The role of parametric assumptions in adaptive Bayesian estimation.

Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter values larger than their counterparts in the psychometric function, (c) stimulus placement at the prior mean, and (d) estimates defined as the posterior mean. Unbiasedness arises in only 10 trials, and 20 trials ensure constant standard errors. The standard error of the estimates equals 0.617 times the inverse of the square root of the number of trials. Other variants yielded bias and larger standard errors.

Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

Piotr Omenzetter and Simon Hoell’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.

Associação entre comprometimento motor, mobilidade e qualidade de vida em crianças com paralisia cerebral espástica, nível I, II e III do Gross Motor Function Classification System

The objective of this study was to verify the association between some mobility items of the International Classification Functionality (ICF), with the evaluations Gross Motor Function Measure (GMFM-88), 1-minute walk test (1MWT) and if the motor impairment influences the quality of life in children with Cerebral Palsy (PC), by using the Paediatric Quality of Life Inventory (PedsQL 4.0 versions for children and parents). The study included 22 children with cerebral palsy spastic, classified in levels I, II, and III on the Gross Motor Function Classification System (GMFCS), with age group of 9.9 years old. Among those who have participated, seven of them were level I, eight of them were level II and seven of them were level III. All of the children and teenagers were rated by using check list ICF (mobility item), GMFM-88, 1-minute walk test and PedsQL 4.0 questionnaires for children and parents. It was observed a strong correlation between GMFM-88 with check list ICF (mobility item), but moderate correlation between GMFM-88 and 1-minute walk test (1MWT). It was also moderate the correlation between the walking test and the check list ICF (mobility item). The correlation between PedsQl 4.0 questionnaires for children and parents was weak, as well as the correlation of both with GMFM, ICF (mobility item) and the walking test. The lack of interrelation between physical function tests and quality of life, indicates that, regardless of the severity of the motor impairment and the difficulty with mobility, children and teenagers suffering of PC spastic, functional level I, II and III GMFCS and their parents have a varied opinion regarding the perception of well being and life satisfaction.

Function of the PDLIM2 protein in oncogenic Wnt signalling pathways

Aberrant regulation of the Wnt signalling pathway is a recurrent theme in cancer biology. Hyper activation due to oncogenic mutations and paracrine activity has been found in both colon cancer and breast cancer, and continues to evolve as a central mechanism in oncogenesis. PDLIM2, a cytoskeletal PDZ protein, is an IGF-1 regulated gene that is highly expressed in cancer cell lines derived from metastatic tumours. Suppression of PDLIM2 inhibits polarized cell migration, reverses the Epithelial to Mesenchymal transition (EMT) phenotype, suppresses the transcription of β-catenin target genes, and regulates gene expression of key transcription factors in EMT. This thesis investigates the mechanism by which PDLIM2 contributes to the maintenance of Wnt signalling in cancer cells. Here we show that PDLIM2 is a critical regulator of the Wnt pathway by regulating β-catenin at the adherens juctions, as also its transcriptional activity by the interaction of PDLIM2 with TCF4 at the nucleus. Evaluation of PDLIM2 in macrophages and co-culture studies with cancer cells and fibroblasts showed the influence exerted on PDLIM2 by paracrine cues. Thus, PDLIM2 integrates cytoskeleton signalling with gene expression by modulating the Wnt signalling pathway and reconciling microenvironmental cues with signals in epithelial cells. Negative correlation of mRNA and protein levels in the triple negative breast cancer cell BT549 suggests that PDLIM2 is part of a more complex mechanism that involves transcription and posttranslational modifications. GST pulldown studies and subsequent mass spectrometry analysis showed that PDLIM2 interacts with 300 proteins, with a high biological function in protein biosynthesis and Ubiquitin/proteasome pathways, including 13 E3 ligases. Overall, these data suggest that PDLIM2 has two distinct functions depending of its location. Located at the cytoplasm mediates cytoskeletal re-arrangements, whereas at the nucleus PDLIM2 acts as a signal transduction adaptor protein mediating transcription and ubiquitination of key transcription factors in cancer development.

RELIBILITY ANALYSIS OF SIMPLE SLOPES AND SOIL-STRUCTURES WITH LINEAR LIMIT STATES

The first objective of this research was to develop closed-form and numerical probabilistic methods of analysis that can be applied to otherwise conventional methods of unreinforced and geosynthetic reinforced slopes and walls. These probabilistic methods explicitly include random variability of soil and reinforcement, spatial variability of the soil, and cross-correlation between soil input parameters on probability of failure. The quantitative impact of simultaneously considering the influence of random and/or spatial variability in soil properties in combination with cross-correlation in soil properties is investigated for the first time in the research literature. Depending on the magnitude of these statistical descriptors, margins of safety based on conventional notions of safety may be very different from margins of safety expressed in terms of probability of failure (or reliability index). The thesis work also shows that intuitive notions of margin of safety using conventional factor of safety and probability of failure can be brought into alignment when cross-correlation between soil properties is considered in a rigorous manner. The second objective of this thesis work was to develop a general closed-form solution to compute the true probability of failure (or reliability index) of a simple linear limit state function with one load term and one resistance term expressed first in general probabilistic terms and then migrated to a LRFD format for the purpose of LRFD calibration. The formulation considers contributions to probability of failure due to model type, uncertainty in bias values, bias dependencies, uncertainty in estimates of nominal values for correlated and uncorrelated load and resistance terms, and average margin of safety expressed as the operational factor of safety (OFS). Bias is defined as the ratio of measured to predicted value. Parametric analyses were carried out to show that ignoring possible correlations between random variables can lead to conservative (safe) values of resistance factor in some cases and in other cases to non-conservative (unsafe) values. Example LRFD calibrations were carried out using different load and resistance models for the pullout internal stability limit state of steel strip and geosynthetic reinforced soil walls together with matching bias data reported in the literature.

Nonlinear soil-structure interaction for buried pressure pipes under differential ground motion

Pipelines extend thousands of kilometers across wide geographic areas as a network to provide essential services for modern life. It is inevitable that pipelines must pass through unfavorable ground conditions, which are susceptible to natural disasters. This thesis investigates the behaviour of buried pressure pipelines experiencing ground distortions induced by normal faulting. A recent large database of physical modelling observations on buried pipes of different stiffness relative to the surrounding soil subjected to normal faults provided a unique opportunity to calibrate numerical tools. Three-dimensional finite element models were developed to enable the complex soil-structure interaction phenomena to be further understood, especially on the subjects of gap formation beneath the pipe and the trench effect associated with the interaction between backfill and native soils. Benchmarked numerical tools were then used to perform parametric analysis regarding project geometry, backfill material, relative pipe-soil stiffness and pipe diameter. Seismic loading produces a soil displacement profile that can be expressed by isoil, the distance between the peak curvature and the point of contraflexure. A simplified design framework based on this length scale (i.e., the Kappa method) was developed, which features estimates of longitudinal bending moments of buried pipes using a characteristic length, ipipe, the distance from peak to zero curvature. Recent studies indicated that empirical soil springs that were calibrated against rigid pipes are not suitable for analyzing flexible pipes, since they lead to excessive conservatism (for design). A large-scale split-box normal fault simulator was therefore assembled to produce experimental data for flexible PVC pipe responses to a normal fault. Digital image correlation (DIC) was employed to analyze the soil displacement field, and both optical fibres and conventional strain gauges were used to measure pipe strains. A refinement to the Kappa method was introduced to enable the calculation of axial strains as a function of pipe elongation induced by flexure and an approximation of the longitudinal ground deformations. A closed-form Winkler solution of flexural response was also derived to account for the distributed normal fault pattern. Finally, these two analytical solutions were evaluated against the pipe responses observed in the large-scale laboratory tests.

The relationship between changes in cardiovascular function and changes in VO2peak following SIT

Background: It is well known that sprint interval training (SIT), induces significant increases in peak oxygen uptake (VO2peak) at the group level. However, there have been only a few studies that have addressed the variability of VO2peak response following SIT, and precise mechanism(s) that may explain individual magnitude of response are unknown. Purpose: Therefore, the purpose of this thesis was to: 1) examine the inter-individual variability of the VO2peak response following SIT, 2) to inspect the relationship between changes in both central and peripheral measures and changes in VO2peak, and 3) to assess if peripheral or central adaptations play a role in whether an individual is a high or low responder with respect to VO2peak. Subjects: Twenty-two young, recreationally active males (age: 20.4 1.7 years; weight: 78.4 10.2 kg; VO2peak: 3.7 0.62 L/min) Methods: VO2peak (L/min), peak cardiac output (Qpeak [L/min]), and peak deoxygenated hemoglobin (HHbpeak [mM]) were measured before and after 16 sessions of SIT (Tabata Protocol) over four weeks. Peak a-vO2diff was calculated using a derivation of the Fick equation. Results: Due to a systematic error, HHbpeak could not be used to differentiate between individual responses. There was a large range of VO2peak response from pre to post testing (-4.75 to 32.18% change) and there was a significant difference between the Low Response Group (LRG) (n=8) and the High Response Group (HRG) (n=8) [f(1, 14)= 64.27, p<0.001]. Furthermore, there was no correlation between delta () VO2peak and Qpeak (r=-0.18, p=0.46) for all participants, nor was there an interaction effect between the Low and High Response Groups [f(1,11)=0.572, p=0.47]. Lastly, there was a significant correlation between VO2peak and peak a-vO2diff [r=0.692, p<0.001], and a significant interaction effect with peak a-vO2diff [f(1, 14)= 13.27, p<0.004] when comparing the HRG to the LRG. Conclusions: There was inter-individual variability of VO2peak response following 4 weeks of SIT, but central adaptations did not influence this variation. This suggests that peripheral adaptations may be responsible for VO2peak adaptation.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Linking Parametric CAD with Adjoint Surface Sensitivities

The goal of this work is to present an efficient CAD-based adjoint process chain for calculating parametric sensitivities (derivatives of the objective function with respect to the CAD parameters) in timescales acceptable for industrial design processes. The idea is based on linking parametric design velocities (geometric sensitivities computed from the CAD model) with adjoint surface sensitivities. A CAD-based design velocity computation method has been implemented based on distances between discrete representations of perturbed geometries. This approach differs from other methods due to the fact that it works with existing commercial CAD packages (unlike most analytical approaches) and it can cope with the changes in CAD model topology and face labeling. Use of the proposed method allows computation of parametric sensitivities using adjoint data at a computational cost which scales with the number of objective functions being considered, while it is essentially independent of the number of design variables. The gradient computation is demonstrated on test cases for a Nozzle Guide Vane (NGV) model and a Turbine Rotor Blade model. The results are validated against finite difference values and good agreement is shown. This gradient information can be passed to an optimization algorithm, which will use it to update the CAD model parameters.

Parametric CAD model based shape optimization using adjoint functions

Adjoint methods have proven to be an efficient way of calculating the gradient of an objective function with respect to a shape parameter for optimisation, with a computational cost nearly independent of the number of the design variables [1]. The approach in this paper links the adjoint surface sensitivities (gradient of objective function with respect to the surface movement) with the parametric design velocities (movement of the surface due to a CAD parameter perturbation) in order to compute the gradient of the objective function with respect to CAD variables.
For a successful implementation of shape optimization strategies in practical industrial cases, the choice of design variables or parameterisation scheme used for the model to be optimized plays a vital role. Where the goal is to base the optimization on a CAD model the choices are to use a NURBS geometry generated from CAD modelling software, where the position of the NURBS control points are the optimisation variables [2] or to use the feature based CAD model with all of the construction history to preserve the design intent [3]. The main advantage of using the feature based model is that the optimized model produced can be directly used for the downstream applications including manufacturing and process planning.
This paper presents an approach for optimization based on the feature based CAD model, which uses CAD parameters defining the features in the model geometry as the design variables. In order to capture the CAD surface movement with respect to the change in design variable, the “Parametric Design Velocity” is calculated, which is defined as the movement of the CAD model boundary in the normal direction due to a change in the parameter value.
The approach presented here for calculating the design velocities represents an advancement in terms of capability and robustness of that described by Robinson et al. [3]. The process can be easily integrated to most industrial optimisation workflows and is immune to the topology and labelling issues highlighted by other CAD based optimisation processes. It considers every continuous (“real value”) parameter type as an optimisation variable, and it can be adapted to work with any CAD modelling software, as long as it has an API which provides access to the values of the parameters which control the model shape and allows the model geometry to be exported. To calculate the movement of the boundary the methodology employs finite differences on the shape of the 3D CAD models before and after the parameter perturbation. The implementation procedure includes calculating the geometrical movement along a normal direction between two discrete representations of the original and perturbed geometry respectively. Parametric design velocities can then be directly linked with adjoint surface sensitivities to extract the gradients to use in a gradient-based optimization algorithm.
The optimisation of a flow optimisation problem is presented, in which the power dissipation of the flow in an automotive air duct is to be reduced by changing the parameters of the CAD geometry created in CATIA V5. The flow sensitivities are computed with the continuous adjoint method for a laminar and turbulent flow [4] and are combined with the parametric design velocities to compute the cost function gradients. A line-search algorithm is then used to update the design variables and proceed further with optimisation process.

The Pair-Bond Formation and Its Role in the Stimulation of Reproductive Function in Female Common Marmosets (Callithrix jacchus)

SILVA, H.P.A.; SOUSA, M.B.C. The pair-bond formation and its role in the stimulation of reproductive function in female common marmosets (collithrix Jacchus). International Journal of Primatology, v, 18, n.3, p.387-400, 1997.

Measuring function and mobility among clients with diabetes in Samoa

The aim of the thesis was to collect baseline data and to investigating suitable physical tests and a self-rapport questionnaire. Collected data was used to find a routine measurement when investigating foot health, function and mobility among clients suffering from diabetes in Samoa. Twenty-one participants suffering from diabetes were included in the study. Clients answered the Foot function index (FFI) questionnaire and performed physical tests, consisting of Bergs balance scale (BBS) and Time up and go (TUG). Results from the physical tests revealed a great balance disturbance and mobility limitations among the majority of the clients. General high weight and BMI was measured among both genders. Subjects with the highest BMI performed lowest time during TUG test. The statistic analyze revealed a strong correlation between the two physical tests, indicating that one of the tests could be applied as a routine measurement in the future, when evaluating function and mobility in Samoa. The compilation of self-report questionnaires indicated a general good foot health with a low amount of pain, disabilities and activity limitations.

The Pair-Bond Formation and Its Role in the Stimulation of Reproductive Function in Female Common Marmosets (Callithrix jacchus)

SILVA, H.P.A.; SOUSA, M.B.C. The pair-bond formation and its role in the stimulation of reproductive function in female common marmosets (collithrix Jacchus). International Journal of Primatology, v, 18, n.3, p.387-400, 1997.

CAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.

Causal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.