SCRIPTORIUM (Université de Moncton)
Not a member yet
2550 research outputs found
Sort by
Régulation de l'expression des cadhérines chez les cellules de sertoli du testicule
No full text"Les cellules de Sertoli sont des cellules de soutien importantes pour l’établissement de la structure de la gonade, la quantité des cellules germinales et la spermatogénèse. Les cellules de Sertoli forment aussi la barrière-hémato testiculaire (BHT) qui est composée de plusieurs types de jonctions et des protéines d’adhésion, y compris des cadhérines. La CDH3 (P-cadhérine) est exprimée chez les cellules de Sertoli à partir de la vie fœtale et diminue après la naissance, tandis que la CDH2 (N-cadhérine) est exprimée de manière stable pendant tous les stades du développement des cellules de Sertoli. La délétion de Cdh2 spécifiquement chez les cellules de Sertoli perturbe la structure du testicule et des tubules séminifères, ce qui affecte la spermatogénèse et mène à une sous-fertilité. Plusieurs hormones comme la FSH, la testostérone, l’hormone thyroïdienne, l’activine et l’inhibine participent à la régulation de la prolifération et de la différenciation des cellules de Sertoli et également à la régulation des protéines jonctionnelles y compris des cadhérines. Parmi les facteurs de transcription, la famille AP-1 est un médiateur de plusieurs voies de signalisation importantes pour la prolifération et la différenciation des cellules de Sertoli et la régulation des jonctions testiculaires. Les profils d’expression des membres AP-1 sont corrélés avec celui de CDH3 chez les cellules de Sertoli. D’ailleurs, des membres de la famille AP-1 participent à la régulation d’autres protéines d’interaction comme GJA1 et NECTIN2 dans plusieurs types cellulaires du testicule."
Factors contributing to school food program acceptance: A Review of Canadian literature
No full textDiet quality and food security are a concern in school-Aged children in Canada. In 2019, the Canadian federal government announced the intention to work towards a national school food program. Understanding the factors that impact school food program acceptability can inform planning to ensure that students are willing to participate. A scoping review of school food programs in Canada completed in 2019 identified 17 peer-reviewed and 18 grey literature publications. Of these, five peer-reviewed and nine grey literature publications included a discussion of factors that impact the acceptance of school food programs. These factors were thematically analyzed into categories: stigmatization, communication, food choice and cultural considerations, administration, location and timing, and social considerations. Considering these factors while planning can help to maximize program acceptability.udemauteur: Stephanie War
Signaling-specific inhibition of the CB1 receptor for cannabis use disorder: phase 1 and phase 2a randomized trials
No full textCannabis use disorder (CUD) is widespread, and there is no pharmacotherapy to facilitate its treatment. AEF0117, the first of a new pharmacological class, is a signaling-specific inhibitor of the cannabinoid receptor 1 (CB1-SSi). AEF0117 selectively inhibits a subset of intracellular effects resulting from Δ9-tetrahydrocannabinol (THC) binding without modifying behavior per se. In mice and non-human primates, AEF0117 decreased cannabinoid self-administration and THC-related behavioral impairment without producing significant adverse effects. In single-ascending-dose (0.2 mg, 0.6 mg, 2 mg and 6 mg; n = 40) and multiple-ascending-dose (0.6 mg, 2 mg and 6 mg; n = 24) phase 1 trials, healthy volunteers were randomized to ascending-dose cohorts (n = 8 per cohort; 6:2 AEF0117 to placebo randomization). In both studies, AEF0117 was safe and well tolerated (primary outcome measurements). In a double-blind, placebo-controlled, crossover phase 2a trial, volunteers with CUD were randomized to two ascending-dose cohorts (0.06 mg, n = 14; 1 mg, n = 15). AEF0117 significantly reduced cannabis’ positive subjective effects (primary outcome measurement, assessed by visual analog scales) by 19% (0.06 mg) and 38% (1 mg) compared to placebo (P < 0.04). AEF0117 (1 mg) also reduced cannabis self-administration (P < 0.05). In volunteers with CUD, AEF0117 was well tolerated and did not precipitate cannabis withdrawal. These data suggest that AEF0117 is a safe and potentially efficacious treatment for CUD. ClinicalTrials.gov identifiers: NCT03325595 , NCT03443895 and NCT03717272 .udemauteur: Etienne Hebert-Chatelai
Apprentissage profond et bioacoustique marine pour la détection de poissons
No full textLa recherche sur l’environnement est un sujet qui n’a jamais été plus pertinent qu’aujourd’hui. L’effet de l’industrialisation sur les écosystèmes de la terre n’était pas initialement bien connu et la conservation de ces écosystèmes est maintenant d’importance critique, À cette fin, il est important de considérer les technologies qui peuvent être utilisées afin de pouvoir suivre l’état des écosystèmes. Puisque tout changement dans la migration d’espèces est un facteur qui peut nous informer des changements dans un écosystème, la détection automatique d’espèces serait un grand atout pour la conservation d’écosystèmes. L’objectif de notre travail est le développement d’un système de détection d’espèces de poissons par le son, plus spécifiquement pour le corb, une espèce de poisson sonifère. À cet effet, nous présentons premièrement une méthode d’apprentissage profond qui utilise des données sous forme de spectrogrammes. Nous présentons deuxièmement une analyse de l’effet de la variation de la taille de fenêtre FFT utilisée pour des spectrogrammes sur la performance de modèles. Cette thèse présente aussi une revue de littérature qui détaille les méthodes d’apprentissage machine utilisées dans le contexte de l’analyse de signaux acoustiques de l’écologie marine ou bioacoustique marine. Mots-clés : Reconnaissance de poissons; Apprentissage profond; Réseaux neuronaux convolutifs; Conservation environnementale; Classification d’audio; Spectrogrammes
Evaluation of a Community Suicide Prevention Project (Roots of Hope): Protocol for an Implementation Science Study
No full textBackground: Roots of Hope (RoH) is a multisite Canadian community-based suicide prevention initiative developed by the Mental Health Commission of Canada (MHCC), which is based on evidence for intervention effectiveness and World Health Organization recommendations. Seven communities developed local activities in the following 5 pillars: specialized supports, training and networks, public awareness, means safety, and evaluation research. Objective: We aim to use an implementation research approach to understand the RoH model for reducing suicidal behaviors and their impacts in communities, and the lessons learned for the equitable development and implementation of RoH in different contexts. Moreover, we want to understand how the program is implemented in relation to the context, the causal pathways, and the factors influencing successful implementation. The evaluation includes assessments of short-term and intermediate effects at each site and overall. Methods: The principal investigator (PI) developed a consensus among local research coordinators on common approaches and indicators through ongoing participation in an online community of practice, and regular virtual and in-person meetings. At the completion of the pilot phase, the PI will summarize evaluation results across sites and conduct pooled analyses. The RoH theory of change and evaluation model shows how evaluation activities from the planning phase through the implementation of activities in each of the pillars can help clarify the viability of the RoH model and identify factors that facilitate and inhibit effective and equitable implementation in different contexts. Beginning with a situational analysis to identify resources in each community and local specificities, we will examine the implementation characteristics of conformity, dosage, coverage, quality, utility, equity, appreciation, facilitators, and impediments. Evaluation of short-term effects will focus on changes in knowledge, attitudes, behaviors, help-seeking, service use, stigma, media reports, empowerment, and care experiences. Intermediate effects, long-term effects, and impact will include assessments of the changes in suicides, suicide attempt rates, and suicide risk indicators. A variety of data sources, both quantitative and qualitative, will be used. Results: The quantitative and qualitative data from all sites will be summarized by the PI in March 2023 to draw conclusions to help the MHCC in its improvements to the RoH model, and to inform communities about how to better implement RoH. Since the COVID-19 pandemic occurred at the beginning of program implementation, its impact and influence will be documented. The validity of RoH in contributing to the prevention of suicides and suicidal behaviors will be clarified in a variety of contexts. The final evaluation report will be available in September 2023. Conclusions: The evaluation results, including the identification of factors that facilitate and inhibit the implementation of RoH and the adaptations to challenges, will be useful to the MHCC, current RoH communities, and those considering adopting the RoH model.udemauteur: Jalila Jbilo
English Emotional Voice Conversion Using StarGAN Model
No full textThe StarGANv2-VC model is a many-to-many non-parallel generative adversarial network (GAN) voice conversion (VC) model that has proven effective in style conversion tasks. This study aimed to investigate the scalability and diversity of the model for English emotional voice conversion (EVC) across different speakers and emotions. We carried out many experiments using an Emotional Speech Database (ESD), comprising a single speaker-multi-emotion experiment, a multi-speakers-multi-emotions experiment (gender-dependent), and a multi-speakers-multi-emotions experiment (gender-independent). We also assessed the effect of training set size and compared the performance of the StarGANv2-VC model with a CycleGAN model. Our study found that the StarGANv2-VC model accurately converted the pitch of the voice across all four emotions (neutral, happy, sad, and angry). However, the model's efficiency in converting multi-emotions to multi-speakers was not as high as its efficiency in voice conversion for multi-speakers. Further research is needed in this area. We objectively assessed the quality of the converted speech using Mel-frequency cepstral distortion (MCD) and root-mean-square error (RMSE) for spectrum and prosody, respectively. Additionally, we conducted cross-emotion recognition using a convolutional recurrent neural network (CRNN).udemauteur: Sid Ahmed Selouan
Effects of the HEARTY exercise randomized controlled trial on eating behaviors in adolescents with obesity
No full textThere are well-recognized benefits of behavioral interventions that include exercise for children and adolescents with obesity. However, such behavioral weight management programs may precipitate unintended consequences. It is unclear if different exercise modalities impact eating behaviors differently in youth with obesity. Objectives: The purpose of this study was to examine the effects of aerobic, resistance, and combined aerobic and resistance exercise training on eating attitudes and behaviors (uncontrolled eating, restrained eating, emotional eating, external eating and food craving) among adolescents with overweight and obesity. Methods: N = 304 (70% female) adolescents with overweight and obesity participated in the 6-month Healthy Eating Aerobic and Resistance Training in Youth (HEARTY) randomized controlled trial. All participants were inactive post-pubertal adolescents (15.6 ± 1.4 years) with a mean BMI = 34.6 ± 4.5 kg/m2. The Food Craving Inventory (food cravings), Dutch Eating Behavior Questionnaire (restrained eating, emotional eating, external eating), and the Three-Factor Eating Questionnaire (uncontrolled eating) were used to assess eating attitudes and behaviors. Results: All exercise groups showed within-group decreases in external eating and food cravings. Participants randomized to the Combined training group and were more adherent showed the greatest improvements in eating behaviors and cravings. Conclusions: A 6-month exercise intervention produced improvements in disordered eating behaviors and food cravings, but effects may be gender and modality-specific. Findings highlight the need to tailor exercise intervention to participant characteristics for the promotion of healthier eating and weight management outcomes in youth with obesity. Clinical Trial Registration # and Date: ClinicalTrials.Gov NCT00195858, September 12, 2005.udemauteur: Denis Prud'homm
Quels déterminants favorisent le partage du congé parental?: exploration des variables individuelles et relationnelles chez des parents canadiens
No full textDe nos jours, l’importance du rôle du père dans la sphère familiale est davantage mise de l’avant. En dépit des discours visant une meilleure égalité entre les guerres, les données soulèvent que les mères prennent encore la majeure partie du congé parental. À l’heure actuelle, peu d’études canadiennes de nature quantitative se sont penchées sur les facteurs influençant la prise de décision quant au partage de ce congé. L’objectif de la présente thèse doctorale est donc d’explorer certains déterminants personnels et relationnels pouvant prédire la façon dont les couples se divisent le congé parental. Au total, 165 parents canadiens, ayant au moins un enfant entre 0 et 2 ans, ont été recrutés afin de compléter une série de questionnaires évaluant les caractéristiques personnelles et relationnelles de l’expérience parentale. Les résultats des analyses de régression soulèvent que la norme sociale, c’est-à-être les opinions de l’entourage des parents quant au congé parental prédit significativement la durée du congé parental absolue et proportionnelle pris par les pères. Plus spécifiquement, plus les proches des mères s’attendent à ce qu’elles prennent un congé parental plus long, plus les pères s’en trouvent affectés avec la prise de congés parentaux plus courts. Ce constat soulève qu’il reste encore du chemin pour déconstruire les stéréotypes liés aux rôles de genre et inhérents au congé parental. Mots-clés : congé parental. Normes sociales, attitudes, relation coparentale. Satisfaction conjugale, milieu de travail
End-to-End Transformer-Based Models in textual-based NLP
No full textTransformer architectures are highly expressive because they use self-attention mechanisms to encode long-range dependencies in the input sequences. In this paper, we present a literature review on Transformer-based (TB) models, providing a detailed overview of each model in comparison to the Transformer’s standard architecture. This survey focuses on TB models used in the field of Natural Language Processing (NLP) for textual-based tasks. We begin with an overview of the fundamental concepts at the heart of the success of these models. Then, we classify them based on their architecture and training mode. We compare the advantages and disadvantages of popular techniques in terms of architectural design and experimental value. Finally, we discuss open research, directions, and potential future work to help solve current TB application challenges in NLP.udemauteur: Moulay Akhlouf
Deep Learning Methods for Chest Disease Detection Using Radiography Images
No full textX-ray images are the most widely used medical imaging modality. They are affordable, non-dangerous, accessible, and can be used to identify different diseases. Multiple computer-aided detection (CAD) systems using deep learning (DL) algorithms were recently proposed to support radiologists in identifying different diseases on medical images. In this paper, we propose a novel two-step approach for chest disease classification. The first is a multi-class classification step based on classifying X-ray images by infected organs into three classes (normal, lung disease, and heart disease). The second step of our approach is a binary classification of seven specific lungs and heart diseases. We use a consolidated dataset of 26,316 chest X-ray (CXR) images. Two deep learning methods are proposed in this paper. The first is called DC-ChestNet. It is based on ensembling deep convolutional neural network (DCNN) models. The second is named VT-ChestNet. It is based on a modified transformer model. VT-ChestNet achieved the best performance overcoming DC-ChestNet and state-of-the-art models (DenseNet121, DenseNet201, EfficientNetB5, and Xception). VT-ChestNet obtained an area under curve (AUC) of 95.13% for the first step. For the second step, it obtained an average AUC of 99.26% for heart diseases and an average AUC of 99.57% for lung diseases.udemauteur: Adnane Ait Nasser; Moulay Akhlouf