The role of intracellular calcium perturbations in muscle damage and dysfunction in mouse models of muscular dystrophy

Duchenne muscular dystrophy (DMD) is a neuromuscular disease caused by mutations in the dystrophin gene. DMD is clinically characterized by severe, progressive and irreversible loss of muscle function, in which most patients lose the ability to walk by their early teens and die by their early 20’s. Impaired intracellular calcium (Ca2+) regulation and activation of cell degradation pathways have been proposed as key contributors to DMD disease progression. This dissertation research consists of three studies investigating the role of intracellular Ca2+ in skeletal muscle dysfunction in different mouse models of DMD. Study one evaluated the role of Ca2+-activated enzymes (proteases) that activate protein degradation in excitation-contraction (E-C) coupling failure following repeated contractions in mdx and dystrophin-utrophin null (mdx/utr-/-) mice. Single muscle fibers from mdx/utr-/- mice had greater E-C coupling failure following repeated contractions compared to fibers from mdx mice. Moreover, protease inhibition during these contractions was sufficient to attenuate E-C coupling failure in muscle fibers from both mdx and mdx/utr-/- mice. Study two evaluated the effects of overexpressing the Ca2+ buffering protein sarcoplasmic/endoplasmic reticulum Ca2+-ATPase 1 (SERCA1) in skeletal muscles from mdx and mdx/utr-/- mice. Overall, SERCA1 overexpression decreased muscle damage and protected the muscle from contraction-induced injury in mdx and mdx/utr-/- mice. In study three, the cellular mechanisms underlying the beneficial effects of SERCA1 overexpression in mdx and mdx/utr-/- mice were investigated. SERCA1 overexpression attenuated calpain activation in mdx muscle only, while partially attenuating the degradation of the calpain target desmin in mdx/utr-/- mice. Additionally, SERCA1 overexpression decreased the SERCA-inhibitory protein sarcolipin in mdx muscle but did not alter levels of Ca2+ regulatory proteins (parvalbumin and calsequestrin) in either dystrophic model. Lastly, SERCA1 overexpression blunted the increase in endoplasmic reticulum stress markers Grp78/BiP in mdx mice and C/EBP homologous protein (CHOP) in mdx and mdx/utr-/- mice. Overall, findings from the studies presented in this dissertation provide new insight into the role of Ca2+ in muscle dysfunction and damage in different dystrophic mouse models. Further, these findings support the overall strategy for improving intracellular Ca2+ control for the development of novel therapies for DMD.

Random path sampling applied to vector models of magnetism

The recently reported Monte Carlo Random Path Sampling method (RPS) is here improved and its application is expanded to the study of the 2D and 3D Ising and discrete Heisenberg models. The methodology was implemented to allow use in both CPU-based high-performance computing infrastructures (C/MPI) and GPU-based (CUDA) parallel computation, with significant computational performance gains. Convergence is discussed, both in terms of free energy and magnetization dependence on field/temperature. From the calculated magnetization-energy joint density of states, fast calculations of field and temperature dependent thermodynamic properties are performed, including the effects of anisotropy on coercivity, and the magnetocaloric effect. The emergence of first-order magneto-volume transitions in the compressible Ising model is interpreted using the Landau theory of phase transitions. Using metallic Gadolinium as a real-world example, the possibility of using RPS as a tool for computational magnetic materials design is discussed. Experimental magnetic and structural properties of a Gadolinium single crystal are compared to RPS-based calculations using microscopic parameters obtained from Density Functional Theory.

An algorithm for the inclusion of RC compact models of packages into board level thermal simulation tools

International audience

Mouse models of diet-induced nonalcoholic steatohepatitis reproduce the heterogeneity of the human disease

International audience

An introduction to mathematical models of coagulation-fragmentation processes: a discrete deterministic mean-field approach

We summarise the properties and the fundamental mathematical results associated with basic models which describe coagulation and fragmentation processes in a deterministic manner and in which cluster size is a discrete quantity (an integer multiple of some basic unit size). In particular, we discuss Smoluchowski's equation for aggregation, the Becker-Döring model of simultaneous aggregation and fragmentation, and more general models involving coagulation and fragmentation.

Animal Models of Friedreich Ataxia

Friedreich ataxia (FRDA) is the most common form of autosomal-recessive ataxia. Common nonmotor features include cardiomyopathy and diabetes mellitus. At present, no effective treatments are available to prevent disease progression. Age of onset varies from infancy to adulthood. In the majority of patients, FRDA is caused by intronic GAA expansions in FXN, which encodes a highly-conserved small mitochondrial matrix protein, frataxin. A mouse model of FRDA has been difficult to generate because complete loss of frataxin causes early embryonic lethality. Although there are some controversies about the function of frataxin, recent biochemical and structural studies have confirmed that it is a component of the multiprotein complex that assembles iron-sulfur clusters in the mitochondrial matrix. The main consequences of frataxin deficiency are energy deficit, altered iron metabolism, and oxidative damage.

Reduced models of chemical reaction in chaotic flows

We describe and evaluate two reduced models for nonlinear chemical reactions in a chaotic laminar flow. Each model involves two separate steps to compute the chemical composition at a given location and time. The “manifold tracking model” first tracks backwards in time a segment of the stable manifold of the requisite point. This then provides a sample of the initial conditions appropriate for the second step, which requires solving one-dimensional problems for the reaction in Lagrangian coordinates. By contrast, the first step of the “branching trajectories model” simulates both the advection and diffusion of fluid particles that terminate at the appropriate point; the chemical reaction equations are then solved along each of the branched trajectories in a second step. Results from each model are compared with full numerical simulations of the reaction processes in a chaotic laminar flow.

Predictive models of buildings energy consumption

Dissertação de Mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Role of chemerin and its receptors in mouse models of tumorigenesis

Dissertação de Mestrado, Oncobiologia: Mecanismos Moleculares do Cancro, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2015

Can we use niche models of indicator species to predict the distribution of endangered communities?

European-wide conservation policies are based on the identification of priority habitats. However, research on conservation biogeography often relies on the results and projections of species distribution models to assess species' vulnerability to global change. We assess whether the distribution and structure of threatened communities can be predicted by the suitability of the environmental conditions for their indicator species. We present some preliminary results elucidating if using species distribution models of indicator species at a regional scale is a valid approach to predict these endangered communities. Dune plant assemblages, affected by severe conditions, are excellent models for studying possible interactions among their integrating species and the environment. We use data from an extensive survey of xerophytic inland sand dune scrub communities from Portugal, one of the most threatened habitat types of Europe. We identify indicator shrub species of different types of communities, model their geographical response to the environment, and evaluate whether the output of these niche models are able to predict the distribution of each type of community in a different region.

Chebanov law and Vakar formula in mathematical models of complex systems

Ecological models written in a mathematical language L(M) or model language, with a given style or methodology can be considered as a text. It is possible to apply statistical linguistic laws and the experimental results demonstrate that the behaviour of a mathematical model is the same of any literary text of any natural language. A text has the following characteristics: (a) the variables, its transformed functions and parameters are the lexic units or LUN of ecological models; (b) the syllables are constituted by a LUN, or a chain of them, separated by operating or ordering LUNs; (c) the flow equations are words; and (d) the distribution of words (LUM and CLUN) according to their lengths is based on a Poisson distribution, the Chebanov's law. It is founded on Vakar's formula, that is calculated likewise the linguistic entropy for L(M). We will apply these ideas over practical examples using MARIOLA model. In this paper it will be studied the problem of the lengths of the simple lexic units composed lexic units and words of text models, expressing these lengths in number of the primitive symbols, and syllables. The use of these linguistic laws renders it possible to indicate the degree of information given by an ecological model.

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^