Métrologie des actinides basée sur l’analyse des matières fécales pour des applications dosimétriques

Les techniques dosimétriques externes, telles que les dosimètres portatifs et compteurs Geiger-Müller, sont largement utilisées dans la détection et l’interprétation de dose reçue par les employés du secteur nucléaire. Toutefois, ces techniques ne permettent pas de caractériser une contamination interne, telle que celle causée par des émetteurs alpha. Actuellement au Canada, les techniques de dosimétrie biologique sont orientées sur l’analyse d’urine, de frottis nasaux et, dans de rares cas, de sang, de tissus et exceptionnellement de matières fécales. Les matières fécales, quant à elles, procurent une voie différente et complémentaire aux autres types de prélèvement, en raison de l’interaction entre le système respiratoire et le système digestif. Il n’existe aucune procédure officielle au Canada sur ce genre d’échantillon. Une nouvelle méthodologie, basée sur la fusion boratée couplée à une méthode d’extraction, a été développée dans le cours de ce mémoire, ce qui devrait permettre de résoudre ce problème.External dosimetric techniques, such as portable dosimeter and Geiger-Müller counter, are largely use in the detection and interpretation of received dose by employees of the nuclear sector. However, those techniques are inefficient for characterising internal contamination, such as alpha emitters. As for the moment, the techniques used in Canada for bioassay are oriented towards urine, nasal swabs sampling and on rare occasion; blood and tissue, exceptionally fecal samples. The fecal samples offer a different and complementary approach to other bioassays due to the interaction between the respiratory tract and the gastrointestinal tract. However, there is no official methodology for fecal analysis in Canada. For this thesis, a new methodology based on borated fusion coupled to column chromatography was developed to remedy this problem

Élaboration et validation d'une version francophone de l'Expanded Cultural Intelligence Scale

Avec l’internationalisation croissante des entreprises québécoises, de plus en plus d’organisations cherchent à recruter et former des employés capables d’interagir efficacement avec des individus issus d’une diversité de contextes ethnoculturels. Or, aucun outil de mesure francophone, validé empiriquement au Québec, n’est disponible pour évaluer cette compétence. Cette étude avait pour objectif de traduire et de faire la validation transculturelle de l’Expanded Cultural Intelligence Scale, un outil de mesure de l’intelligence culturelle permettant d’identifier des individus efficaces dans leurs contacts interculturels. L’outil a été traduit grâce à la méthode de traduction inversée, méthode qui a permis l’élaboration d’une version expérimentale qui entretenait une bonne équivalence sémantique, expérientielle et conceptuelle avec l’outil original. La version expérimentale a tout d’abord été soumise, en même temps que la version originale, à un échantillon de 39 personnes bilingues. Des coefficients de corrélations élevés ont permis de confirmer la validité concomitante et de contenu de la version expérimentale. Par la suite, la version expérimentale a été soumise, à deux reprises, à un échantillon d’étudiants issus du CÉGEP de Victoriaville et de l’Université de Sherbrooke. 679 étudiants ont répondu au questionnaire lors de la première administration et 531 ont répondu à celui-ci lors de la deuxième administration. Les coefficients de corrélation test-retest confirment une stabilité temporelle acceptable de la version de l’E-CQS. Les alphas ainsi que la structure factorielle de l’outil ont été testés au Temps 1 et au Temps 2. Dans l’ensemble, les résultats suggèrent que la version francophone de l’E-CQS produit dans la présente recherche est un outil de mesure prometteur de la compétence interculturelle

A wireless sEMG-based body-machine interface for assistive technology devices

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO

Transformational Leadership and Incivility: A Multilevel and Longitudinal Test

This research examines group-level perceptions of transformational leadership (TFL) as negative longitudinal predictors of witnessing person-related (e.g., insults/affronts) and work-related (e.g., negation/intentional work overload) acts of incivility at work. Witnessing workplace incivility was also postulated to negatively predict employee need satisfaction. Data were collected among production employees in different Canadian plants of a major manufacturing company (N = 344) who worked for 42 different managers (Mgroup size= 9.76). Two waves of data collection occurred 1 year apart. Results from multilevel analyses showed that workgroups where managers were perceived to engage in more frequent TFL behaviors reported reduced levels of person- and work-related incivility 1 year later. However, group-level incivility did not predict change in group-level need satisfaction 1 year later. At the individual level, results showed that witnessing higher levels of person-related incivility than one’s colleagues predicted reduced satisfaction of the need for relatedness 1 year later. These longitudinal findings build upon previous literature by identifying TFL as a potential managerial strategy to reduce incivility in workgroups over time. They also show that mere exposure to workplace misbehavior still affects employees’ adjustment, suggesting that every effort to reduce deviance in workplaces is worthwhile

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions

Deep learning of chest X‑rays can predict mechanical ventilation outcome in ICU‑admitted COVID‑19 patients

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model

A longitudinal analysis of motivation profiles at work

This paper examines the multidimensional nature of workplace motivation and the importance of a continuum structure in self-determination theory through application of complementary variable- and person-centered approaches. This approach is taken to simultaneously model the complexity of motivation and highlight interactions between motivational factors. Additionally, this study represents an initial test of the temporal stability of work motivation profiles. A sample of 510 full-time employees were recruited from a range of occupations. Results support the central importance of a general factor representing self-determination as the most influential factor in an employee’s motivation profile. However, smaller effects associated with the motivation subscales, especially identified regulation, were also noticed. Importantly, motivation profiles were found to be highly stable over the 4-month duration of this study. Results lend support to the theoretical position that while general self-determination is an essential component of motivation, it alone does not fully describe an employee’s motivation

Élaboration et validation d'une version francophone de l'Expanded Cultural Intelligence Scale

Avec l’internationalisation croissante des entreprises québécoises, de plus en plus d’organisations cherchent à recruter et former des employés capables d’interagir efficacement avec des individus issus d’une diversité de contextes ethnoculturels. Or, aucun outil de mesure francophone, validé empiriquement au Québec, n’est disponible pour évaluer cette compétence. Cette étude avait pour objectif de traduire et de faire la validation transculturelle de l’Expanded Cultural Intelligence Scale, un outil de mesure de l’intelligence culturelle permettant d’identifier des individus efficaces dans leurs contacts interculturels. L’outil a été traduit grâce à la méthode de traduction inversée, méthode qui a permis l’élaboration d’une version expérimentale qui entretenait une bonne équivalence sémantique, expérientielle et conceptuelle avec l’outil original. La version expérimentale a tout d’abord été soumise, en même temps que la version originale, à un échantillon de 39 personnes bilingues. Des coefficients de corrélations élevés ont permis de confirmer la validité concomitante et de contenu de la version expérimentale. Par la suite, la version expérimentale a été soumise, à deux reprises, à un échantillon d’étudiants issus du CÉGEP de Victoriaville et de l’Université de Sherbrooke. 679 étudiants ont répondu au questionnaire lors de la première administration et 531 ont répondu à celui-ci lors de la deuxième administration. Les coefficients de corrélation test-retest confirment une stabilité temporelle acceptable de la version de l’E-CQS. Les alphas ainsi que la structure factorielle de l’outil ont été testés au Temps 1 et au Temps 2. Dans l’ensemble, les résultats suggèrent que la version francophone de l’E-CQS produit dans la présente recherche est un outil de mesure prometteur de la compétence interculturelle