Detection and dynamics of porcine circovirus 2 shedding in semen using conventional and real-time PCR

The dynamics of porcine circovirus type 2 (PCV2) shedding in semen of naturally infected boars was studied. Semen was collected serially each 15 or 20 days during 62 days from 5 boars from a herd and from 11 boars from an artificial insemination center. All boars were positive for PCV2 DNA by nested polymerase chain reaction of raw semen in at least two sampling dates, and most of them had detectable shedding in all sampling dates. Real-time quantitative PCR was performed in 23 samples. All samples showed low amounts of PCV2 DNA, ranging from 98 to 652 PCV2 copies/mL. No differences between the frequencies of PCV2 DNA shed in semen were found considering herds and age of boars. PCV2 shedding in the semen can occur continuously or intermittently up to 60 days in naturally infected boars at 12 to 42 months old in absence of PCV2 clinical signs. These results demonstrate sporadic and long-term shedding patterns of low amounts of PCV2 DNA in semen from naturally infected boars.

A Soft Contact Collision Method For Real-Time Simulation of Triangularized Geometries in Multibody Dynamics

When modeling machines in their natural working environment collisions become a very important feature in terms of simulation accuracy. By expanding the simulation to include the operation environment, the need for a general collision model that is able to handle a wide variety of cases has become central in the development of simulation environments. With the addition of the operating environment the challenges for the collision modeling method also change. More simultaneous contacts with more objects occur in more complicated situations. This means that the real-time requirement becomes more difficult to meet. Common problems in current collision modeling methods include for example dependency on the geometry shape or mesh density, calculation need increasing exponentially in respect to the number of contacts, the lack of a proper friction model and failures due to certain configurations like closed kinematic loops. All these problems mean that the current modeling methods will fail in certain situations. A method that would not fail in any situation is not very realistic but improvements can be made over the current methods.

Real-time Image-based RGB-D Camera Motion Tracking and Environment Mapping

In this work, image based estimation methods, also known as direct methods, are studied which avoid feature extraction and matching completely. Cost functions use raw pixels as measurements and the goal is to produce precise 3D pose and structure estimates. The cost functions presented minimize the sensor error, because measurements are not transformed or modified. In photometric camera pose estimation, 3D rotation and translation parameters are estimated by minimizing a sequence of image based cost functions, which are non-linear due to perspective projection and lens distortion. In image based structure refinement, on the other hand, 3D structure is refined using a number of additional views and an image based cost metric. Image based estimation methods are particularly useful in conditions where the Lambertian assumption holds, and the 3D points have constant color despite viewing angle. The goal is to improve image based estimation methods, and to produce computationally efficient methods which can be accomodated into real-time applications. The developed image-based 3D pose and structure estimation methods are finally demonstrated in practise in indoor 3D reconstruction use, and in a live augmented reality application.

Evaluation of stunned and infarcted canine myocardium by real time myocardial contrast echocardiography

Differentiation between stunned and infarcted myocardium in the setting of acute ischemia is challenging. Real time myocardial contrast echocardiography allows the simultaneous assessment of myocardial perfusion and function. In the present study we evaluated infarcted and stunned myocardium in an experimental model using real time myocardial contrast echocardiography. Sixteen dogs underwent 180 min of coronary occlusion followed by reperfusion (infarct model) and seven other dogs were submitted to 20 min of coronary occlusion followed by reperfusion (stunned model). Wall motion abnormality and perfusional myocardial defect areas were measured by planimetry. Risk and infarct areas were determined by tissue staining. In the infarct model, the wall motion abnormality area during coronary occlusion (5.52 ± 1.14 cm²) was larger than the perfusional myocardial defect area (3.71 ± 1.45 cm²; P < 0.001). Reperfusion resulted in maintenance of wall motion abnormality (5.45 ± 1.41 cm²; P = 0.43 versus occlusion) and reduction of perfusional myocardial defect (1.51 ± 1.29 cm²; P = 0.004 versus occlusion). Infarct size determined by contrast echocardiography correlated with tissue staining (r = 0.71; P = 0.002). In the stunned model, the wall motion abnormality area was 5.49 ± 0.68 cm² during occlusion and remained 5.1 ± 0.63 cm² after reperfusion (P = 0.07). Perfusional defect area was 2.43 ± 0.79 cm² during occlusion and was reduced to 0.2 ± 0.53 cm² after reperfusion (P = 0.04). 2,3,5-Triphenyl tetrazolium chloride staining confirmed the absence of necrotic myocardium in all dogs in the stunned model. Real time myocardial contrast echocardiography is a noninvasive technique capable of distinguishing between stunned and infarcted myocardium after acute ischemia.

Novel virtual environment and real-time simulation based methods for improving life-cycle efficiency of non-road mobile machinery

Virtual environments and real-time simulators (VERS) are becoming more and more important tools in research and development (R&D) process of non-road mobile machinery (NRMM). The virtual prototyping techniques enable faster and more cost-efficient development of machines compared to use of real life prototypes. High energy efficiency has become an important topic in the world of NRMM because of environmental and economic demands. The objective of this thesis is to develop VERS based methods for research and development of NRMM. A process using VERS for assessing effects of human operators on the life-cycle efficiency of NRMM was developed. Human in the loop simulations are ran using an underground mining loader to study the developed process. The simulations were ran in the virtual environment of the Laboratory of Intelligent Machines of Lappeenranta University of Technology. A physically adequate real-time simulation model of NRMM was shown to be reliable and cost effective in testing of hardware components by the means of hardware-in-the-loop (HIL) simulations. A control interface connecting integrated electro-hydraulic energy converter (IEHEC) with virtual simulation model of log crane was developed. IEHEC consists of a hydraulic pump-motor and an integrated electrical permanent magnet synchronous motorgenerator. The results show that state of the art real-time NRMM simulators are capable to solve factors related to energy consumption and productivity of the NRMM. A significant variation between the test drivers is found. The results show that VERS can be used for assessing human effects on the life-cycle efficiency of NRMM. HIL simulation responses compared to that achieved with conventional simulation method demonstrate the advances and drawbacks of various possible interfaces between the simulator and hardware part of the system under study. Novel ideas for arranging the interface are successfully tested and compared with the more traditional one. The proposed process for assessing the effects of operators on the life-cycle efficiency will be applied for wider group of operators in the future. Driving styles of the operators can be analysed statistically from sufficient large result data. The statistical analysis can find the most life-cycle efficient driving style for the specific environment and machinery. The proposed control interface for HIL simulation need to be further studied. The robustness and the adaptation of the interface in different situations must be verified. The future work will also include studying the suitability of the IEHEC for different working machines using the proposed HIL simulation method.

Bioeffects of albumin-encapsulated microbubbles and real-time myocardial contrast echocardiography in an experimental canine model

Myocardial contrast echocardiography has been used for assessing myocardial perfusion. Some concerns regarding its safety still remain, mainly regarding the induction of microvascular alterations. We sought to determine the bioeffects of microbubbles and real-time myocardial contrast echocardiography (RTMCE) in a closed-chest canine model. Eighteen mongrel dogs were randomly assigned to two groups. Nine were submitted to continuous intravenous infusion of perfluorocarbon-exposed sonicated dextrose albumin (PESDA) plus continuous imaging using power pulse inversion RTMCE for 180 min, associated with manually deflagrated high-mechanical index impulses. The control group consisted of 3 dogs submitted to continuous imaging using RTMCE without PESDA, 3 dogs received PESDA alone, and 3 dogs were sham-operated. Hemodynamics and cardiac rhythm were monitored continuously. Histological analysis was performed on cardiac and pulmonary tissues. No hemodynamic changes or cardiac arrhythmias were observed in any group. Normal left ventricular ejection fraction and myocardial perfusion were maintained throughout the protocol. Frequency of mild and focal microhemorrhage areas in myocardial and pulmonary tissue was similar in PESDA plus RTMCE and control groups. The percentages of positive microscopical fields in the myocardium were 0.4 and 0.7% (P = NS) in the PESDA plus RTMCE and control groups, respectively, and in the lungs they were 2.1 and 1.1%, respectively (P = NS). In this canine model, myocardial perfusion imaging obtained with PESDA and RTMCE was safe, with no alteration in cardiac rhythm or left ventricular function. Mild and focal myocardial and pulmonary microhemorrhages were observed in both groups, and may be attributed to surgical tissue manipulation.

Simultaneous detection of the C282Y, H63D and S65C mutations in the hemochromatosis gene using quenched-FRET real-time PCR

Hereditary hemochromatosis (HH) is a common autosomal disorder of iron metabolism mainly affecting Caucasian populations. Three recurrent disease-associated mutations have been detected in the hemochromatosis gene (HFE): C282Y, H63D, and S65C. Although HH phenotype has been associated with all three mutations, C282Y is considered the most relevant mutation responsible for hemochromatosis. Clinical complications of HH include cirrhosis of the liver, congestive cardiac failure and cardiac arrhythmias, endocrine pancreatic disease, which can be prevented by early diagnosis and treatment. Therefore, a reliable genotyping method is required for presymptomatic diagnosis. We describe the simultaneous detection of the C282Y, H63D and S65C mutations in the hemochromatosis gene by real-time PCR followed by melting curve analysis using fluorescence resonance energy transfer (FRET) probes. The acceptor fluorophore may be replaced by a quencher, increasing multiplex possibilities. Real-time PCR results were compared to the results of sequencing and conventional PCR followed by restriction digestion and detection by agarose gel electrophoresis (PCR-RFLP). Genotypes from 80 individuals obtained both by the conventional PCR-RFLP method and quenched-FRET real-time PCR were in full agreement. Sequencing also confirmed the results obtained by the new method, which proved to be an accurate, rapid and cost-effective diagnostic assay. Our findings demonstrate the usefulness of real-time PCR for the simultaneous detection of mutations in the HFE gene, which allows a reduction of a significant amount of time in sample processing compared to the PCR-RFLP method, eliminates the use of toxic reagents, reduces the risk of contamination in the laboratory, and enables full process automation.

Detection and quantification of Roundup Ready® soybean residues in sausage samples by conventional and real-time PCR

The increasing presence of products derived from genetically modified (GM) plants in human and animal diets has led to the development of detection methods to distinguish biotechnology-derived foods from conventional ones. The conventional and real-time PCR have been used, respectively, to detect and quantify GM residues in highly processed foods. DNA extraction is a critical step during the analysis process. Some factors such as DNA degradation, matrix effects, and the presence of PCR inhibitors imply that a detection or quantification limit, established for a given method, is restricted to a matrix used during validation and cannot be projected to any other matrix outside the scope of the method. In Brazil, sausage samples were the main class of processed products in which Roundup Ready® (RR) soybean residues were detected. Thus, the validation of methodologies for the detection and quantification of those residues is absolutely necessary. Sausage samples were submitted to two different methods of DNA extraction: modified Wizard and the CTAB method. The yield and quality were compared for both methods. DNA samples were analyzed by conventional and real-time PCR for the detection and quantification of Roundup Ready® soybean in the samples. At least 200 ng of total sausage DNA was necessary for a reliable quantification. Reactions containing DNA amounts below this value led to large variations on the expected GM percentage value. In conventional PCR, the detection limit varied from 1.0 to 500 ng, depending on the GM soybean content in the sample. The precision, performance, and linearity were relatively high indicating that the method used for analysis was satisfactory.

Real-time simulation of multibody systems with applications for working mobile vehicles

This dissertation describes an approach for developing a real-time simulation for working mobile vehicles based on multibody modeling. The use of multibody modeling allows comprehensive description of the constrained motion of the mechanical systems involved and permits real-time solving of the equations of motion. By carefully selecting the multibody formulation method to be used, it is possible to increase the accuracy of the multibody model while at the same time solving equations of motion in real-time. In this study, a multibody procedure based on semi-recursive and augmented Lagrangian methods for real-time dynamic simulation application is studied in detail. In the semirecursive approach, a velocity transformation matrix is introduced to describe the dependent coordinates into relative (joint) coordinates, which reduces the size of the generalized coordinates. The augmented Lagrangian method is based on usage of global coordinates and, in that method, constraints are accounted using an iterative process. A multibody system can be modelled as either rigid or flexible bodies. When using flexible bodies, the system can be described using a floating frame of reference formulation. In this method, the deformation mode needed can be obtained from the finite element model. As the finite element model typically involves large number of degrees of freedom, reduced number of deformation modes can be obtained by employing model order reduction method such as Guyan reduction, Craig-Bampton method and Krylov subspace as shown in this study The constrained motion of the working mobile vehicles is actuated by the force from the hydraulic actuator. In this study, the hydraulic system is modeled using lumped fluid theory, in which the hydraulic circuit is divided into volumes. In this approach, the pressure wave propagation in the hoses and pipes is neglected. The contact modeling is divided into two stages: contact detection and contact response. Contact detection determines when and where the contact occurs, and contact response provides the force acting at the collision point. The friction between tire and ground is modelled using the LuGre friction model, which describes the frictional force between two surfaces. Typically, the equations of motion are solved in the full matrices format, where the sparsity of the matrices is not considered. Increasing the number of bodies and constraint equations leads to the system matrices becoming large and sparse in structure. To increase the computational efficiency, a technique for solution of sparse matrices is proposed in this dissertation and its implementation demonstrated. To assess the computing efficiency, augmented Lagrangian and semi-recursive methods are implemented employing a sparse matrix technique. From the numerical example, the results show that the proposed approach is applicable and produced appropriate results within the real-time period.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

New statistical methods to assess the effect of time-dependent exposures in case-control studies

Contexte. Les études cas-témoins sont très fréquemment utilisées par les épidémiologistes pour évaluer l’impact de certaines expositions sur une maladie particulière. Ces expositions peuvent être représentées par plusieurs variables dépendant du temps, et de nouvelles méthodes sont nécessaires pour estimer de manière précise leurs effets. En effet, la régression logistique qui est la méthode conventionnelle pour analyser les données cas-témoins ne tient pas directement compte des changements de valeurs des covariables au cours du temps. Par opposition, les méthodes d’analyse des données de survie telles que le modèle de Cox à risques instantanés proportionnels peuvent directement incorporer des covariables dépendant du temps représentant les histoires individuelles d’exposition. Cependant, cela nécessite de manipuler les ensembles de sujets à risque avec précaution à cause du sur-échantillonnage des cas, en comparaison avec les témoins, dans les études cas-témoins. Comme montré dans une étude de simulation précédente, la définition optimale des ensembles de sujets à risque pour l’analyse des données cas-témoins reste encore à être élucidée, et à être étudiée dans le cas des variables dépendant du temps. Objectif: L’objectif général est de proposer et d’étudier de nouvelles versions du modèle de Cox pour estimer l’impact d’expositions variant dans le temps dans les études cas-témoins, et de les appliquer à des données réelles cas-témoins sur le cancer du poumon et le tabac. Méthodes. J’ai identifié de nouvelles définitions d’ensemble de sujets à risque, potentiellement optimales (le Weighted Cox model and le Simple weighted Cox model), dans lesquelles différentes pondérations ont été affectées aux cas et aux témoins, afin de refléter les proportions de cas et de non cas dans la population source. Les propriétés des estimateurs des effets d’exposition ont été étudiées par simulation. Différents aspects d’exposition ont été générés (intensité, durée, valeur cumulée d’exposition). Les données cas-témoins générées ont été ensuite analysées avec différentes versions du modèle de Cox, incluant les définitions anciennes et nouvelles des ensembles de sujets à risque, ainsi qu’avec la régression logistique conventionnelle, à des fins de comparaison. Les différents modèles de régression ont ensuite été appliqués sur des données réelles cas-témoins sur le cancer du poumon. Les estimations des effets de différentes variables de tabac, obtenues avec les différentes méthodes, ont été comparées entre elles, et comparées aux résultats des simulations. Résultats. Les résultats des simulations montrent que les estimations des nouveaux modèles de Cox pondérés proposés, surtout celles du Weighted Cox model, sont bien moins biaisées que les estimations des modèles de Cox existants qui incluent ou excluent simplement les futurs cas de chaque ensemble de sujets à risque. De plus, les estimations du Weighted Cox model étaient légèrement, mais systématiquement, moins biaisées que celles de la régression logistique. L’application aux données réelles montre de plus grandes différences entre les estimations de la régression logistique et des modèles de Cox pondérés, pour quelques variables de tabac dépendant du temps. Conclusions. Les résultats suggèrent que le nouveau modèle de Cox pondéré propose pourrait être une alternative intéressante au modèle de régression logistique, pour estimer les effets d’expositions dépendant du temps dans les études cas-témoins

Development and use of a multiplex real-time quantitative polymerase chain reaction assay for detection and differentiation of Porcine circovirus-2 genotypes 2a and 2b in an epidemiological survey.

By the end of 2004, the Canadian swine population had experienced a severe 2 increase in the incidence of Porcine circovirus-associated disease (PCVAD), a problem that was 3 associated with the emergence of a new Porcine circovirus-2 genotype (PCV-2b), previously 4 unrecovered in North America. Thus it became important to develop a diagnostic tool that could 5 differentiate between the old and new circulating genotypes (PCV-2a and -2b, respectively). 6 Consequently, a multiplex real-time quantitative polymerase chain reaction (mrtqPCR) assay that 7 could sensitively and specifically identify and differentiate PCV-2 genotypes was developed. A 8 retrospective epidemiological survey that used the mrtqPCR assay was performed to determine if 9 cofactors could affect the risk of PCVAD. From 121 PCV-2–positive cases gathered for this 10 study, 4.13%, 92.56% and 3.31% were positive for PCV-2a, PCV-2b, and both genotypes, 11 respectively. In a data analysis using univariate logistic regressions, PCVAD compatible 12 (PCVAD/c) score was significantly associated with the presence of Porcine reproductive and 13 respiratory syndrome virus (PRRSV), PRRSV viral load, PCV-2 viral load, and PCV-2 14 immunohistochemistry (IHC) results. Polytomous logistic regression analysis revealed that 15 PCVAD/c score was affected by PCV-2 viral load (P = 0.0161) and IHC (P = 0.0128), but not by 16 the PRRSV variables (P > 0.9); suggesting that mrtqPCR in tissue is a reliable alternative to IHC. 17 Logistic regression analyses revealed that PCV-2 increased the odds ratio of isolating 2 major 18 swine pathogens of the respiratory tract, Actinobacillus pleuropneumoniae and Streptococcus 19 suis serotypes 1/2, 1, 2, 3, 4, and 7, which are serotypes commonly associated with clinical 20 diseases.

Learning-Based Approach to Real Time Tracking and Analysis of Faces

This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature. The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an overcomplete dictionary of Haar wavelets.