Degenerate Gases of Strontium for Studies of Quantum Magnetism

We describe the construction and characterization of a new apparatus that can produce degenerate quantum gases of strontium. The realization of degenerate gases is an important first step toward future studies of quantum magnetism. Three of the four stable isotopes of strontium have been cooled into the degenerate regime. The experiment can make nearly pure Bose-Einstein condensates containing approximately 1x10^4 atoms, for strontium-86, and approximately 4x10^5 atoms, for strontium-84. We have also created degenerate Fermi gases of strontium-87 with a reduced temperature, T/T_F of approximately 0.2. The apparatus will be able to produce Bose-Einstein condensates of strontium-88 with straightforward modifications. We also report the first experimental and theoretical results from the strontium project. We have developed a technique to accelerate the continuous loading of strontium atoms into a magnetic trap. By applying a laser addressing the 3P1 to 3S1 transition in our magneto-optical trap, the rate at which atoms populate the magnetically-trapped 3P2 state can be increased by up to 65%. Quantum degenerate gases of atoms in the metastable 3P0 and 3P2 states are a promising platform for quantum simulation of systems with long-range interactions. We have performed an initial numerical study of a method to transfer the ground state degenerate gases that we can currently produce into one of the metastable states via a three-photon transition. Numerical simulations of the Optical Bloch equations governing the three-photon transition indicate that >90% of a ground state degenerate gas can be transferred into a metastable state.

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.

Optimization of quantum states for signaling across an arbitrary qubit noise channel with minimum-error detection

International audience

Equivalence of quantum field theories related by the θ-exact Seiberg-Witten map

The equivalence of the noncommutative U(N) quantum field theories related by the θ-exact Seiberg-Witten maps is, in this paper, proven to all orders in the perturbation theory with respect to the coupling constant. We show that this holds for super Yang-Mills theories with N=0, 1, 2, 4 supersymmetry. A direct check of this equivalence relation is performed by computing the one-loop quantum corrections to the quadratic part of the effective action in the noncommutative U(1) gauge theory with N=0, 1, 2, 4 supersymmetry.

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].^

Challenges with in vitro and in vivo experimental models of urinary bladder cancer for novel drug discovery

Urinary bladder cancer (UBC) is the second most frequent malignancy of the urinary system and the ninth most common cancer worldwide, affecting individuals over the age of 65. Several investigations have embarked on advancing knowledge of the mechanisms underlying urothelial carcinogenesis, understanding the mechanisms of antineoplastic drugs resistance and discovering new antineoplastic drugs. In vitro and in vivo models are crucial for providing additional insights into the mechanisms of urothelial carcinogenesis. With these models, various molecular pathways involved in urothelial carcinogenesis have been discovered, allowing therapeutic manipulation.

Deficit of quantal release of GABA in experimental models of temporal lobe epilepsy

Because GABA (gamma-aminobutyric acid) receptor-mediated inhibition controls the excitability of principal neurons in the brain, deficits in GABAergic inhibition have long been favored to explain seizures. In an experimental model of temporal lobe epilepsy, we have identified a deficit of inhibition in presynaptic GABAergic terminals characterized by decreased GABA quantal activity associated with reduced synaptic vesicle density. This decrease in vesicle number primarily seems to affect the reserve pool, rather than the docked or the readily releasable pool.

Occurrence and abundance models of threatened plant species: Applications to mitigate the impact of hydroelectric power dams

Species occurrence and abundance models are important tools that can be used in biodiversity conservation, and can be applied to predict or plan actions needed to mitigate the environmental impacts of hydropower dams. In this study our objectives were: (i) to model the occurrence and abundance of threatened plant species, (ii) to verify the relationship between predicted occurrence and true abundance, and (iii) to assess whether models based on abundance are more effective in predicting species occurrence than those based on presence–absence data. Individual representatives of nine species were counted within 388 randomly georeferenced plots (10 m × 50 m) around the Barra Grande hydropower dam reservoir in southern Brazil. We modelled their relationship with 15 environmental variables using both occurrence (Generalised Linear Models) and abundance data (Hurdle and Zero-Inflated models). Overall, occurrence models were more accurate than abundance models. For all species, observed abundance was significantly, although not strongly, correlated with the probability of occurrence. This correlation lost significance when zero-abundance (absence) sites were excluded from analysis, but only when this entailed a substantial drop in sample size. The same occurred when analysing relationships between abundance and probability of occurrence from previously published studies on a range of different species, suggesting that future studies could potentially use probability of occurrence as an approximate indicator of abundance when the latter is not possible to obtain. This possibility might, however, depend on life history traits of the species in question, with some traits favouring a relationship between occurrence and abundance. Reconstructing species abundance patterns from occurrence could be an important tool for conservation planning and the management of threatened species, allowing scientists to indicate the best areas for collection and reintroduction of plant germplasm or choose conservation areas most likely to maintain viable populations.