Identification of acoustic parameters for broiler welfare estimate

Estimates of broiler welfare have subjective character. Nowadays, researchers seek non-invasive features or indicators that may describe this condition in animal production. The aim of this study was to identify acoustic parameters to estimate broiler welfare using the following five vocalization acoustic parameters: energy, spectral centroid, bandwidth, first formant, and second formant. The database that generated the model was obtained from a field experiment with 432 broilers, which half were Cobb® and half, Ross® breed, from day 21 to 42, containing bird vocalizations under either welfare or stress conditions. The results of the experiment generated responses to the tested conditions of gender, genetic strain, and welfare. The proposed model was based on the specific response of mean weights for each situation of stress and well-being. From the results, a model was developed to estimate the welfare condition of broilers from the registered information linked to their vocalization.

McFarland nephelometer as a simple method to estimate the sensitivity of the polymerase chain reaction using Mycobacterium tuberculosis as a research tool

Polymerase chain reaction (PCR) has been widely investigated for the diagnosis of tuberculosis. However, before this technique is applied on clinical samples, it needs to be well standardized. We describe the use of McFarland nephelometer, a very simple approach to determine microorganism concentration in solution, for PCR standardization and DNA quantitation, using Mycobacterium tuberculosis as a model. Tuberculosis is an extremely important disease for the public health system in developing countries and, with the advent of AIDS, it has also become an important public health problem in developed countries. Using Mycobacterium tuberculosis as a research model, we were able to detect 3 M. tuberculosis genomes using the McFarland nephelometer to assess micobacterial concentration. We have shown here that McFarland nephelometer is an easy and reliable procedure to determine PCR sensitivity at lower costs.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Inhibition of the sarcoplasmic reticulum Ca2+ pump with thapsigargin to estimate the contribution of Na+-Ca2+ exchange to ventricular myocyte relaxation

Relaxation in the mammalian ventricle is initiated by Ca2+ removal from the cytosol, which is performed by three main transport systems: sarcoplasmic reticulum Ca2+-ATPase (SR-A), Na+-Ca2+ exchanger (NCX) and the so-called slow mechanisms (sarcolemmal Ca2+-ATPase and mitochondrial Ca2+ uptake). To estimate the relative contribution of each system to twitch relaxation, SR Ca2+ accumulation must be selectively inhibited, usually by the application of high caffeine concentrations. However, caffeine has been reported to often cause changes in membrane potential due to NCX-generated inward current, which compromises the reliability of its use. In the present study, we estimated integrated Ca2+ fluxes carried by SR-A, NCX and slow mechanisms during twitch relaxation, and compared the results when using caffeine application (Cf-NT) and an electrically evoked twitch after inhibition of SR-A with thapsigargin (TG-TW). Ca2+ transients were measured in 20 isolated adult rat ventricular myocytes with indo-1. For transients in which one or more transporters were inhibited, Ca2+ fluxes were estimated from the measured free Ca2+ concentration and myocardial Ca2+ buffering characteristics. NCX-mediated integrated Ca2+ flux was significantly higher with TG-TW than with Cf-NT (12 vs 7 µM), whereas SR-dependent flux was lower with TG-TW (77 vs 81 µM). The relative participations of NCX (12.5 vs 8% with TG-TW and Cf-NT, respectively) and SR-A (85 vs 89.5% with TG-TW and Cf-NT, respectively) in total relaxation-associated Ca2+ flux were also significantly different. We thus propose TG-TW as a reliable alternative to estimate NCX contribution to twitch relaxation in this kind of analysis.

Estimate of respiration rate and physicochemical changes of fresh-cut apples stored under different temperatures

In this study, the influence of storage temperature and passive modified packaging (PMP) on the respiration rate and physicochemical properties of fresh-cut Gala apples (Malus domestica B.) was investigated. The samples were packed in flexible multilayer bags and stored at 2 °C, 5 °C, and 7 °C for eleven days. Respiration rate as a function of CO2 and O2 concentrations was determined using gas chromatography. The inhibition parameters were estimated using a mathematical model based on Michaelis-Menten equation. The following physicochemical properties were evaluated: total soluble solids, pH, titratable acidity, and reducing sugars. At 2 °C, the maximum respiration rate was observed after 150 hours. At 5 °C and 7 °C the maximum respiration rates were observed after 100 and 50 hours of storage, respectively. The inhibition model results obtained showed a clear effect of CO2 on O2 consumption. The soluble solids decreased, although not significantly, during storage at the three temperatures studied. Reducing sugars and titratable acidity decreased during storage and the pH increased. These results indicate that the respiration rate influenced the physicochemical properties.

Estimate of dietary intake of chloropropanols (3-MCPD and 1,3-DCP) and health risk assessment

Chloropropanols, including 3-monochloropropane-1,2-diol (3-MCPD) and 1,3-dichloropropan-2-ol (1,3-DCP), comprise a group of chemical contaminants with carcinogenic and genotoxic properties. They have been found in a variety of processed foods and food ingredients, such as hydrolyzed vegetable protein, soy sauce, cereal-based products, malt-derived ingredients, and smoked foods. This study aimed to assess the dietary exposure to 3-MCPD and 1,3-DCP in Brazil and verify whether the presence of these substances in foods could represent health risks. The intake was calculated by combining data on food consumption, provided by the Consumer Expenditure Survey 2008-2009, with the levels of contaminant occurrence determined by gas chromatography-mass spectrometry. The exposure to 3-MCPD ranged from 0.06 to 0.51 µg.kg bw-1.day-1 considering average and high consumers, while the intake of 1,3-DCP was estimated to be 0.0036 µg.kg bw-1.day-1 in the worst case scenario evaluated. Based on these results, it was verified that the Brazilians' exposure to chloropropanols does not present a significant health risk. However, the consumption of specific foods containing high levels of 3-MCPD could exceed the provisional maximum tolerable daily intake of 2 µg.kg bw-1 established for this compound and, therefore, represent a potential concern.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Original estimate of Mr. Danforth on the Port Dalhousie and Thorold Railway

Original estimate of Mr. Danforth on the Port Dalhousie and Thorold Railway (1 page, handwritten), Nov. 3, 1853.

Approximate cost of completing the railway from Port Dalhousie to St. Catharines and an estimate of the cost of the piers at Port Dalhousie signed by William Hamilton Merritt

Approximate cost of completing the railway from Port Dalhousie to St. Catharines and an estimate of the cost of the piers at Port Dalhousie signed by William Hamilton Merritt (5 pages, handwritten), July 8, 1854.

Approximate estimate of the cost of completing the Port Dalhousie Railway to the Grand Central Railway Station at Lock 12

Approximate estimate of the cost of completing the Port Dalhousie Railway to the Grand Central Railway Station at Lock 12. This document is badly torn and burned but most of the text is legible, July 14, 1854.

Port Dalhousie and Thorold Railway estimate of work done to date with an approximation of probable damage sustained by suspending the track

Port Dalhousie and Thorold Railway estimate of work done to date with an approximation of probable damage sustained by suspending the track, Aug. 22, 1854.

Estimate of work done on the Port Dalhousie and Thorold Railway by Messrs. Brown and McDonell

Estimate of work done on the Port Dalhousie and Thorold Railway by Messrs. Brown and McDonell, contractors, on sections 1, 2, and 3 ending at St. Catharines during the month ending Aug. 31, 1854.

Estimate of work done on the Port Dalhousie and Thorold Railway by Messrs. Brown and McDonell

Estimate of work done on the Port Dalhousie and Thorold Railway by Messrs. Brown and McDonell, contractors, on sections 1, 2, and 3 ending at St. Catharines during the month of August, 1854, signed by S.D. Woodruff, Sept. 1854.