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Recherche uO, le dépôt numérique de l'Université d'Ottawa, réunit le matériel de recherche et d'enseignement créé par notre communauté universitaire et nos partenaires. Le savoir de l'Université est ainsi disponible à long terme et en accès libre, ce qui lui procure de la visibilité et facilite sa diffusion.

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Multistate-CANVAS Enables the De Novo Design of Enzymes for Complex Multistep C-C Bond-forming Reactions
(Université d'Ottawa | University of Ottawa, 2026-09-11) Kouassi, Jean Roch; Chica, Roberto
The design of enzymes for reactions lacking natural biocatalysts is a major goal of computational protein design. While computational methods have enabled the creation of de novo enzymes for diverse chemical transformations, designing enzymes for complex multistep C-C bond-forming reactions remains a significant challenge. These reactions require a single active site to accommodate multiple substrates and evolving catalytic requirements throughout the reaction coordinate, making them difficult to represent using conventional single-state computational design approaches. To address this challenge, we developed Multistate-CANVAS (MsC), a computational workflow for the de novo design of enzymes for multistep C-C bond-forming reactions. As a proof of concept, MsC was applied to the Friedel-Crafts alkylation of 2-methylindole with trans-cinnamaldehyde, a multistep C-C bond-forming reaction. Experimental characterization demonstrated that multiple variants could be successfully expressed, purified, folded, and exhibited measurable catalytic activity. The most active variant, FCS_1, achieved a 254-fold enhancement in conversion relative to the uncatalyzed background reaction with a catalytic efficiency of 0.01 M⁻¹ s⁻¹. The successful generation of active Friedel-Crafts alkylases suggests the feasibility of extending computational enzyme design to complex multistep C-C bond-forming reactions. More broadly, MsC provides a framework for expanding the scope of de novo enzyme design toward increasingly challenging new-to-nature chemistry.
Optimization of Anaerobic Digestion through In-situ Hydrogen Biomethanation and Machine Learning Prediction of Methane Production
(Université d'Ottawa | University of Ottawa, 2026-09-11) Paydarnik, Pouria; Bonakdari, Hossein
Biogas produced through anaerobic digestion (AD) represents an important renewable energy carrier, yet the relatively low methane (CH4) content of raw biogas limits its value as fuel and grid-injectable gas. In-situ hydrogen (H2) biomethanation (HBM) the direct injection of H2 into an operating anaerobic digester to promote hydrogenotrophic conversion of carbon dioxide (CO2) into additional CH4 offers a promising route for biological biogas upgrading. However, the low aqueous solubility of H2 means that gas-liquid mass transfer is frequently the rate-limiting step, and existing Anaerobic Digestion Model No. 1 (ADM1) implementations do not adequately represent the dynamic interplay between H2 supply, transfer intensity, and process stability. This thesis presents a hybrid mechanistic, data-driven framework to optimize in-situ HBM and predict CH4 production using machine learning. The standard ADM1 model, implemented within the Benchmark Simulation Model No. 2 (BSM2) structure using the open-source PyADM1 platform, was extended in three ways: (i) an external H2 injection term was incorporated into the gas-phase H2 balance; (ii) gas-specific volumetric mass transfer coefficients (k_L a) for H2, CO2, and CH4 were introduced using diffusivity-based scaling relationships; and (iii) an inorganic carbon limitation factor was added to the hydrogenotrophic methanogenesis rate to prevent unrealistic H2 conversion under carbon-depleted conditions. The modified model was verified against published biomethanation benchmark data, achieving deviations below 8% for CH4 production and below 1% for pH across the stable operating range. Long-term dynamic simulations were conducted over 280-day periods across a matrix of k_L a scenarios (60 to 1000 d-1) and H2 injection rates (0 to 2500 m3.d-1), yielding a dataset of approximately 280 daily observations per scenario. Results showed that CH4 production increased consistently with H2 injection under all k_L a conditions, but the efficiency of H2 utilization depended strongly on mass transfer intensity. Under low k_L a conditions (60 d-1), H2 conversion efficiency reached only 57-61%, with substantial H2 slip and declining CH4 content at higher injection rates. Under high k_L a conditions (1000 d-1), H2 conversion efficiency exceeded 96% across the full injection range, with CH4 content in the outlet gas reaching up to 84%. An injection-efficiency evaluation framework was developed to identify the most favorable operating points under each mass-transfer scenario for subsequent machine-learning (ML) dataset preparation. Four ML algorithms Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machine (ELM), and Evolutionary Polynomial Regression (EPR) were trained using ten process variables to predict CH4 and total biogas flow rates. SVR achieved the highest testing accuracy, with R2 values of 0.996 for CH4 and 0.9948 for biogas, followed by ELM with 0.9899 and 0.9834, respectively. RF also demonstrated strong predictive performance, with testing R2 values of 0.9879 for CH4 and 0.9807 for biogas. EPR produced lower but still strong predictive performance, with testing R2 values of 0.9561 and 0.9276, while offering the added advantage of explicit, interpretable mathematical expressions. The findings demonstrate that in-situ H2 biomethanation can substantially improve CH4 yield and biogas quality when gas-liquid mass transfer is sufficiently strong, and that the combined ADM1 ML framework developed in this work provides both process insight and practical prediction tools for anaerobic digestion optimization. The EPR-derived explicit formula further provides a transparent and computationally inexpensive alternative for CH4 estimation within the investigated operating domain.
L’analytique de l’apprentissage et l’autorégulation dans les environnements numériques d’apprentissage : comment soutenir les activités de réflexion inhérentes au développement de compétences d’autorégulation des professionnelles et des professionnels?
(Université d'Ottawa | University of Ottawa, 2026-09-11) Ciocca, Jean-Luc; Duplàa, Emmanuel
Dans un contexte marqué par la compétition et l’innovation, la formation en ligne s’est imposée dans les organisations comme un dispositif de formation professionnelle visant à maintenir un haut niveau de compétence du personnel (Boboc et Metzger, 2015). Or, avec l’essor du numérique, l’autorégulation est devenue une compétence clé pour soutenir l’engagement et la persévérance, en aidant chacun à définir ses objectifs et à élaborer des stratégies pour les atteindre (Poellhuber et Michelot, 2019; Schunk et Zimmerman, 2008). La plupart des plateformes de formation en ligne analysent les activités d’apprentissage et génèrent des retours d’information : ce processus s’inscrit dans ce que l’on appelle l’analytique de l’apprentissage (ou Learning Analytics en anglais) (Elias, 2011; Long et Siemens, 2011), laquelle repose sur l’exploitation des données éducatives afin de comprendre et d’améliorer les processus d’apprentissage grâce à des techniques d’analyse (Long et Siemens, 2011). Notre recherche doctorale, de nature qualitative interprétative et de type exploratoire, porte sur le phénomène de l’analytique de l’apprentissage au soutien de l’autorégulation. À ce sujet, elle vise à mieux comprendre comment les retours d’information issus de l’analytique de l’apprentissage peuvent contribuer à la régulation des apprentissages des professionnelles et des professionnels dans le contexte du perfectionnement professionnel dans une formation en ligne. Notre objectif est de documenter, selon la perspective étudiante, quelles dimensions de l’autorégulation sont les plus et les moins influencées par ces retours d’information. Inscrit dans une démarche microsociologique, cette recherche s’appuie sur la théorie sociocognitive de l’autorégulation (Bandura, 1986; Zimmerman, 2002; Zimmerman et al., 2017; Zimmerman et Moylan, 2009) et sur les processus qui sous-tendent l’analytique de l’apprentissage (Clow, 2012; Verbert et al., 2013). En tant qu’étude de cas (Merriam, 1988), nous avons examiné comment une démarche fondée sur l’analytique de l’apprentissage favorise le développement des compétences d’autorégulation dans un contexte de perfectionnement professionnel en ligne. Pour ce faire, nous avons utilisé deux méthodes de collecte de données de nature qualitative auprès de onze personnes aux parcours de perfectionnement professionnel variés et inscrites dans différentes facultés à l’université d’Ottawa : l’entrevue individuelle semi-dirigée (Paillé, 1991) et la photo-élicitation (Harper, 2002). Les résultats de la recherche ont révélé que l’analytique de l’apprentissage soutiendrait davantage l’autorégulation sur les plans métacognitif, comportemental et motivationnel, notamment par le biais d’outils de suivi et de gestion du temps. Notre étude a permis d’approfondir les connaissances du phénomène de l’analytique de l’apprentissage, lequel a été peu étudié dans le contexte de la formation en ligne à des fins de développement professionnel. Nous avons aussi formulé des pistes d’exploration portant sur des approches méthodologiques et des terrains de recherche visant à renforcer la compréhension de l’impact de l’analytique sur l’autorégulation dans des contextes de formations diversifiés.
Novel Approaches to Insomnia Care: A Multi-Study Investigation at the Intersection of Technology, Sleep, and Mental Health
(Université d'Ottawa | University of Ottawa, 2026-09-11) Dion, Karianne; Robillard, Rébecca
As the most prevalent sleep disorder in Canada, insomnia has wide-ranging consequences on physical and mental health and represents a significant health burden for society. Insomnia is particularly common among individuals with psychological comorbidities such as depression and post-traumatic stress disorder (PTSD) and is now viewed as a core feature of these conditions. Insomnia is linked to greater severity and persistence of psychological conditions, and contrary to common assumptions, rarely resolve “on their own” following mental-health-focused treatments, emphasizing the need for sleep-focused care. As the first-line treatment for insomnia, cognitive behavioural therapy for insomnia (CBT-I) is effective in cases of co-occurring psychological disorders and contributes to meaningful improvements in both sleep and mental health. Despite its status as the gold-standard treatment for insomnia, substantial barriers restrict access to CBT-I and existing protocols often fail to address the clinical complexity associated with comorbid psychological disorders. Emerging technologies such as portable sleep monitors and digital interventions offer promising opportunities to enhance CBT-I and expand access for individuals with complex mental health needs. Given the widespread adoption of these technologies among the general population, a better understanding of how portable sleep monitors are used in real-world settings and of their potential influence on sleep is needed to inform their appropriate integration in clinical practice. Beyond technological innovations, multimodal treatment approaches that address multiple dimensions of sleep represent another pathway for improving outcomes in the context of psychological comorbidities. In particular, the combination of CBT-I and imagery rehearsal therapy (IRT) for nightmares has demonstrated efficacy in PTSD samples under highly controlled conditions, underscoring the need to assess its effectiveness in routine clinical settings. Study 1 (population-based survey) investigated the use of sleep wearables in a representative sample of 1,200 Canadians. This study highlights that one in five Canadians use commercial sleep wearables, reveals generally positive attitudes toward their impact on sleep, and identifies novel predictors of use, including sleep and psychological disorders. Compared to nonusers, wearable users had significantly poorer sleep and higher engagement in sleep-related health care, including medical consultations and medication use. These findings may reflect a greater interest in sleep monitoring technologies among individuals with heightened sleep difficulties. Wearable use was also identified as a moderator of the relationship between anxiety symptoms and sleep duration, with users experiencing greater declines in sleep quantity as anxiety increased relative to nonusers. Shifting from population-level adoption of sleep monitors to their implementation in clinical practice, Study 2 (randomized clinical trial) explored the integration of a portable EEG device in digital CBT-I in a sample with comorbid insomnia and depression symptoms (n = 47). Results from this study indicate comparable treatment adherence, acceptability, satisfaction, and effectiveness between participants monitoring their sleep with a standard sleep diary versus those using a portable EEG device. Yet, through quantitative and qualitative data, participants highlighted that integrating EEG monitoring in digital CBT-I yielded several advantages including gaining a better understanding of one’s sleep and increased treatment motivation. Challenges associated with EEG monitoring were also identified, indicating potential limitations in its suitability in some individuals and informing important considerations for clinical practice. Extending CBT-I adaptations to a different clinical population, Study 3 (clinical effectiveness study) assessed the real-world outcomes of a 10-week group intervention combining CBT-I and IRT in military and law enforcement personnel with PTSD (n = 60) receiving care at operational stress injury clinic. Significant improvements were observed from pre- to post-intervention across insomnia severity, nightmare frequency and distress, and PTSD symptoms. Parallel improvements were observed between insomnia, nightmares, and PTSD, illustrating the relevance of sleep-focused care as part of an integrated approach to psychological care for trauma-exposed populations. Collectively, these studies underscore both the widespread adoption and clinical relevance of sleep monitors, emphasize sleep as a key transdiagnostic target in comorbid psychological disorders, and highlight novel approaches to adapting and expanding access to insomnia care. Future studies should investigate additional applications of sleep monitors within insomnia interventions such as CBT-I/IRT, examine physiological changes during CBT-I at both macro- and micro-levels of sleep architecture, and explore strategies to make CBT-I more accessible, adaptable, and effective for individuals with diverse psychological comorbidities.
An Investigation of Chromatin Dynamics in Natural Killer Cells After Tumour Resection to Identify Therapeutic Targets Against Postoperative Anti-Tumour Immune Dysfunction
(Université d'Ottawa | University of Ottawa, 2026-09-11) Mistry, Henna; Auer, Rebecca
Tumour resection is critical for improving the overall survival of patients with solid malignancies, yet surgery paradoxically generates an environment that favours residual disease, in part through widespread immunosuppression. Natural killer (NK) cells are key mediators of anti-tumour immunity and their postoperative dysfunction has been intricately linked to cancer recurrence, though its molecular basis remains unclear. Here, we demonstrate that circulating NK cells from cancer patients are globally hyporesponsive following surgery, and that this dysfunction persists despite removal from the immunosuppressive postoperative milieu. Multiomic profiling suggests a coordinated state of genome remodeling, metabolic suppression, and dysregulated protein homeostasis. Central to this dysfunctional state was a subpopulation of NK cells characterized by a loss of accessibility around some effector gene promoters, alongside an increase in the predicted activity of heat shock factors (HSF), despite reduced overall translation and protein aggregation. Simultaneously, the predicted activities of activator protein 1 (AP-1) transcription factors were aberrantly lost in these stressed cells. Critically, to determine if genome remodeling can be prevented, and subsequently correlated with the prevention of NK cell dysfunction, we developed a novel ex vivo model in which healthy donor NK cells are added to postoperative blood and assayed for their effector functions. Preliminary experiments identified BET inhibitors and PKC agonists that prevented healthy donor NK cell dysfunction, but not endogenous NK cell dysfunction in postoperative blood. Altogether, these findings define a distinct stress signature underlying postoperative NK cell dysfunction and suggest that prevention, rather than reversal of the epigenomic remodeling, is necessary to improve postoperative NK cell anti-tumour immunity.