Background. Within the South African context there is a large body of research regarding the associations between maternal gestational drinking and diagnosable child FASDs. However, there remains a paucity of local research regarding the impacts of other kinds of maternal drinking behaviours (e.g. past and present maternal drinking) and related socio-demographic factors on developmentally sensitive areas of child neurocognitive functioning, such as executive functioning (EF). Methods. This study was cross-sectional in design, utilising a gender balanced sample of N=464 children between the ages of 9.00 and 15.12 (year.months) in three rural areas within the Western Cape. Information regarding maternal drinking behaviours (before, during and after pregnancy) and related socio-demographic factors was collected via structured interviews with mothers or proxy respondents. Six subtests from the Cambridge Automated Neuropsychological Battery (CANTAB), were used to assess three aspects of child EF namely: (1) processing speed, assessed by the MOT and RTI subtests, (2) attention, assessed by the MTT and RVP subtests and (3) memory, assessed by the SWM and PAL subtests. Findings. For all three maternal alcohol use behaviours examined, there was an apparent non-significant trend whereby children of mothers who reported alcohol use (before, during and after pregnancy) performed worse (on average) than children of mothers reporting non-alcohol use on the EF subtests. Several of the socio-demographic factors were found to act as significant predictors of subtest specific EF performance including child sex (RTI: B=.46, p<. 01; MTT: B=.05, p<.05), child age (RTI: B=.27, p<.05; MTT: B=.11, p<.01), home language (MOT: B=- .13, p<.05), maternal employment (MTT: B=-.04, p<.05) and household size (SWM: B=-1.29, p<.05). Conclusions. These study findings provide initial insights into the impacts of different types of maternal drinking behaviours and related socio-demographic factors on child EF outcomes within the context of an LMIC, South Africa.

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INTRODUCTION. D’ici 2030, le Québec comptera 180 000 personnes aînées vivant avec un trouble neurocognitif (PATNC). Les TNC entraînent des enjeux d’interaction personne environnement – l’aîné interagissant avec son environnement humain (proche aidant) ou non humain (domicile) – compromettant ainsi le maintien à domicile. Basée sur le Modèle de compétence, l’Évaluation à domicile de l’interaction personne environnement (ÉDIPE) – version cognitive vise à combler le manque d’instruments validés ciblant ces enjeux. Cet instrument comprend trois sections (Exploration des problèmes cognitifs et de leur impact; Évaluation de l’interaction; Validation et interprétation du processus d’évaluation) évaluées lors d’entrevues, d’observations et de mises en situation; deux échelles (ordinale; dichotomique) qualifient l’interaction personne-environnement. Ce Mémoire porte sur l’étude de la fidélité interexaminateurs de l’ÉDIPE–version cognitive. MÉTHODOLOGIE. Basés sur la Théorie classique de la mesure, deux ergothérapeutes indépendants et formés ont administré simultanément l’ÉDIPE–version cognitive à 30 dyades (PATNC-proches aidant), à domicile (3,2h/évaluation). Pour chaque item, le coefficient kappa, le pourcentage d’accord et l’erreur-type ont été calculés. RÉSULTATS. Les coefficients kappa varient entre -0,053 et 1,000 (pourcentages d’accord 50%-100%); la majorité (80%) varie d’Acceptable à Presque parfait. DISCUSSION. La formation et l’application rigoureuse du guide de passation soutiennent la fidélité interexaminateurs de l’instrument. Plusieurs coefficients faibles démontrent un pourcentage d’accord élevé, référant aux paradoxes de Feinstein et Cicchetti. CONCLUSION. Cette étude documente la fidélité interexaminateurs d’un instrument prometteur comblant une lacune dans la compréhension de l’interaction personne environnement des PATNC vivant à domicile. Poursuivre la validation de l’ÉDIPE–version cognitive appuiera davantage son utilisation en clinique et en recherche. / INTRODUCTION. By 2030, Quebec will have 180,000 older people living with a neurocognitive disorder (OPLwNDs). Neurocognitive disorders lead to issues affecting the person-environment interaction – the older adult interacting with his or her human (caregiver) or non-human (home) environment – thereby compromising aging in place. Based on the Model of Competence, the Home Assessment of Person-Environment Interaction (HoPE)-Cognitive Version aims to fill the gap in validated tools targeting these issues. This tool has three sections (Exploration of cognitive problems and their impact; Assessment of interaction; Validation and interpretation of the assessment process) that employ interviews, observations and task performance; two scales (ordinal; dichotomous) qualify the person-environment interaction. This Master’s thesis examines the interrater reliability of the HoPE-Cognitive Version. METHODOLOGY. Based on classical test theory, two independent, trained occupational therapists simultaneously administered the HoPE-Cognitive Version to 30 dyads (OPLwND-caregiver), at home (3.2 h/assessment). For each item, the kappa coefficient, percentage of agreement and standard error were calculated. RESULTS. Kappa coefficients ranged from -0.053 to 1.000 (percentages of agreement 50%–100%); the majority (80%) ranged from Acceptable to Almost Perfect. DISCUSSION. Training and rigorous application of the assessment guide support the tool’s interrater reliability. Several low coefficients demonstrate a high percentage of agreement, referring to Feinstein and Cicchetti’s paradoxes. CONCLUSION. This study documents the interrater reliability of a promising tool that fills a gap in understanding the person-environment interaction of OPLwNDs living at home. Further validation of the HoPE-Cognitive Version will support its use in clinical and research settings.

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<p>Deep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks. </p> <p> </p> <p>Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.</p> <p> </p> <p>We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver. </p> <p> </p> <p>Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.</p>

Contexte: Le retard de croissance intra-utérin (RCIU) désigne l’incapacité du fœtus à atteindre son potentiel de croissance déterminé génétiquement en raison de diverses causes. Il est défini le plus souvent par un poids de naissance inférieur au 10ème percentile pour l’âge gestationnel sur les courbes néonatales. Ce travail de thèse a comme objectif de répondre aux questions non-résolues sur la définition et les conséquences du RCIU dans le contexte de la grande prématurité: (1) Quelle est la meilleure définition du RCIU à utiliser pour identifier les enfants à risque ? (2) Quels sont les risques de mortalité et de morbidités néonatales respiratoires et neurologiques associés au RCIU et existe-t-il des interactions avec les pathologies de la grossesse responsables de cette naissance très prématurée ? (3) Quel est l’impact du RCIU sur le devenir neuro-développemental à 2 ans, en particulier chez les enfants nés extrêmement prématurément ? Méthodes: Nous avons utilisé deux sources de données. L’étude MOSAIC (Models for OrganiSing Access to Intensive Care for Very Preterm Babies in Europe) est une étude européenne en population qui porte sur l’ensemble des naissances survenues entre 22 et 31 semaines d’aménorrhée en 2003 dans dix régions européennes. Les enfants ont été suivis jusqu’à la sortie d’hospitalisation (population d’étude : 4525 enfants). La deuxième source est une cohorte d’enfants nés avant 27SA qui ont été hospitalisés dans le service de réanimation néonatale à l'hôpital de Port-Royal de 1999 à 2008 et qui ont eu un examen pédiatrique et une évaluation selon l’échelle de Brunet-Lézine qui inclut quatre domaines du développement global de l’enfant : la motricité globale, la motricité fine, le langage et l’interaction sociale (445 enfants admis, 268 enfants suivis à 2 ans). Résultats: Dans les deux populations, les risques de décès et de dysplasie broncho-pulmonaire étaient plus élevés pour les enfants ayant un poids de naissance <10éme percentile des courbes néonatales, mais également pour des enfants avec un poids plus élevé (entre le 10éme et le 24éme percentile des courbes néonatales ou <10ème percentile des courbes fœtales). Par contre, il n’y avait pas de lien entre les complications neurologiques et le faible poids, ni d’interaction avec les pathologies de la grossesse. Le RCIU était associé à un risque élevé du retard neurocognitif à deux ans d’âge corrigé chez les extrêmes prématurés, surtout dans le domaine de la motricité fine et de l’interaction sociale mais pas dans le domaine du langage et de la motricité globale. Nous n’avons pas trouvé d’association entre le RCIU et le risque d’infirmité motrice cérébrale à deux ans d’âge corrigé. Conclusions: L’utilisation du 10ème percentile des courbes néonatales n’est pas adaptée pour identifier l’impact du RCIU chez les grands prématurés ; l’utilisation de multiples seuils ou de courbes de croissance fœtale est nécessaire. Le RCIU accroit les risques de mortalité et de dysplasie broncho-pulmonaire, mais n’est pas associé aux lésions cérébrales sévères ; ces associations sont observées dans différents contextes périnatals (pathologies vasculaires et infectieuses, et naissances à des âges gestationnels très précoces). Le RCIU représente un facteur pronostic défavorable pour le neuro-développement à moyen terme. Nos résultats soulèvent de nouvelles questions sur le suivi adapté pour les enfants ayant un RCIU après leur sortie de l’hôpital et aussi sur les éventuels mécanismes biologiques pouvant expliquer les liens entre le RCIU avec une morbidité respiratoire et certains domaines du développement neurocognitif à moyen terme. / Background: Intrauterine growth restriction (IUGR) refers to the inability of the fetus to achieve its genetically determined growth potential due to various causes. Most often, it is defined by a birth weight less than the 10th percentile for gestational age using neonatal growth curves. This thesis aims to answer unresolved questions about the definition and consequences of IUGR in the context of very preterm birth: (1) what is the best definition of IUGR for identifying children at risk? (2) What are the risks of mortality and neonatal respiratory and neurological morbidity associated with IUGR and are there interactions with the underlying pregnancy complications responsible for the very preterm birth? (3) What is the impact of IUGR on neurodevelopmental at 2 years, especially for children born extremely preterm ? Methods: We used two data sources. The MOSAIC study (Models for Organising Access to Intensive Care for Very Preterm Babies in Europe) is a European population-based study that included all births occurring between 22 and 31 weeks of gestation in 2003 in ten European regions. The children were followed until hospital discharge (study population = 4525 infants). The second source is a cohort of children born before 27 weeks of GA who were hospitalized in the neonatal intensive care unit at the Port Royal Hospital from 1999 to 2008 and had a pediatric examination and Brunet-Lézine (BL) neurodevelopmental assessment at 2 years of corrected age (445 children in the cohort, 268children followed at 2 years). The BL assessment includes four areas of child development: gross motor, fine motor, language and social interaction skills. Results: In both populations, the risk of death and bronchopulmonary dysplasia were higher for children with a birth weight <10th percentile of neonatal growth curves but also for children with a higher birth weight (between the 10th and the 24th percentile of neonatal growth curves or <10th percentile of fetal growth curves). In contrast, there was no link between neurological complications and low birth weight and no interactions with pregnancy complications. IUGR was associated with neurocognitive delay among extremely preterm children evaluated at two years of corrected age, especially for fine motor and social interaction skills, but not for language and gross motor skills. We did not find any association between IUGR and the risk of cerebral palsy at two years of corrected age. Conclusions: The use of the 10th percentile of neonatal growth curves is not suitable for identifying the impact of IUGR in very preterm infants; using higher thresholds or fetal growth curves is necessary. IUGR increased the risks of mortality and bronchopulmonary dysplasia, but was not associated with severe brain damage; these associations are observed in multiple clinical contexts (vascular and infectious pregnancy complications, and births at very early gestational ages). IUGR is a risk factor for poor medium-term neuro-development. Our results raise new questions about the appropriate surveillance for children with IUGR after discharge from the hospital and also about possible biological mechanisms that could explain the relationship between IUGR and respiratory morbidity and neurocognitive development.

Les proches qui accompagnent une personne âgée vivant avec un trouble neurocognitif majeur à un stade avancé (TNC) en centre d’hébergement et de soins de longue durée (nommé CHSLD au Québec) rencontrent de multiples défis au quotidien. Parmi ceux-ci se retrouve la responsabilité qui leur est conférée de prendre les décisions relatives aux soins de santé pour la personne vivant avec un TNC lorsque celle-ci devient incapable de le faire. Certaines de ces décisions placent les proches dans un processus complexe qui doit être accompagné par l’équipe soignante, notamment lorsque les décisions en question auront potentiellement une incidence sur la fin de vie de la personne âgée. Les infirmières et infirmiers, par leurs compétences relatives aux soins à la famille et leur présence quotidienne directe auprès des résidentes et résidents et de leurs proches, se retrouvent dans une position privilégiée pour offrir cet accompagnement. Toutefois, peu d’études se sont intéressées aux différentes composantes de cet accompagnement. Par conséquent, cette étude visait à proposer une théorie de l’accompagnement infirmier des proches qui doivent prendre des décisions concernant la fin de vie d’une personne âgée vivant avec un TNC en CHSLD. Inspirée par la philosophie herméneutique de Gadamer (1960/2018) et la théorie du human caring élaborée par Watson (2012), une théorisation ancrée constructiviste a été réalisée auprès de neuf infirmières ou infirmiers et 10 proches rencontrés dans le cadre d’une entrevue semi-structurée individuelle. Les infirmiers et infirmières occupaient toutes un poste régulier en CHSLD depuis au moins un an, alors que les proches étaient ou avaient été impliqués dans le processus de prise de décisions concernant la fin de vie d’une personne âgée vivant avec un TNC en CHSLD. L’analyse des données s’est appuyée sur les principes suggérés par Charmaz (2014), qui incluent entre autres la codification initiale, la codification ciblée, la comparaison constante et l’écriture de mémos. Il découle de la théorie proposée l’aspect fondamental du lien de confiance établi entre l’infirmière ou l’infirmier et les proches, celui-ci ayant un impact important sur le processus de prise de décisions vécu par les proches et la qualité de l’accompagnement offert par l’infirmière ou l’infirmier. Ensuite, l’exploration du refus des soins palliatifs et le soutien du besoin des proches d’être témoin de l’état de santé actuel de la personne âgée vivant avec un TNC se sont aussi avérés des composantes essentielles de l’accompagnement infirmier. Finalement, l’enseignement au moment opportun ainsi qu’une transmission claire de l’information complètent les thèmes centraux de la théorie. Ces connaissances permettent de mieux comprendre les principaux éléments d’un accompagnement infirmier de qualité, contribuant ainsi à soutenir la pratique infirmière basée sur des résultats probants et à guider la recherche dans le développement d’interventions efficaces afin de faciliter l’expérience des proches. En outre, ces résultats démontrent l’apport indispensable des infirmières et infirmiers au processus de prise de décisions des proches. / Relatives supporting an older person living with an advanced major neurocognitive disorder (NCD) in a long-term care home (called a CHSLD in Quebec) encounter multiple challenges every day. Among them is the responsibility of making healthcare decisions on behalf of a relative living with an NCD, who is no longer able to do so themself. Some of these decisions launch relatives into a complex process that requires guidance from the healthcare team, especially when the decisions may impact the end-of-life of the person living with an NCD. Through their skills in family care and their daily presence directly among residents and relatives, nurses have a privileged role to play in offering this support. However, few studies have examined its various components. The aim of this study is therefore to propose a theory on nurses’ support of relatives making end-of-life decisions for a resident living with an NCD in a CHSLD. Inspired by Gadamer’s hermeneutical philosophy (1960/2018) and Watson’s theory of human caring (2012), a constructivist grounded theory was conducted with nine nurses and 10 relatives, whom were met in individual semi-structured interviews. These nurses had all held regular positions in CHSLDs for at least one year, while relatives were or had been involved in the end-of-life decision-making process for a person living with an NCD in a CHSLD. The data analysis was based on principles suggested by Charmaz (2014), including initial coding, focus coding, constant comparison, and the writing of memos. The proposed theory highlights trust as the fundamental aspect in the nurse-relative relationship. Indeed, trust has a significant impact on families’ decision-making process and on the quality of the support nurses provide to relatives. Exploring the refusal of palliative care and supporting relatives’ need to witness and take stock of the state of health of the person living with an NCD for themselves are two other essential components of nursing care. Finally, nurses’ well-timed education of relatives and clear transmission of information are other themes that are central to this theory. Deepening the understanding of the main elements of quality nursing support, this study reinforces evidence-based nursing practice and guides research leading to effective interventions that will ultimately facilitate relatives’ experience. Our results also demonstrate nurses’ invaluable contribution to relatives’ decision-making process.

Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.<br>