Dimensional analysis of MINMOD leads to definition of the disposition index of glucose regulation and improved simulation algorithm

BACKGROUND: Frequently Sampled Intravenous Glucose Tolerance Test (FSIVGTT) together with its mathematical model, the minimal model (MINMOD), have become important clinical tools to evaluate the metabolic control of glucose in humans. Dimensional analysis of the model is up to now not available. METHODS: A formal dimensional analysis of MINMOD was carried out and the degree of freedom of MINMOD was examined. Through re-expressing all state variable and parameters in terms of their reference scales, MINMOD was transformed into a dimensionless format. Previously defined physiological indices including insulin sensitivity, glucose effectiveness, and first and second phase insulin responses were re-examined in this new formulation. Further, the parameter estimation from FSIVGTT was implemented using both the dimensional and the dimensionless formulations of MINMOD, and the performances were compared utilizing Monte Carlo simulation as well as real human FSIVGTT data. RESULTS: The degree of freedom (DOF) of MINMOD was found to be 7. The model was maximally simplified in the dimensionless formulation that normalizes the variation in glucose and insulin during FSIVGTT. In the new formulation, the disposition index (Dl), a composite parameter known to be important in diabetes pathology, was naturally defined as one of the dimensionless parameters in the system. The numerical simulation using the dimensionless formulation led to a 1.5–5 fold gain in speed, and significantly improved accuracy and robustness in parameter estimation compared to the dimensional implementation. CONCLUSION: Dimensional analysis of MINMOD led to simplification of the model, direct identification of the important composite factors in the dynamics of glucose metabolic control, and better simulations algorithms

A Vector Fitting Approach for the Automated Estimation of Lumped Boundary Conditions of 1D Circulation Models

Purpose: The choice of appropriate boundary conditions is a crucial step in the development of cardiovascular models for blood flow simulations. The three-element Windkessel model is usually employed as a lumped boundary condition, providing a reduced order representation of the peripheral circulation. However, the systematic estimation of the Windkessel parameters remains an open problem. Moreover, the Windkessel model is not always adequate to model blood flow dynamics, which often require more elaborate boundary conditions. In this study, we propose a method for the estimation of the parameters of high order boundary conditions, including the Windkessel model, from pressure and flow rate waveforms at the truncation point. Moreover, we investigate the effect of adopting higher order boundary conditions, corresponding to equivalent circuits with more than one storage element, on the accuracy of the model. Method: The proposed technique is based on Time-Domain Vector Fitting, a modeling algorithm that, given samples of the input and output of a system, such as pressure and flow waveforms, can derive a differential equation approximating their relation. Results: The capabilities of the proposed method are tested on a 1D circulation model consisting of the 55 largest human systemic arteries, to demonstrate its accuracy and its usefulness to estimate boundary conditions with order higher than the traditional Windkessel models. The proposed method is compared to other common estimation techniques, and its robustness in parameter estimation is verified in presence of noisy data and of physiological changes of aortic flow rate induced by mental stress. Conclusion: Results suggest that the proposed method is able to accurately estimate boundary conditions of arbitrary order. Higher order boundary conditions can improve the accuracy of cardiovascular simulations, and Time-Domain Vector Fitting can automatically estimate them

Sexual health and the pandemic crisis: testing the role of psychological vulnerability/protective factors on sexual functioning and sexual distress during a critical life period in Portugal

Recent findings suggest that the current COVID-19 pandemic has a potential negative impact in several areas of life, including sexual health. However, less is known about the psychological dimensions that may work as vulnerability/protective factors for the development of sexual problems in the current pandemic. The current study used a longitudinal design to examine the role played by personality trait factors (neuroticism, extraversion) as well as psychosexual factors (sexual beliefs) in predicting sexual functioning and sexual distress across time during the current pandemic crisis. A total of 528 individuals (337 women) completed a web survey assessing sexual health indicators and psychological factors. The first wave was conducted during the confinement period in Portugal (N = 528) between May and June 2020 and the second four months later (N = 146), when strict confinement rules were over. Generalized estimating equations (GEE) were used to examine the ability of psychological factors to predict sexual functioning and distress across time, while controlling for age and gender. Results indicated that sexual distress at time point 2 was lower than during confinement, and men had lower levels of sexual functioning post-confinement while no significant difference was observed for women. Moreover, higher levels of neuroticism and age-related beliefs significantly predicted lower sexual functioning as well as higher sexual distress, whereas lower levels of extraversion predicted lower sexual functioning after controlling for age and gender effects. Findings support the role of psychological vulnerability factors to predict sexual problems across time and may have important implications in the prevention and treatment of sexual dysfunctions

Kinetics of the direct DME synthesis from CO2_{2} rich syngas under variation of the CZA-to-γ-Al2_{2}O3_{3} ratio of a mixed catalyst bed

The one-step synthesis of dimethyl ether over mechanical mixtures of Cu/ZnO/Al$_{2}$O$_{3}$ (CZA) and γ-Al$_{2}$O$_{3}$ was studied in a wide range of process conditions. Experiments were performed at an industrially relevant pressure of 50 bar varying the carbon oxide ratio in the feed (CO$_{2}$ in COx from 20 to 80%), temperature (503–533 K), space-time (240–400 kg$_{cat}$s m$_{gas}$$^{-3}$), and the CZA-to-γ-Al$_{2}$O$_{3}$ weight ratio (from 1 to 5). Factors favoring the DME production in the investigated range of conditions are an elevated temperature, a low CO$_{2}$ content in the feed, and a CZA-to-γ-Al$_{2}$O$_{3}$ weight ratio of 2. A lumped kinetic model was parameterized to fit the experimental data, resulting in one of the predictive models with the broadest range of validity in the open literature for the CZA/γ-Al$_{2}$O$_{3}$ system

Gesture Recognition Using Hidden Markov Models Augmented with Active Difference Signatures

With the recent invention of depth sensors, human gesture recognition has gained significant interest in the fields of computer vision and human computer interaction. Robust gesture recognition is a difficult problem because of the spatiotemporal variations in gesture formation, subject size, subject location, image fidelity, and subject occlusion. Gesture boundary detection, or the automatic detection of the onset and offset of a gesture in a sequence of gestures, is critical toward achieving robust gesture recognition. Existing gesture recognition methods perform the task of gesture segmentation either using resting frames in a gesture sequence or by using additional information such as audio, depth images, or RGB images. This ancillary information introduces high latency in gesture segmentation and recognition, thus making it inappropriate for real time applications. This thesis proposes a novel method to recognize time-varying human gestures from continuous video streams. The proposed method passes skeleton joint information into a Hidden Markov Model augmented with active difference signatures to achieve state-of-the-art gesture segmentation and recognition. Active body parts are used to calculate the likelihood of previously unseen data to facilitate gesture segmentation. Active difference signatures are used to describe temporal motion as well as static differences from a canonical resting position. Geometric features, such as joint angles, and joint topological distances are used along with active difference signatures as salient feature descriptors. These feature descriptors serve as unique signatures which identify hidden states in a Hidden Markov Model. The Hidden Markov Model is able to identify gestures in a robust fashion which is tolerant to spatiotemporal and human-to-human variation in gesture articulation. The proposed method is evaluated on both isolated and continuous datasets. An accuracy of 80.7% is achieved on the isolated MSR3D dataset and a mean Jaccard index of 0.58 is achieved on the continuous ChaLearn dataset. Results improve upon existing gesture recognition methods, which achieve a Jaccard index of 0.43 on the ChaLearn dataset. Comprehensive experiments investigate the feature selection, parameter optimization, and algorithmic methods to help understand the contributions of the proposed method

Estimação e diagnóstico em modelos multivariados para dados censurados

Orientadores: Víctor Hugo Lachos Dávila, Luis Mauricio Castro CeperoTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: Em alguns ensaios clínicos da síndrome da imunodeficiência adquirida (AIDS), as medições dos ácidos ribonucleicos do vírus da imunodeficiência humana (HIV-1) são coletadas periodicamente ao longo do tempo e muitas vezes estão sujeitas a limites de detecção inferiores ou superiores, dependendo dos ensaios de quantificação que foram utilizados. Assim, estas respostas podem ser censuradas à esquerda ou à direita. Na prática, dados longitudinais provenientes de estudos de acompanhamento do HIV, podem ser modelados utilizando modelos lineares e não-lineares de efeitos mistos censurados e também modelos de regressão censurados com estruturas de correlação específicas sobre os erros. Uma complicação adicional surge quando duas ou mais variáveis respostas são coletadas de forma irregular e repetidamente em cada sujeito durante um certo período de tempo. Os modelos lineares multivariados de efeitos mistos com respostas censuradas são ferramentas bastante utilizadas para análise conjunta de mais de uma série de respostas de dados longitudinais. Nesta tese desenvolvemos métodos inferenciais para lidar com dados censurados com estrutura longitudinal sob uma perspectiva clássica. Como resultado, conclusões importantes foram obtidas a partir da análise dos modelos propostosAbstract: In some acquired immunodeficiency syndrome (AIDS) clinical trials, the human immunodeficiency virus-1 ribonucleic acid measurements are collected irregularly over time and are often subject to some upper and lower detection limits, depending on the quantification assays. Hence, these responses are either left- or right-censored. In practice, longitudinal data coming from those follow-up studies can be modelled using censored linear and nonlinear mixed-effects models and also censored regression models with a specific correlation structures on the error terms. A complication arises when more than one series of responses are repeatedly collected on each subject at irregularly occasions over a period of time. The multivariate censored linear mixed model is a frequently used tool for a joint analysis of more than one series of longitudinal data. In this thesis we develop a series of essays in which different models and techniques to deal with censored data are applied. As result, we had several works to carry out censored dataDoutoradoEstatisticaDoutora em Estatística2011/22063-9, 2015/05385-3FAPES

High-dimensional hierarchical models and massively parallel computing

This work expounds a computationally expedient strategy for the fully Bayesian treatment of high-dimensional hierarchical models. Most steps in a Markov chain Monte Carlo routine for such models are either conditionally independent draws or low-dimensional draws based on summary statistics of parameters at higher levels of the hierarchy. We construct both sets of steps using parallelized algorithms designed to take advantage of the immense parallel computing power of general-purpose graphics processing units while avoiding the severe memory transfer bottleneck. We apply our strategy to RNA-sequencing (RNA-seq) data analysis, a multiple-testing, low-sample-size scenario where hierarchical models provide a way to borrow information across genes. Our approach is solidly tractable, and it performs well under several metrics of estimation, posterior inference, and gene detection. Best-case-scenario empirical Bayes counterparts perform equally well, lending support to existing empirical Bayes approaches in RNA-seq. Finally, we attempt to improve the robustness of estimation and inference of our RNA-seq model using alternate hierarchical distributions