Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping

Introduction
Mild cognitive impairment (MCI) has clinical value in its ability to predict later dementia. A better understanding of cognitive profiles can further help delineate who is most at risk of conversion to dementia. We aimed to (1) examine to what extent the usual MCI subtyping using core criteria corresponds to empirically defined clusters of patients (latent profile analysis [LPA] of continuous neuropsychological data) and (2) compare the two methods of subtyping memory clinic participants in their prediction of conversion to dementia.

Methods
Memory clinic participants (MCI, n = 139) and age-matched controls (n = 98) were recruited. Participants had a full cognitive assessment, and results were grouped (1) according to traditional MCI subtypes and (2) using LPA. MCI participants were followed over approximately 2 years after their initial assessment to monitor for conversion to dementia.

Results
Groups were well matched for age and education. Controls performed significantly better than MCI participants on all cognitive measures. With the traditional analysis, most MCI participants were in the amnestic multidomain subgroup (46.8%) and this group was most at risk of conversion to dementia (63%). From the LPA, a three-profile solution fit the data best. Profile 3 was the largest group (40.3%), the most cognitively impaired, and most at risk of conversion to dementia (68% of the group).

Discussion
LPA provides a useful adjunct in delineating MCI participants most at risk of conversion to dementia and adds confidence to standard categories of clinical inference.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

Rhodopsin gene polymorphism associated with divergent light environments in atlantic cod

The spectral sensitivity of visual pigments in vertebrate eyes is optimized for specific light conditions. One of such pigments, rhodopsin (RH1), mediates dim-light vision. Amino acid replacements at tuning sites may alter spectral sensitivity, providing a mechanism to adapt to ambient light conditions and depth of habitat in fish. Here we present a first investigation of RH1 gene polymorphism among two ecotypes of Atlantic cod in Icelandic waters, which experience divergent light environments throughout the year due to alternative foraging behaviour. We identified one synonymous single nucleotide polymorphism (SNP) in the RH1 protein coding region and one in the 3' untranslated region (3'-UTR) that are strongly divergent between these two ecotypes. Moreover, these polymorphisms coincided with the well-known panthophysin (Pan I) polymorphism that differentiates coastal and frontal (migratory) populations of Atlantic cod. While the RH1 SNPs do not provide direct inference for a specific molecular mechanism, their association with this dim-sensitive pigment indicates the involvement of the visual system in local adaptation of Atlantic cod.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.

Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.

Local Computation in Credal Networks

The goal of this contribution is to discuss local computation in credal networks — graphical models that can represent imprecise and indeterminate probability values. We analyze the inference problem in credal networks, discuss how inference algorithms can benefit from local computation, and suggest that local computation can be particularly important in approximate inference algorithms.

Climate drives temporal replacement and nested‐resultant richness patterns of Scottish coastal vegetation

Beta diversity quantifies spatial and/or temporal variation in species composition. It is comprised of two distinct components, species replacement and nestedness, which derive from opposing ecological processes. Using Scotland as a case study and a β-diversity partitioning framework, we investigate temporal replacement and nestedness patterns of coastal grassland species over a 34-yr time period. We aim to 1) understand the influence of two potentially pivotal processes (climate and land-use changes) on landscape-scale (5 × 5 km) temporal replacement and nestedness patterns, and 2) investigate whether patterns from one β-diversity component can mask observable patterns in the other.

We summarised key aspects of climate driven macro-ecological variation as measures of variance, long-term trends, between-year similarity and extremes, for three important climatic predictors (minimum temperature, water-balance and growing degree-days). Shifts in landscape-scale heterogeneity, a proxy of land-use change, was summarised as a spatial multiple-site dissimilarity measure. Together, these climatic and spatial predictors were used in a multi-model inference framework to gauge the relative contribution of each on temporal replacement and nestedness patterns.

Temporal β-diversity patterns were reasonably well explained by climate change but weakly explained by changes in landscape-scale heterogeneity. Climate was shown to have a greater influence on temporal nestedness than replacement patterns over our study period, linking nestedness patterns, as a result of imbalanced gains and losses, to climatic warming and extremes respectively. Important climatic predictors (i.e. growing degree-days) of temporal β-diversity were also identified, and contrasting patterns between the two β-diversity components revealed.

Results suggest climate influences plant species recruitment and establishment processes of Scotland's coastal grasslands, and while species extinctions take time, they are likely to be facilitated by climatic perturbations. Our findings also highlight the importance of distinguishing between different components of β-diversity, disentangling contrasting patterns than can mask one another.

Genetic structure, relationships and admixture with wild relatives in native pig breeds from Iberia and its islands

Background: Native pig breeds in the Iberian Peninsula are broadly classified as belonging to either the Celtic or the Mediterranean breed groups, but there are other local populations that do not fit into any of these groups. Most of the native pig breeds in Iberia are in danger of extinction, and the assessment of their genetic diversity and population structure, relationships and possible admixture between breeds, and the appraisal of conservation alternatives are crucial to adopt appropriate management strategies. Methods: A panel of 24 microsatellite markers was used to genotype 844 animals representing the 17 most important native swine breeds and wild populations existing in Portugal and Spain and various statistical tools were applied to analyze the results. Results: Genetic diversity was high in the breeds studied, with an overall mean of 13.6 alleles per locus and an average expected heterozygosity of 0.80. Signs of genetic bottlenecks were observed in breeds with a small census size, and population substructure was present in some of the breeds with larger census sizes. Variability among breeds accounted for about 20% of the total genetic diversity, and was explained mostly by differences among the Celtic, Mediterranean and Basque breed groups, rather than by differences between domestic and wild pigs. Breeds clustered closely according to group, and proximity was detected between wild pigs and the Mediterranean cluster of breeds. Most breeds had their own structure and identity, with very little evidence of admixture, except for the Retinto and Entrepelado varieties of the Mediterranean group, which are very similar. Genetic influence of the identified breed clusters extends beyond the specific geographical areas across borders throughout the Iberian Peninsula, with a very sharp transition from one breed group to another. Analysis of conservation priorities confirms that the ranking of a breed for conservation depends on the emphasis placed on its contribution to the betweenand within-breed components of genetic diversity. Conclusions: Native pig breeds in Iberia reveal high levels of genetic diversity, a solid breed structure and a clear organization in well-defined clusters.

Wireless networks congestion control

Congestion control in wireless networks is an important and open issue. Previous research has proven the poor performance of the Transport Control Protocol (TCP) in such networks. The factors that contribute to the poor performance of TCP in wireless environments concern its unsuitability to identify/detect and react properly to network events, its TCP window based ow control algorithm that is not suitable for the wireless channel, and the congestion collapse due to mobility. New rate based mechanisms have been proposed to mitigate TCP performance in wired and wireless networks. However, these mechanisms also present poor performance, as they lack of suitable bandwidth estimation techniques for multi-hop wireless networks. It is thus important to improve congestion control performance in wireless networks, incorporating components that are suitable for wireless environments. A congestion control scheme which provides an e - cient and fair sharing of the underlying network capacity and available bandwidth among multiple competing applications is crucial to the definition of new e cient and fair congestion control schemes on wireless multi-hop networks. The Thesis is divided in three parts. First, we present a performance evaluation study of several congestion control protocols against TCP, in wireless mesh and ad-hoc networks. The obtained results show that rate based congestion control protocols need an eficient and accurate underlying available bandwidth estimation technique. The second part of the Thesis presents a new link capacity and available bandwidth estimation mechanism denoted as rt-Winf (real time wireless inference). The estimation is performed in real-time and without the need to intrusively inject packets in the network. Simulation results show that rt-Winf obtains the available bandwidth and capacity estimation with accuracy and without introducing overhead trafic in the network. The third part of the Thesis proposes the development of new congestion control mechanisms to address the congestion control problems of wireless networks. These congestion control mechanisms use cross layer information, obtained by rt-Winf, to accurately and eficiently estimate the available bandwidth and the path capacity over a wireless network path. Evaluation of these new proposed mechanisms, through ns-2 simulations, shows that the cooperation between rt-Winf and the congestion control algorithms is able to significantly increase congestion control eficiency and network performance.

Optimal alarms systems and its application to financial time series

This thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.

Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

Soft-computing techniques applied to artificial tissue temperature estimation

Tese dout., Engenharia electrónica e computação - Processamento de sinal, Universidade do Algarve, 2008

Toxicity risks linked to cyanobacteria blooms in southern Portugal reservoirs

Tese de doutoramento, Ciências e Tecnologias do Ambiente, Escola Superior de Saúde, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

A definição de mercados relevantes no direito europeu e português da concorrência : teoria e prática

Tese de doutoramento, Direito (Ciências Jurídico-Económicas), Universidade de Lisboa, Faculdade de Direito, 2014