A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Mayaro Virus Infection in Amazonia: A Multimodel Inference Approach to Risk Factor Assessment

Background: Arboviral diseases are major global public health threats. Yet, our understanding of infection risk factors is, with a few exceptions, considerably limited. A crucial shortcoming is the widespread use of analytical methods generally not suited for observational data - particularly null hypothesis-testing (NHT) and step-wise regression (SWR). Using Mayaro virus (MAYV) as a case study, here we compare information theory-based multimodel inference (MMI) with conventional analyses for arboviral infection risk factor assessment. Methodology/Principal Findings: A cross-sectional survey of anti-MAYV antibodies revealed 44% prevalence (n = 270 subjects) in a central Amazon rural settlement. NHT suggested that residents of village-like household clusters and those using closed toilet/latrines were at higher risk, while living in non-village-like areas, using bednets, and owning fowl, pigs or dogs were protective. The "minimum adequate" SWR model retained only residence area and bednet use. Using MMI, we identified relevant covariates, quantified their relative importance, and estimated effect-sizes (beta +/- SE) on which to base inference. Residence area (beta(Village) = 2.93 +/- 0.41; beta(Upland) = -0.56 +/- 0.33, beta(Riverbanks) = -2.37 +/- 0.55) and bednet use (beta = -0.95 +/- 0.28) were the most important factors, followed by crop-plot ownership (beta = 0.39 +/- 0.22) and regular use of a closed toilet/latrine (beta = 0.19 +/- 0.13); domestic animals had insignificant protective effects and were relatively unimportant. The SWR model ranked fifth among the 128 models in the final MMI set. Conclusions/Significance: Our analyses illustrate how MMI can enhance inference on infection risk factors when compared with NHT or SWR. MMI indicates that forest crop-plot workers are likely exposed to typical MAYV cycles maintained by diurnal, forest dwelling vectors; however, MAYV might also be circulating in nocturnal, domestic-peridomestic cycles in village-like areas. This suggests either a vector shift (synanthropic mosquitoes vectoring MAYV) or a habitat/habits shift (classical MAYV vectors adapting to densely populated landscapes and nocturnal biting); any such ecological/adaptive novelty could increase the likelihood of MAYV emergence in Amazonia.

Bioelectrical impedance with different equations versus deuterium oxide dilution method for the inference of body composition in healthy older persons

There is no consensus regarding the accuracy of bioimpedance for the determination of body composition in older persons. This study aimed to compare the assessment of lean body mass of healthy older volunteers obtained by the deuterium dilution method (reference) with those obtained by two frequently used bioelectrical impedance formulas and one formula specifically developed for a Latin-American population. A cross-sectional study. Twenty one volunteers were studied, 12 women, with mean age 72 +/- 6.7 years. Urban community, Ribeiro Preto, Brazil. Fat free mass was determined, simultaneously, by the deuterium dilution method and bioelectrical impedance; results were compared. In bioelectrical impedance, body composition was calculated by the formulas of Deuremberg, Lukaski and Bolonchuck and Valencia et al. Lean body mass of the studied volunteers, as determined by bioelectrical impedance was 37.8 +/- 9.2 kg by the application of the Lukaski e Bolonchuk formula, 37.4 +/- 9.3 kg (Deuremberg) and 43.2 +/- 8.9 kg (Valencia et. al.). The results were significantly correlated to those obtained by the deuterium dilution method (41.6 +/- 9.3 Kg), with r=0.963, 0.932 and 0.971, respectively. Lean body mass obtained by the Valencia formula was the most accurate. In this study, lean body mass of older persons obtained by the bioelectrical impedance method showed good correlation with the values obtained by the deuterium dilution method. The formula of Valencia et al., developed for a Latin-American population, showed the best accuracy.

Likelihood-based inference for power distributions

This paper considers likelihood-based inference for the family of power distributions. Widely applicable results are presented which can be used to conduct inference for all three parameters of the general location-scale extension of the family. More specific results are given for the special case of the power normal model. The analysis of a large data set, formed from density measurements for a certain type of pollen, illustrates the application of the family and the results for likelihood-based inference. Throughout, comparisons are made with analogous results for the direct parametrisation of the skew-normal distribution.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

Indagini in Deep Inference

La tesi è uno studio di alcuni aspetti della nuova metodologia “deep inference”, abbinato ad una rivisitazione dei concetti classici di proof theory, con l'aggiunta di alcuni risultati originali orientati ad una maggior comprensione dell'argomento, nonché alle applicazioni pratiche. Nel primo capitolo vengono introdotti, seguendo un approccio di stampo formalista (con alcuni spunti personali), i concetti base della teoria della dimostrazione strutturale – cioè quella che usa strumenti combinatoriali (o “finitistici”) per studiare le proprietà delle dimostrazioni. Il secondo capitolo focalizza l'attenzione sulla logica classica proposizionale, prima introducendo il calcolo dei sequenti e dimostrando il Gentzen Hauptsatz, per passare poi al calcolo delle strutture (sistema SKS), dimostrando anche per esso un teorema di eliminazione del taglio, appositamente adattato dall'autore. Infine si discute e dimostra la proprietà di località per il sistema SKS. Un percorso analogo viene tracciato dal terzo ed ultimo capitolo, per quanto riguarda la logica lineare. Viene definito e motivato il calcolo dei sequenti lineari, e si discute del suo corrispettivo nel calcolo delle strutture. L'attenzione qui è rivolta maggiormente al problema di definire operatori non-commutativi, che mettono i sistemi in forte relazione con le algebre di processo.

Inference on copula-based correlation structures

We propose an extension of the approach provided by Kluppelberg and Kuhn (2009) for inference on second-order structure moments. As in Kluppelberg and Kuhn (2009) we adopt a copula-based approach instead of assuming normal distribution for the variables, thus relaxing the equality in distribution assumption. A new copula-based estimator for structure moments is investigated. The methodology provided by Kluppelberg and Kuhn (2009) is also extended considering the copulas associated with the family of Eyraud-Farlie-Gumbel-Morgenstern distribution functions (Kotz, Balakrishnan, and Johnson, 2000, Equation 44.73). Finally, a comprehensive simulation study and an application to real financial data are performed in order to compare the different approaches.

Partial reconstruction of the trajectories of a discretely observed branching diffusion with immigration and an application to inference

In this treatise we consider finite systems of branching particles where the particles move independently of each other according to d-dimensional diffusions. Particles are killed at a position dependent rate, leaving at their death position a random number of descendants according to a position dependent reproduction law. In addition particles immigrate at constant rate (one immigrant per immigration time). A process with above properties is called a branching diffusion withimmigration (BDI). In the first part we present the model in detail and discuss the properties of the BDI under our basic assumptions. In the second part we consider the problem of reconstruction of the trajectory of a BDI from discrete observations. We observe positions of the particles at discrete times; in particular we assume that we have no information about the pedigree of the particles. A natural question arises if we want to apply statistical procedures on the discrete observations: How can we find couples of particle positions which belong to the same particle? We give an easy to implement 'reconstruction scheme' which allows us to redraw or 'reconstruct' parts of the trajectory of the BDI with high accuracy. Moreover asymptotically the whole path can be reconstructed. Further we present simulations which show that our partial reconstruction rule is tractable in practice. In the third part we study how the partial reconstruction rule fits into statistical applications. As an extensive example we present a nonparametric estimator for the diffusion coefficient of a BDI where the particles move according to one-dimensional diffusions. This estimator is based on the Nadaraya-Watson estimator for the diffusion coefficient of one-dimensional diffusions and it uses the partial reconstruction rule developed in the second part above. We are able to prove a rate of convergence of this estimator and finally we present simulations which show that the estimator works well even if we leave our set of assumptions.

Characterization of thermoelectric commercial modules using a module test system

Il presente lavoro di tesi è stato svolto presso la DTU, Technical University of Denmark, nel Department of Energy Conversion and Storage, Riso Campus. Lo scopo del periodo di soggiorno estero è stato quello di caratterizzare appropriati moduli termoelettrici forniti da aziende del settore, utilizzando un opportuno apparato di caratterizzazione. Quest’ultimo è noto come “module test system” e, nello specifico, è stato fornito dalla PANCO GmbH, azienda anch’essa attiva nel campo delle tecnologie termoelettriche. Partendo da uno studio teorico dei fenomeni fisici interessati (effetto Seebeck per la produzione di potenza termoelettrica), si è cercato in seguito di analizzare le principali caratteristiche, ed elementi, del “module test system”. Successivamente a questa prima fase di analisi, sono stati condotti esperimenti che, con l’aiuto di modelli computazionali implementati attraverso il software Comsol Multiphysics, hanno permesso di studiare l’affidabilità del sistema di caratterizzazione. Infine, una volta acquisite le basi necessarie ad una corretta comprensione dei fenomeni fisici e delle caratteristiche relative alla strumentazione, sono stati analizzati moduli termoelettrici di tipo commerciale. In particolare, sono stati estrapolati dati quali correnti, tensioni, gradienti di temperatura, che hanno permesso di ricavare flussi termici, efficienze, e potenze che caratterizzano il modulo in questione durante le condizioni di funzionamento. I risultati ottenuti sono stati successivamente comparati con dati forniti dal produttore, presenti sul catalogo.

Phage therapy: A software system for phage quantification and kinetic model inference.

One of the most serious problems of the modern medicine is the growing emergence of antibiotic resistance among pathogenic bacteria. In this circumstance, different and innovative approaches for treating infections caused by multidrug-resistant bacteria are imperatively required. Bacteriophage Therapy is one among the fascinating approaches to be taken into account. This consists of the use of bacteriophages, viruses that infect bacteria, in order to defeat specific bacterial pathogens. Phage therapy is not an innovative idea, indeed, it was widely used around the world in the 1930s and 1940s, in order to treat various infection diseases, and it is still used in Eastern Europe and the former Soviet Union. Nevertheless, Western scientists mostly lost interest in further use and study of phage therapy and abandoned it after the discovery and the spread of antibiotics. The advancement of scientific knowledge of the last years, together with the encouraging results from recent animal studies using phages to treat bacterial infections, and above all the urgent need for novel and effective antimicrobials, have given a prompt for additional rigorous researches in this field. In particular, in the laboratory of synthetic biology of the department of Life Sciences at the University of Warwick, a novel approach was adopted, starting from the original concept of phage therapy, in order to study a concrete alternative to antibiotics. The innovative idea of the project consists in the development of experimental methodologies, which allow to engineer a programmable synthetic phage system using a combination of directed evolution, automation and microfluidics. The main aim is to make “the therapeutics of tomorrow individualized, specific, and self-regulated” (Jaramillo, 2015). In this context, one of the most important key points is the Bacteriophage Quantification. Therefore, in this research work, a mathematical model describing complex dynamics occurring in biological systems involving continuous growth of bacteriophages, modulated by the performance of the host organisms, was implemented as algorithms into a working software using MATLAB. The developed program is able to predict different unknown concentrations of phages much faster than the classical overnight Plaque Assay. What is more, it gives a meaning and an explanation to the obtained data, making inference about the parameter set of the model, that are representative of the bacteriophage-host interaction.

Inference of Raja miraletus population structure using mitochondrial and nuclear DNA: comparing the resolution power of molecular markers in exploring species and population boundaries

This study focused on the role of oceanographic discontinuities and the presence of transitional areas in shaping the population structure and the phylogeography of the Raja miraletus species complex, coupled with the test of the effective occurrence of past speciation events. The comparisons between the Atlantic African and the North-Eastern Atlantic-Mediterranean geographic populations were unravelled using both Cytochrome Oxidase I and eight microsatellite loci. This approach guaranteed a robust dataset for the identification of a speciation event between the Atlantic African clade, corresponding to the ex Raja ocellifera nominal species, and the NE Atlantic-Mediterranean R. miraletus clade. As a matter of fact, the origin of the Atlantic Africa and the NE Atlantic-Mediterranean deep split dated about 11.74MYA and was likely due to the synergic influence currents and two upwelling areas crossing the Western African Waters. Within the Mediterranean Sea, particular attention was also paid to the transitional area represented by Adventura and Maltese Bank, that might have contributed in sustaining the connectivity of the Western and the Eastern Mediterranean geographical populations. Furthermore, the geology of the easternmost part of Sicily and the geo-morphological depression of the Calabrian Arc could have driven the differentiation of the Eastern Mediterranean Sea. Although bathymetric and oceanographic discontinuity could represent barriers to dispersal and migration between Eastern and Western Mediterranean samples, a clear and complete genetic separation among them was not detected. Results produced by this work identified a speciation event defining Raja ocellifera and R. miraletus as two different species, and describing the R. miraletus species complex as the most ancient cryptic speciation event in the family Rajidae, representing another example of how strictly connected the environment, the behavioural habits and the evolutionary and ecologic drivers are.