HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways

The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalisable statistical platform to infer the dynamic pathways by which many, potentially interacting, discrete traits are acquired or lost over time in biomedical systems. The platform uses HyperTraPS (hypercubic transition path sampling) to learn progression pathways from cross-sectional, longitudinal, or phylogenetically-linked data with unprecedented efficiency, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. Its Bayesian structure quantifies uncertainty in pathway structure and allows interpretable predictions of behaviours, such as which symptom a patient will acquire next. We exploit the model’s topology to provide visualisation tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways

Motivational profiles and their relationships with basic psychological needs, academic performance, study strategies, self-esteem, and vitality in dental students in Chile

Purpose To determine dental students’ motivational profiles through a person-centred approach and to analyse the associations with the satisfaction of their basic psychological needs, study strategies, academic performance, self-esteem, and vitality. Methods A total of 924 students from the University of San Sebastian (Chile) participated in this cross-sectional cor¬relational study in spring 2016. Data were collected through 5 self-reported instruments, in addition to students’ academic performance. The Cronbach alpha, descriptive statistics, and correla¬tion scores were computed. A k-means cluster analysis with intrinsic and controlled motivation was conducted to identify different mo-tivational profiles. Subsequently, multivariate analysis of covariance controlling for the effects of gender and year of study was carried out to assess differences among the retained motivational profiles and learning variables. Results All instruments showed acceptable Cronbach alpha scores. A 4-cluster solution was retained for the motivational profile over a 3- or 5-cluster solution. Students’ motiva-tional profiles were characterized by different degrees of intrinsic and controlled motivation. The high intrinsic motivation groups showed higher perceptions of their basic psychological, a greater propensity for a deep rather than surface study strategy, better academic performance, and higher scores for self-esteem and vitality than the low intrinsic motivation groups, regardless of the degree of controlled motivation. Conclusion Students with a high intrinsic motivation profile, regardless of their controlled motivation scores, reported better learning characteristics. Therefore, special attention should be paid to students’ motivational profiles, as the quality of motivation might serve as a basis for interventions to support their academic success and well-being

Using network-flow techniques to solve an optimization problem from surface-physics

The solid-on-solid model provides a commonly used framework for the description of surfaces. In the last years it has been extended in order to investigate the effect of defects in the bulk on the roughness of the surface. The determination of the ground state of this model leads to a combinatorial problem, which is reduced to an uncapacitated, convex minimum-circulation problem. We will show that the successive shortest path algorithm solves the problem in polynomial time.Comment: 8 Pages LaTeX, using Elsevier preprint style (macros included

HIV-2 viral production and infectivity are affected by APO3 host factors

Poster presented at the 7th Postgraduate iMed.ULisboa Students Meeting. Faculty of Pharmacy, Universidade de Lisboa, 15-16 July 2015.Egas Moniz - Cooperativa de Ensino Superior CRL and Fundação para a Ciência e Tecnologia, Lisbon, Portuga

Comparative study of relationship between bruxism and decrease telomeres length

Poster presented at the First International Congress of CiiEM - From Basic Sciences To Clinical Research. Egas Moniz, Caparica, Portugal, 27-28 November 201

Methodology of Building An Expert System Using Induction Rules with Structured Programming

This article aims to give an idea of how to develop expert systems to help society in all senses. The rules of induction used with MYCIN can help us in medicine, agricultura, and the financial system, implementing an inference engine with different rules so that in case there are no people, the expert system can diagnose in a clear and precise way, based on a knowledge base with some consequents and their corresponding antecedents, these artificial intelligence algorithms used will allow us to determine what type of consequence or conclusion it gives us, of the chaining of several antecedents with structured programming to give us its corresponding answer, then this research is aimed at obtaining an agile methodology for expert systems that will help us in the development and implementation of this type of systems.     Keywords: algorithms of artificial intelligence, knowledge base, artificial intelligence, inference machine, production rules, real-time systems, expert system

APOBEC3 host factors modulate viral production and infectivity of HIV-2

Poster presented at the 15th European AIDS Conference/EACS. Barcelona, 21-24 October 2015.This work was funded by FCT – SFRH/BD/81921/2011 and Egas Moniz, Cooperativa de Ensino Superior, CRL, Portugal

Lower Critical Dimension of Ising Spin Glasses

Exact ground states of two-dimensional Ising spin glasses with Gaussian and bimodal (+- J) distributions of the disorder are calculated using a ``matching'' algorithm, which allows large system sizes of up to N=480^2 spins to be investigated. We study domain walls induced by two rather different types of boundary-condition changes, and, in each case, analyze the system-size dependence of an appropriately defined ``defect energy'', which we denote by DE. For Gaussian disorder, we find a power-law behavior DE ~ L^\theta, with \theta=-0.266(2) and \theta=-0.282(2) for the two types of boundary condition changes. These results are in reasonable agreement with each other, allowing for small systematic effects. They also agree well with earlier work on smaller sizes. The negative value indicates that two dimensions is below the lower critical dimension d_c. For the +-J model, we obtain a different result, namely the domain-wall energy saturates at a nonzero value for L\to \infty, so \theta = 0, indicating that the lower critical dimension for the +-J model exactly d_c=2.Comment: 4 pages, 4 figures, 1 table, revte