Caractérisation de deux récepteurs du fer d'Actinobacillus pleuropneumoniae (FhuA et HgbA) ainsi que leur utilisation dans un vaccin sous-unitaire

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

The 2016 Revision of ISO 1 – Standard Reference Temperature for the Specification of Geometrical and Dimensional Properties

This paper discusses the changes in the 2016 (third edition) of International Standard ISO 1. While the value of the standard reference temperature remains unchanged at 20 °C, the important definitions for the “reference temperature” and “standard reference temperature,” absent in prior editions, are now defined, with the latter exclusively reserved for the assignment of the internationally agreed upon temperature of 20 °C. The scope of the revised Standard has been carefully refined and made more explicit. This, together with other clarifications and improvements, has eliminated the ambiguities associated with specifications at non-standard reference temperatures and allows, if needed, different reference temperatures to be associated with different properties of a workpiece. The relationship between ISO 1 and dimensional measurements is also discussed and clarified. In this paper, we discuss the motivation for these changes and present several issues debated during the revision process for the benefit of future standards committees that might study this topic

The planarity of the stickface motion in the field hockey hit

The field hockey hit is an important but poorly understood stroke. This study investigated the planarity of the stickface motion during the downswing, in order to better characterise the kinematics and to assess the suitability of planar pendulum models for simulating the hit. Thirteen experienced female field hockey players were filmed executing hits with a single approach step, and the kinematics of the centre of the stickface were measured. A method was developed for identifying how far back from impact the stickface motion was planar. Orthogonal least-squares regression was used to determine best-fit planes for sections of the stickface path of varying length, each of which ended at impact, and these sections were considered planar if the mean residual between the stickface path and the fitted plane was less than 0.25% of the distance traveled by the stickface during that period. On average the stickface motion was planar for the last 83±12% of its downswing path, with the length of the planar section ranging from 1.85 m to 2.70 m. The suitability of a planar model for the stickface motion was supported, but further investigation of the stick and arm kinematics is warranted

The planarity of the stickface motion in the field hockey hit

The field hockey hit is an important but poorly understood stroke. This study investigated the planarity of the stickface motion during the downswing, in order to better characterise the kinematics and to assess the suitability of planar pendulum models for simulating the hit. Thirteen experienced female field hockey players were filmed executing hits with a single approach step, and the kinematics of the centre of the stickface were measured. A method was developed for identifying how far back from impact the stickface motion was planar. Orthogonal least-squares regression was used to determine best-fit planes for sections of the stickface path of varying length, each of which ended at impact, and these sections were considered planar if the mean residual between the stickface path and the fitted plane was less than 0.25% of the distance traveled by the stickface during that period. On average the stickface motion was planar for the last 83±12% of its downswing path, with the length of the planar section ranging from 1.85 m to 2.70 m. The suitability of a planar model for the stickface motion was supported, but further investigation of the stick and arm kinematics is warranted

Curvature-Based Environment Description for Robot Navigation Using Laser Range Sensors

This work proposes a new feature detection and description approach for mobile robot navigation using 2D laser range sensors. The whole process consists of two main modules: a sensor data segmentation module and a feature detection and characterization module. The segmentation module is divided in two consecutive stages: First, the segmentation stage divides the laser scan into clusters of consecutive range readings using a distance-based criterion. Then, the second stage estimates the curvature function associated to each cluster and uses it to split it into a set of straight-line and curve segments. The curvature is calculated using a triangle-area representation where, contrary to previous approaches, the triangle side lengths at each range reading are adapted to the local variations of the laser scan, removing noise without missing relevant points. This representation remains unchanged in translation or rotation, and it is also robust against noise. Thus, it is able to provide the same segmentation results although the scene will be perceived from different viewpoints. Therefore, segmentation results are used to characterize the environment using line and curve segments, real and virtual corners and edges. Real scan data collected from different environments by using different platforms are used in the experiments in order to evaluate the proposed environment description algorithm

Modelagem tridimensional de superfícies planas a partir de imagem TOF

Neste artigo é apresentado um estudo para a modelagem tridimensional de superfícies utilizando imagens obtidas com uma câmara TOF. O sistema utilizado é a câmara PMD CamCube 2.0, que calcula a distância de objetos utilizando o princípio TOF (Time-Of-Flight). Inicialmente, a câmara foi calibrada, o que possibilitou a correção geométrica da imagem a partir de Parâmetros de Orientação Interior (POI). Outro fator que afeta a qualidade dos dados é a presença de ruído em função da iluminação exterior ao sistema, que pode ser controlada variando o tempo de integração na captação da imagem. Posteriormente realizou-se a segmentação da imagem TOF para identificar as superfícies planas do objeto estudado. A solução encontrada utiliza o cálculo dos autovalores locais em superfícies, em pequenas janelas móveis. Com isto, gera-se uma nova imagem com bordas evidenciadas. A nova imagem é segmentada através do algoritmo de deslocamento pela média, com o objetivo de obter superfícies planas do objeto de interesse. Os píxeis da imagem segmentada são então projetados ortogonalmente, gerando uma nuvem de pontos com coordenadas tridimensionais das superfícies. Finalmente, as dimensões do objeto são comparadas com medidas de referência, sendo verificado que a diferença na medida tridimensional obtida é de aproximadamente 2 centímetros

Virulence factors of Actinobacillus pleuropneumoniae involved in colonization, persistence and induction of lesions in its porcine host

Actinobacillus pleuropneumoniae is the causative agent of porcine pleuropneumonia. The virulence factors of this microorganism involved in colonization and the induction of lung lesions have been thoroughly studied and some have been well characterized. A. pleuropneumoniae binds preferentially to cells of the lower respiratory tract in a process involving different adhesins and probably biofilm formation. Apx toxins and lipopolysaccharides exert pathogenic effects on several host cells, resulting in typical lung lesions. Lysis of host cells is essential for the bacterium to obtain nutrients from the environment and A. pleuropneumoniae has developed several uptake mechanisms for these nutrients. In addition to persistence in lung lesions, colonization of the upper respiratory tract – and of the tonsils in particular – may also be important for long-term persistent asymptomatic infection. Information on virulence factors involved in tonsillar and nasal cavity colonization and persistence is scarce, but it can be speculated that similar features as demonstrated for the lung may play a role

The Cell Tracking Challenge: 10 years of objective benchmarking

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a signifcant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.Web of Science2071020101