Metodologies de Desenvolupament Dirigides per Models

En aquest treball, s'introduiran dos de les metodologies de desenvolupament dirigides per models més significatives: Model Driven Architecture (MDA) i Domain Specific Modeling (DSM). Així mateix, es presentarà un estudi comparatiu d'algunes de les diferents eines existents actualment al mercat que els hi donen suport.

Theoretical study of the electrostatically driven step of receptor-G protein recognition.

This study proposes a theoretical model describing the electrostatically driven step of the alpha 1 b-adrenergic receptor (AR)-G protein recognition. The comparative analysis of the structural-dynamics features of functionally different receptor forms, i.e., the wild type (ground state) and its constitutively active mutants D142A and A293E, was instrumental to gain insight on the receptor-G protein electrostatic and steric complementarity. Rigid body docking simulations between the different forms of the alpha 1 b-AR and the heterotrimeric G alpha q, G alpha s, G alpha i1, and G alpha t suggest that the cytosolic crevice shared by the active receptor and including the second and the third intracellular loops as well as the cytosolic extension of helices 5 and 6, represents the receptor surface with docking complementarity with the G protein. On the other hand, the G protein solvent-exposed portions that recognize the intracellular loops of the activated receptors are the N-terminal portion of alpha 3, alpha G, the alpha G/alpha 4 loop, alpha 4, the alpha 4/beta 6 loop, alpha 5, and the C-terminus. Docking simulations suggest that the two constitutively active mutants D142A and A293E recognize different G proteins with similar selectivity orders, i.e., G alpha q approximately equal to G alpha s > G alpha i > G alpha t. The theoretical models herein proposed might provide useful suggestions for new experiments aiming at exploring the receptor-G protein interface.

Analysis of angiotensin II- and ACTH-driven mineralocorticoid functions and omental adiposity in a non-genetic, hyperadipose female rat phenotype

The hypothalamic damage induced by neonatal treatment with monosodium l-glutamate (MSG) induces several metabolic abnormalities, resulting in a rat hyperleptinemic-hyperadipose phenotype. This study was conducted to explore the impact of the neonatal MSG treatment, in the adult (120 days old) female rat on: (a) the in vivo and in vitro mineralocorticoid responses to ACTH and angiotensin II (AII); (b) the effect of leptin on ACTH- and AII-stimulated mineralocorticoid secretions by isolated corticoadrenal cells; and (c) abdominal adiposity characteristics. Our data indicate that, compared with age-matched controls, MSG rats displayed: (1) enhanced and reduced mineralocorticoid responses to ACTH and AII treatments, respectively, effects observed in both in vivo and in vitro conditions; (2) adrenal refractoriness to the inhibitory effect of exogenous leptin on ACTH-stimulated aldosterone output by isolated adrenocortical cells; and (3) distorted omental adiposity morphology and function. This study supports that the adult hyperleptinemic MSG female rat is characterized by enhanced ACTH-driven mineralocorticoid function, impaired adrenal leptin sensitivity, and disrupted abdominal adiposity function. MSG rats could counteract undesirable effects of glucocorticoid excess, by developing a reduced AII-driven mineralocorticoid function. Thus, chronic hyperleptinemia could play a protective role against ACTH-mediated allostatic loads in the adrenal leptin resistant, MSG female rat phenotype.

Evidence evaluation in fingerprint comparison and automated fingerprint identification systems: modeling between finger variability

In the context of the investigation of the use of automated fingerprint identification systems (AFIS) for the evaluation of fingerprint evidence, the current study presents investigations into the variability of scores from an AFIS system when fingermarks from a known donor are compared to fingerprints that are not from the same source. The ultimate goal is to propose a model, based on likelihood ratios, which allows the evaluation of mark-to-print comparisons. In particular, this model, through its use of AFIS technology, benefits from the possibility of using a large amount of data, as well as from an already built-in proximity measure, the AFIS score. More precisely, the numerator of the LR is obtained from scores issued from comparisons between impressions from the same source and showing the same minutia configuration. The denominator of the LR is obtained by extracting scores from comparisons of the questioned mark with a database of non-matching sources. This paper focuses solely on the assignment of the denominator of the LR. We refer to it by the generic term of between-finger variability. The issues addressed in this paper in relation to between-finger variability are the required sample size, the influence of the finger number and general pattern, as well as that of the number of minutiae included and their configuration on a given finger. Results show that reliable estimation of between-finger variability is feasible with 10,000 scores. These scores should come from the appropriate finger number/general pattern combination as defined by the mark. Furthermore, strategies of obtaining between-finger variability when these elements cannot be conclusively seen on the mark (and its position with respect to other marks for finger number) have been presented. These results immediately allow case-by-case estimation of the between-finger variability in an operational setting.

Local WMR navigation with monocular data

This article presents recent WMR (wheeled mobile robot) navigation experiences using local perception knowledge provided by monocular and odometer systems. A local narrow perception horizon is used to plan safety trajectories towards the objective. Therefore, monocular data are proposed as a way to obtain real time local information by building two dimensional occupancy grids through a time integration of the frames. The path planning is accomplished by using attraction potential fields, while the trajectory tracking is performed by using model predictive control techniques. The results are faced to indoor situations by using the lab available platform consisting in a differential driven mobile robot

Explaining Fiscal Balances with a Simultaneous Equation Model of Revenue and Expenditure : A Case Study of Swiss Cantons Using Panel Data

Empirical literature on the analysis of the efficiency of measures for reducing persistent government deficits has mainly focused on the direct explanation of deficit. By contrast, this paper aims at modeling government revenue and expenditure within a simultaneous framework and deriving the fiscal balance (surplus or deficit) equation as the difference between the two variables. This setting enables one to not only judge how relevant the explanatory variables are in explaining the fiscal balance but also understand their impact on revenue and/or expenditure. Our empirical results, obtained by using a panel data set on Swiss Cantons for the period 1980-2002, confirm the relevance of the approach followed here, by providing unambiguous evidence of a simultaneous relationship between revenue and expenditure. They also reveal strong dynamic components in revenue, expenditure, and fiscal balance. Among the significant determinants of public fiscal balance we not only find the usual business cycle elements, but also and more importantly institutional factors such as the number of administrative units, and the ease with which people can resort to political (direct democracy) instruments, such as public initiatives and referendum.

Fosfomycin versus meropenem in bacteraemic urinary tract infections caused by extended-spectrum β-lactamase-producing Escherichia coli (FOREST): study protocol for an investigator-driven randomised controlled trial.

INTRODUCTION Finding therapeutic alternatives to carbapenems in infections caused by extended-spectrum β-lactamase-producing Escherichia coli (ESBL-EC) is imperative. Although fosfomycin was discovered more than 40 years ago, it was not investigated in accordance with current standards and so is not used in clinical practice except in desperate situations. It is one of the so-called neglected antibiotics of high potential interest for the future. METHODS AND ANALYSIS The main objective of this project is to demonstrate the clinical non-inferiority of intravenous fosfomycin with regard to meropenem for treating bacteraemic urinary tract infections (UTI) caused by ESBL-EC. This is a 'real practice' multicentre, open-label, phase III randomised controlled trial, designed to compare the clinical and microbiological efficacy, and safety of intravenous fosfomycin (4 g/6 h) and meropenem (1 g/8 h) as targeted therapy for this infection; a change to oral therapy is permitted after 5 days in both arms, in accordance with predetermined options. The study design follows the latest recommendations for designing trials investigating new options for multidrug-resistant bacteria. Secondary objectives include the study of fosfomycin concentrations in plasma and the impact of both drugs on intestinal colonisation by multidrug-resistant Gram-negative bacilli. ETHICS AND DISSEMINATION Ethical approval was obtained from the Andalusian Coordinating Institutional Review Board (IRB) for Biomedical Research (Referral Ethics Committee), which obtained approval from the local ethics committees at all participating sites in Spain (22 sites). Data will be presented at international conferences and published in peer-reviewed journals. DISCUSSION This project is proposed as an initial step in the investigation of an orphan antimicrobial of low cost with high potential as a therapeutic alternative in common infections such as UTI in selected patients. These results may have a major impact on the use of antibiotics and the development of new projects with this drug, whether as monotherapy or combination therapy. TRIAL REGISTRATION NUMBER NCT02142751. EudraCT no: 2013-002922-21. Protocol V.1.1 dated 14 March 2014.

Segmentation and grid generation for numerical simulations of vad connections with patient-specific data

Objectives: We are interested in the numerical simulation of the anastomotic region comprised between outflow canula of LVAD and the aorta. Segmenta¬tion, geometry reconstruction and grid generation from patient-specific data remain an issue because of the variable quality of DICOM images, in particular CT-scan (e.g. metallic noise of the device, non-aortic contrast phase). We pro¬pose a general framework to overcome this problem and create suitable grids for numerical simulations.Methods: Preliminary treatment of images is performed by reducing the level window and enhancing the contrast of the greyscale image using contrast-limited adaptive histogram equalization. A gradient anisotropic diffusion filter is applied to reduce the noise. Then, watershed segmentation algorithms and mathematical morphology filters allow reconstructing the patient geometry. This is done using the InsightToolKit library (www.itk.org). Finally the Vascular Model¬ing ToolKit (www.vmtk.org) and gmsh (www.geuz.org/gmsh) are used to create the meshes for the fluid (blood) and structure (arterial wall, outflow canula) and to a priori identify the boundary layers. The method is tested on five different patients with left ventricular assistance and who underwent a CT-scan exam.Results: This method produced good results in four patients. The anastomosis area is recovered and the generated grids are suitable for numerical simulations. In one patient the method failed to produce a good segmentation because of the small dimension of the aortic arch with respect to the image resolution.Conclusions: The described framework allows the use of data that could not be otherwise segmented by standard automatic segmentation tools. In particular the computational grids that have been generated are suitable for simulations that take into account fluid-structure interactions. Finally the presented method features a good reproducibility and fast application.

Protocol for the modeling the epidemiologic transition study: a longitudinal observational study of energy balance and change in body weight, diabetes and cardiovascular disease risk.

ABSTRACT: BACKGROUND: The prevalence of obesity has increased in societies of all socio-cultural backgrounds. To date, guidelines set forward to prevent obesity have universally emphasized optimal levels of physical activity. However there are few empirical data to support the assertion that low levels of energy expenditure in activity is a causal factor in the current obesity epidemic are very limited. METHODS: The Modeling the Epidemiologic Transition Study (METS) is a cohort study designed to assess the association between physical activity levels and relative weight, weight gain and diabetes and cardiovascular disease risk in five population-based samples at different stages of economic development. Twenty-five hundred young adults, ages 25-45, were enrolled in the study; 500 from sites in Ghana, South Africa, Seychelles, Jamaica and the United States. At baseline, physical activity levels were assessed using accelerometry and a questionnaire in all participants and by doubly labeled water in a subsample of 75 per site. We assessed dietary intake using two separate 24-h recalls, body composition using bioelectrical impedance analysis, and health history, social and economic indicators by questionnaire. Blood pressure was measured and blood samples collected for measurement of lipids, glucose, insulin and adipokines. Full examination including physical activity using accelerometry, anthropometric data and fasting glucose will take place at 12 and 24 months. The distribution of the main variables and the associations between physical activity, independent of energy intake, glucose metabolism and anthropometric measures will be assessed using cross-section and longitudinal analysis within and between sites. DISCUSSION: METS will provide insight on the relative contribution of physical activity and diet to excess weight, age-related weight gain and incident glucose impairment in five populations' samples of young adults at different stages of economic development. These data should be useful for the development of empirically-based public health policy aimed at the prevention of obesity and associated chronic diseases.

A stratified approach for modeling the distribution of a threatened ant species in the Swiss National Park

We present models predicting the potential distribution of a threatened ant species, Formica exsecta Nyl., in the Swiss National Park ( SNP). Data to fit the models have been collected according to a random-stratified design with an equal number of replicates per stratum. The basic aim of such a sampling strategy is to allow the formal testing of biological hypotheses about those factors most likely to account for the distribution of the modeled species. The stratifying factors used in this study were: vegetation, slope angle and slope aspect, the latter two being used as surrogates of solar radiation, considered one of the basic requirements of F. exsecta. Results show that, although the basic stratifying predictors account for more than 50% of the deviance, the incorporation of additional non-spatially explicit predictors into the model, as measured in the field, allows for an increased model performance (up to nearly 75%). However, this was not corroborated by permutation tests. Implementation on a national scale was made for one model only, due to the difficulty of obtaining similar predictors on this scale. The resulting map on the national scale suggests that the species might once have had a broader distribution in Switzerland. Reasons for its particular abundance within the SNP might possibly be related to habitat fragmentation and vegetation transformation outside the SNP boundaries.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Genetic and genomic analysis modeling of germline c-MYC overexpression and cancer susceptibility

Background: Germline genetic variation is associated with the differential expression of many human genes. The phenotypic effects of this type of variation may be important when considering susceptibility to common genetic diseases. Three regions at 8q24 have recently been identified to independently confer risk of prostate cancer. Variation at 8q24 has also recently been associated with risk of breast and colorectal cancer. However, none of the risk variants map at or relatively close to known genes, with c-MYC mapping a few hundred kilobases distally. Results: This study identifies cis-regulators of germline c-MYC expression in immortalized lymphocytes of HapMap individuals. Quantitative analysis of c-MYC expression in normal prostate tissues suggests an association between overexpression and variants in Region 1 of prostate cancer risk. Somatic c-MYC overexpression correlates with prostate cancer progression and more aggressive tumor forms, which was also a pathological variable associated with Region 1. Expression profiling analysis and modeling of transcriptional regulatory networks predicts a functional association between MYC and the prostate tumor suppressor KLF6. Analysis of MYC/Myc-driven cell transformation and tumorigenesis substantiates a model in which MYC overexpression promotes transformation by down-regulating KLF6. In this model, a feedback loop through E-cadherin down-regulation causes further transactivation of c-MYC.Conclusion: This study proposes that variation at putative 8q24 cis-regulator(s) of transcription can significantly alter germline c-MYC expression levels and, thus, contribute to prostate cancer susceptibility by down-regulating the prostate tumor suppressor KLF6 gene.

Neuronal adaptation effects in decision making

Recently, there has been an increased interest on the neural mechanisms underlying perceptual decision making. However, the effect of neuronal adaptation in this context has not yet been studied. We begin our study by investigating how adaptation can bias perceptual decisions. We considered behavioral data from an experiment on high-level adaptation-related aftereffects in a perceptual decision task with ambiguous stimuli on humans. To understand the driving force behind the perceptual decision process, a biologically inspired cortical network model was used. Two theoretical scenarios arose for explaining the perceptual switch from the category of the adaptor stimulus to the opposite, nonadapted one. One is noise-driven transition due to the probabilistic spike times of neurons and the other is adaptation-driven transition due to afterhyperpolarization currents. With increasing levels of neural adaptation, the system shifts from a noise-driven to an adaptation-driven modus. The behavioral results show that the underlying model is not just a bistable model, as usual in the decision-making modeling literature, but that neuronal adaptation is high and therefore the working point of the model is in the oscillatory regime. Using the same model parameters, we studied the effect of neural adaptation in a perceptual decision-making task where the same ambiguous stimulus was presented with and without a preceding adaptor stimulus. We find that for different levels of sensory evidence favoring one of the two interpretations of the ambiguous stimulus, higher levels of neural adaptation lead to quicker decisions contributing to a speed–accuracy trade off.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]