Análise Bayesiana de dados composicionais na presença de covariáveis

Pós-graduação em Matematica Aplicada e Computacional - FCT

Methodological Difficulties of Conducting Agroecological Studies from a Statistical Perspective

Statistical methods for analyzing agroecological data might not be able to help agroecologists to solve all of the current problems concerning crop and animal husbandry, but such methods could well help them assess, tackle, and resolve several agroecological issues in a more reliable and accurate manner. Therefore, our goal in this article is to discuss the importance of statistical tools for alternative agronomic approaches, because alternative approaches, such as organic farming, should not only be promoted by encouraging farmers to deploy agroecological techniques, but also by providing agroecologists with robust analyses based on rigorous statistical procedures.

A new probabilistic canopy dynamics model (SLCD) that is suitable for evergreen and deciduous forest ecosystems

There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change. While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the stand level on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable for enabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMs and G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrient fluxes by combining modeling approaches at the stand level.Our study aimed to estimate monthly changes of leaf area in response to climate variations from sparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) was designed to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and the total population size was limited depending on forest age and productivity. Foliage dynamics were driven by a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on the forest environment.The model was applied to three tree species growing under contrasting climates and soil types. In tropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameters on litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt stand were used to validate the model. Seasonal LAI changes were accurately rendered for both sites (R-2 = 0.898 adjustment, R-2 = 0.698 validation). Litterfall production was correctly simulated (R-2 = 0.562, R-2 = 0.4018 validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA model to simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological patterns were simulated with good accuracy in the two cases studied. However, IA/max was not accurately simulated in the beech forest, and further improvement is required.Our probabilistic approach is expected to contribute to improving predictions of LAI dynamics. The model formalism is general and suitable to broadleaf forests for a large range of ecological conditions. (C) 2014 Elsevier B.V. All rights reserved.

Comparative effects of photodynamic therapy mediated by curcumin on standard and clinical isolate of Streptococcus mutans

Aim: The aim of this study was investigate the effect of photodynamic therapy (PDT) using curcumin (C) as a photosensitizing agent irradiated with an LED (L) in the blue wavelength as a light source on a standard and clinical isolate of Streptococcus mutans (S. mutans) in a planktonic suspension model. Materials and methods: Suspensions of both strains were divided into 4 groups as follows: absence of C and L (control group: C–L–), with C and without L (C group: C+L–), absence of C with L (L group: C–L+) and presence of C and L (PDT group: C+L+). Three different concentrations of curcumin (0.75 mg/ml, 1.5 mg/ml and 3 mg/ml) and three light fluences of studied light source (24, 48 and 72 J cm–2) were tested. Aliquots of each studied group was plated in BHI agar and submitted to colony forming units counting (CFU/ml) and the data transformed into logarithmical scale. Results: A high photoinactivation rate of more than 70% was verified to standard S. mutans strain submitted to PDT whereas the clinical isolate showed a lower sensitivity to all the associations of curcumin and LED. A slight bacterial reduction was verified to C+L– and C–L+, demonstrating no toxic effects to the isolated application of light and photosensitizer to both S. mutans strains tested. Conclusion: Photodynamic therapy using a combination of curcumin and blue LED presented a substantial antimicrobial effect on S. mutans standard strain in a planktonic suspension model with a less pronounced effect on its clinical isolate counterparts due to resistance to this alternative approach. Clinical significance: Alternative antimicrobial approaches, as photodynamic therapy, should be encouraged due to optimal results against cariogenic bacteria aiming to prevent or treat dental caries.

3D superimposition and understanding temporomandibular joint arthritis

Structured AbstractObjectivesTo investigate the 3D morphological variations in 169 temporomandibular ioint (TMJ) condyles, using novel imaging statistical modeling approaches.Setting and sample populationThe Department of Orthodontics and Pediatric Dentistry at the University of Michigan. Cone beam CT scans were acquired from 69 subjects with long-term TMJ osteoarthritis (OA, mean age 39.115.7years), 15 subjects at initial consult diagnosis of OA (mean age 44.914.8years), and seven healthy controls (mean age 4312.4years).Materials and methods3D surface models of the condyles were constructed, and homologous correspondent points on each model were established. The statistical framework included Direction-Projection-Permutation (DiProPerm) for testing statistical significance of the differences between healthy controls and the OA groups determined by clinical and radiographic diagnoses.ResultsCondylar morphology in OA and healthy subjects varied widely with categorization from mild to severe bone degeneration or overgrowth. DiProPerm statistics supported a significant difference between the healthy control group and the initial diagnosis of OA group (t=6.6, empirical p-value=0.006) and between healthy and long-term diagnosis of OA group (t=7.2, empirical p-value=0). Compared with healthy controls, the average condyle in OA subjects was significantly smaller in all dimensions, except its anterior surface, even in subjects with initial diagnosis of OA.ConclusionThis new statistical modeling of condylar morphology allows the development of more targeted classifications of this condition than previously possible.

Statistical analysis and simulation techniques in wall-bounded turbulence

The present work is devoted to the assessment of the energy fluxes physics in the space of scales and physical space of wall-turbulent flows. The generalized Kolmogorov equation will be applied to DNS data of a turbulent channel flow in order to describe the energy fluxes paths from production to dissipation in the augmented space of wall-turbulent flows. This multidimensional description will be shown to be crucial to understand the formation and sustainment of the turbulent fluctuations fed by the energy fluxes coming from the near-wall production region. An unexpected behavior of the energy fluxes comes out from this analysis consisting of spiral-like paths in the combined physical/scale space where the controversial reverse energy cascade plays a central role. The observed behavior conflicts with the classical notion of the Richardson/Kolmogorov energy cascade and may have strong repercussions on both theoretical and modeling approaches to wall-turbulence. To this aim a new relation stating the leading physical processes governing the energy transfer in wall-turbulence is suggested and shown able to capture most of the rich dynamics of the shear dominated region of the flow. Two dynamical processes are identified as driving mechanisms for the fluxes, one in the near wall region and a second one further away from the wall. The former, stronger one is related to the dynamics involved in the near-wall turbulence regeneration cycle. The second suggests an outer self-sustaining mechanism which is asymptotically expected to take place in the log-layer and could explain the debated mixed inner/outer scaling of the near-wall statistics. The same approach is applied for the first time to a filtered velocity field. A generalized Kolmogorov equation specialized for filtered velocity field is derived and discussed. The results will show what effects the subgrid scales have on the resolved motion in both physical and scale space, singling out the prominent role of the filter length compared to the cross-over scale between production dominated scales and inertial range, lc, and the reverse energy cascade region lb. The systematic characterization of the resolved and subgrid physics as function of the filter scale and of the wall-distance will be shown instrumental for a correct use of LES models in the simulation of wall turbulent flows. Taking inspiration from the new relation for the energy transfer in wall turbulence, a new class of LES models will be also proposed. Finally, the generalized Kolmogorov equation specialized for filtered velocity fields will be shown to be an helpful statistical tool for the assessment of LES models and for the development of new ones. As example, some classical purely dissipative eddy viscosity models are analyzed via an a priori procedure.

Environmental Assessment of the Agricultural Sector

One of the main problems recognized in sustainable development goals and sustainable agricultural objectives is Climate change. Farming contributes significantly to the overall Greenhouse gases (GHG) in the atmosphere, which is approximately 10-12 percent of total GHG emissions, but when taking in consideration also land-use change, including deforestation driven by agricultural expansion for food, fiber and fuel the number rises to approximately 30 percent (Smith et. al., 2007). There are two distinct methodological approaches for environmental impact assessment; Life Cycle Assessment (a bottom up approach) and Input-Output Analysis (a top down approach). The two methodologies differ significantly but there is not an immediate choice between them if the scope of the study is on a sectorial level. Instead, as an alternative, hybrid approaches which combine these two approaches have emerged. The aim of this study is to analyze in a greater detail the agricultural sectors contribution to Climate change caused by the consumption of food products. Hence, to identify the food products that have the greatest impact through their life cycle, identifying their hotspots and evaluating the mitigation possibilities for the same. At the same time evaluating methodological possibilities and models to be applied for this purpose both on a EU level and on a country level (Italy).

Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce.

Investigating within-canopy variation of functional traits and cellular structure of sugar maple (Acer saccharum) leaves

Patterns of increasing leaf mass per area (LMA), area-based leaf nitrogen (Narea), and carbon isotope composition (δ13C) with increasing height in the canopy have been attributed to light gradients or hydraulic limitation in tall trees. Theoretical optimal distributions of LMA and Narea that scale with light maximize canopy photosynthesis; however, sub-optimal distributions are often observed due to hydraulic constraints on leaf development. Using observational, experimental, and modeling approaches, we investigated the response of leaf functional traits (LMA, density, thickness, and leaf nitrogen), leaf carbon isotope composition (δ13C), and cellular structure to light availability, height, and leaf water potential (Ψl) in an Acer saccharum forest to tease apart the influence of light and hydraulic limitations. LMA, leaf and palisade layer thickness, and leaf density were greater at greater light availability but similar heights, highlighting the strong control of light on leaf morphology and cellular structure. Experimental shading decreased both LMA and area-based leaf nitrogen (Narea) and revealed that LMA and Narea were more strongly correlated with height earlier in the growing season and with light later in the growing season. The supply of CO2 to leaves at higher heights appeared to be constrained by stomatal sensitivity to vapor pressure deficit (VPD) or midday leaf water potential, as indicated by increasing δ13C and VPD and decreasing midday Ψl with height. Model simulations showed that daily canopy photosynthesis was biased during the early growing season when seasonality was not accounted for, and was biased throughout the growing season when vertical gradients in LMA and Narea were not accounted for. Overall, our results suggest that leaves acclimate to light soon after leaf expansion, through an accumulation of leaf carbon, thickening of palisade layers and increased LMA, and reduction in stomatal sensitivity to Ψl or VPD. This period of light acclimation in leaves appears to optimize leaf function over time, despite height-related constraints early in the growing season. Our results imply that vertical gradients in leaf functional traits and leaf acclimation to light should be incorporated in canopy function models in order to refine estimates of canopy photosynthesis.

Arctic patterned-ground ecosystems: A synthesis of field studies and models along a North American Arctic Transect

Arctic landscapes have visually striking patterns of small polygons, circles, and hummocks. The linkages between the geophysical and biological components of these systems and their responses to climate changes are not well understood. The "Biocomplexity of Patterned Ground Ecosystems" project examined patterned-ground features (PGFs) in all five Arctic bioclimate subzones along an 1800-km trans-Arctic temperature gradient in northern Alaska and northwestern Canada. This paper provides an overview of the transect to illustrate the trends in climate, PGFs, vegetation, n-factors, soils, active-layer depth, and frost heave along the climate gradient. We emphasize the thermal effects of the vegetation and snow on the heat and water fluxes within patterned-ground systems. Four new modeling approaches build on the theme that vegetation controls microscale soil temperature differences between the centers and margins of the PGFs, and these in turn drive the movement of water, affect the formation of aggradation ice, promote differential soil heave, and regulate a host of system propel-ties that affect the ability of plants to colonize the centers of these features. We conclude with an examination of the possible effects of a climate wan-ning on patterned-ground ecosystems.

Endocardial Ablation to Eliminate Epicardial Arrhythmia Substrate in Scar-Related Ventricular Tachycardia

OBJECTIVES We evaluated the feasibility and safety of epicardial substrate elimination using endocardial radiofrequency (RF) delivery in patients with scar-related ventricular tachycardia (VT). BACKGROUND Epicardial RF delivery is limited by fat or associated with bleeding, extra-cardiac damages, coronary vessels and phrenic nerve injury. Alternative ablation approaches may be desirable. METHODS Forty-six patients (18 ischemic cardiomyopathy [ICM], 13 non-ischemic dilated cardiomyopathy [NICM], 15 arrhythmogenic right ventricular cardiomyopathy [ARVC]) with sustained VT underwent combined endo- and epicardial mapping. All patients received endocardial ablation targeting local abnormal ventricular activities in the endocardium (Endo-LAVA) and epicardium (Epi-LAVA), followed by epicardial ablation if needed. RESULTS From a total of 173 endocardial ablations targeting Epi-LAVA at the facing site, 48 (28%) applications (ICM: 20/71 [28%], NICM: 3/39 [8%], ARVC: 25/63 [40%]) successfully eliminated the Epi-LAVA. Presence of Endo-LAVA, most delayed and low bipolar amplitude of Epi-LAVA, low unipolar amplitude in the facing endocardium, and Epi-LAVA within a wall thinning area at CT scan were associated with successful ablation. Endocardial ablation could abolish all Epi-LAVA in 4 ICM and 2 ARVC patients, whereas all patients with NICM required epicardial ablation. Endocardial ablation was able to eliminate Epi-LAVA at least partially in 15 (83%) ICM, 2 (13%) NICM, and 11 (73%) ARVC patients, contributing to a potential reduction in epicardial RF applications. Pericardial bleeding occurred in 4 patients with epicardial ablation. CONCLUSIONS Elimination of Epi-LAVA using endocardial RF delivery is feasible and may be used first to reduce the risk of epicardial ablation.

Endocarditis and biofilm-associated pili of Enterococcus faecalis.

Increasing multidrug resistance in Enterococcus faecalis, a nosocomial opportunist and common cause of bacterial endocarditis, emphasizes the need for alternative therapeutic approaches such as immunotherapy or immunoprophylaxis. In an earlier study, we demonstrated the presence of antibodies in E. faecalis endocarditis patient sera to recombinant forms of 9 E. faecalis cell wall-anchored proteins; of these, we have now characterized an in vivo-expressed locus of 3 genes and an associated sortase gene (encoding sortase C; SrtC). Here, using mutation analyses and complementation, we demonstrated that both the ebp (encoding endocarditis and biofilm-associated pili) operon and srtC are important for biofilm production of E. faecalis strain OG1RF. In addition, immunogold electron microscopy using antisera against EbpA-EbpC proteins as well as patient serum demonstrated that E. faecalis produces pleomorphic surface pili. Assembly of pili and their cell wall attachment appeared to occur via a mechanism of cross-linking of the Ebp proteins by the designated SrtC. Importantly, a nonpiliated, allelic replacement mutant was significantly attenuated in an endocarditis model. These biologically important surface pili, which are antigenic in humans during endocarditis and encoded by a ubiquitous E. faecalis operon, may be a useful immunotarget for studies aimed at prevention and/or treatment of this pathogen.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Experimental validation of a mean field model of mineralized collagen fiber arrays at two levels of hierarchy

In the course of this study, stiffness of a fibril array of mineralized collagen fibrils modeled with a mean field method was validated experimentally at site-matched two levels of tissue hierarchy using mineralized turkey leg tendons (MTLT). The applied modeling approaches allowed to model the properties of this unidirectional tissue from nanoscale (mineralized collagen fibrils) to macroscale (mineralized tendon). At the microlevel, the indentation moduli obtained with a mean field homogenization scheme were compared to the experimental ones obtained with microindentation. At the macrolevel, the macroscopic stiffness predicted with micro finite element (μFE) models was compared to the experimental stiffness measured with uniaxial tensile tests. Elastic properties of the elements in μFE models were injected from the mean field model or two-directional microindentations. Quantitatively, the indentation moduli can be properly predicted with the mean-field models. Local stiffness trends within specific tissue morphologies are very weak, suggesting additional factors responsible for the stiffness variations. At macrolevel, the μFE models underestimate the macroscopic stiffness, as compared to tensile tests, but the correlations are strong.

Internal Models, Vestibular Cognition, and Mental Imagery: Conceptual Considerations

Vestibular cognition has recently gained attention. Despite numerous experimental and clinical demonstrations, it is not yet clear what vestibular cognition really is. For future research in vestibular cognition, adopting a computational approach will make it easier to explore the underlying mech- anisms. Indeed, most modeling approaches in vestibular science include a top-down or a priori component. We review recent Bayesian optimal observer models, and discuss in detail the conceptual value of prior assumptions, likelihood and posterior estimates for research in vestibular cognition. We then consider forward models in vestibular processing, which are required in order to distinguish between sensory input that is induced by active self-motion, and sensory input that is due to passive self-motion. We suggest that forward models are used not only in the service of estimating sensory states but they can also be drawn upon in an offline mode (e.g., spatial perspective transformations), in which interaction with sensory input is not desired. A computational approach to vestibular cogni- tion will help to discover connections across studies, and it will provide a more coherent framework for investigating vestibular cognition.