Monte Carlo. The relative support of each model varies widely among loci, but the GSM is the only model that always receives at least 9% support, whatever the locus. The analysis also provides robust estimates of mutation parameters for each locus and of the divergence time of the two invasive lineages (67,000 generations with a 90% credibility interval of 35,000-174,000). With an average of 10 generations per year, this divergence time fits with the last post-glacial Korea Japan land separation. (c) 2005 Elsevier Inc. All rights reserved.

Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo

We describe a Bayesian method for investigating correlated evolution of discrete binary traits on phylogenetic trees. The method fits a continuous-time Markov model to a pair of traits, seeking the best fitting models that describe their joint evolution on a phylogeny. We employ the methodology of reversible-jump ( RJ) Markov chain Monte Carlo to search among the large number of possible models, some of which conform to independent evolution of the two traits, others to correlated evolution. The RJ Markov chain visits these models in proportion to their posterior probabilities, thereby directly estimating the support for the hypothesis of correlated evolution. In addition, the RJ Markov chain simultaneously estimates the posterior distributions of the rate parameters of the model of trait evolution. These posterior distributions can be used to test among alternative evolutionary scenarios to explain the observed data. All results are integrated over a sample of phylogenetic trees to account for phylogenetic uncertainty. We implement the method in a program called RJ Discrete and illustrate it by analyzing the question of whether mating system and advertisement of estrus by females have coevolved in the Old World monkeys and great apes.

Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo

This article presents a statistical method for detecting recombination in DNA sequence alignments, which is based on combining two probabilistic graphical models: (1) a taxon graph (phylogenetic tree) representing the relationship between the taxa, and (2) a site graph (hidden Markov model) representing interactions between different sites in the DNA sequence alignments. We adopt a Bayesian approach and sample the parameters of the model from the posterior distribution with Markov chain Monte Carlo, using a Metropolis-Hastings and Gibbs-within-Gibbs scheme. The proposed method is tested on various synthetic and real-world DNA sequence alignments, and we compare its performance with the established detection methods RECPARS, PLATO, and TOPAL, as well as with two alternative parameter estimation schemes.

Exploring the applications of a pulsating jet hydrodynamic modulated voltammetric (HMV) system - electrochemistry of nanostructured Pt electrodes and trace analysis

The electrochemistry of nanostructured electrodes is investigated using hydrodynamic modulated voltammetry (HMV). Here a liquid crystal templating process is used to produce a platinum modified electrode with a relatively high surface area (Roughness factor, Rf = 42.4). The electroreduction of molecular oxygen at a nanostructured platinum surface is used to demonstrate the ability of HMV to discriminate between Faradaic and non-Faradaic electrode reactions. The HMV approach shows that the reduction of molecular oxygen shows considerable hysteresis correlating with the formation and stripping of oxide species at the platinum surface. Without the HMV analysis it is difficult to discern the same detail under the conditions employed. In addition the detection limit of the apparatus is explored and shown, under ideal conditions, to be of the order of 45 nmol dm(-3) employing [Fe(CN)(6)](4-) as a test species. (C) 2009 Elsevier B.V. All rights reserved.

Estimation of Hidden Markov Models parameters using differential evolution

Hidden Markov Models (HMMs) have been successfully applied to different modelling and classification problems from different areas over the recent years. An important step in using HMMs is the initialisation of the parameters of the model as the subsequent learning of HMM’s parameters will be dependent on these values. This initialisation should take into account the knowledge about the addressed problem and also optimisation techniques to estimate the best initial parameters given a cost function, and consequently, to estimate the best log-likelihood. This paper proposes the initialisation of Hidden Markov Models parameters using the optimisation algorithm Differential Evolution with the aim to obtain the best log-likelihood.

Enhancing change detection in low-quality surveillance footage using markov random fields

Urban surveillance footage can be of poor quality, partly due to the low quality of the camera and partly due to harsh lighting and heavily reflective scenes. For some computer surveillance tasks very simple change detection is adequate, but sometimes a more detailed change detection mask is desirable, eg, for accurately tracking identity when faced with multiple interacting individuals and in pose-based behaviour recognition. We present a novel technique for enhancing a low-quality change detection into a better segmentation using an image combing estimator in an MRF based model.