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Recent Submissions

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    Development of a coupled hydro-economic model to support groundwater irrigation decisions
    (Public Library of Science, 2026-04-03) Tian, Boyao; Brookfield, Andrea; Insley, Margaret
    Groundwater sustains global agriculture but faces significant pressure from overexploitation, threatening long-term water security. Achieving a balance between agricultural productivity and sustainable groundwater use requires decision-support tools that can integrate hydrologic and economic information and be adapted to different farm and aquifer conditions. This study develops an accessible farm-level hydro-economic model that links groundwater dynamics with economic outcomes to evaluate irrigation strategies under regulatory and physical constraints. The model estimates land value over time while incorporating uncertain precipitation, irrigation practices, and regulatory limits. This research presents a novel application of Conditional Value-at-Risk to assess economic risk of groundwater irrigation by focusing on the tail of the probability curve, emphasizing potential extreme adverse outcomes rather than average performance. Applied to a representative High Plains Aquifer site, the model shows that more pumping does not guarantee greater profitability, as diminishing returns and aquifer depletion can undermine long-term benefits. Instead, irrigation strategies aligned with site-specific aquifer properties and regulatory thresholds improve both economic performance and sustainability. This scalable approach provides a useful framework to inform irrigation policy, support farmer decision-making, and promote sustainable groundwater under growing uncertainty.
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    Reinitiation of antidepressant pharmacotherapy among patients discharged from the hospital: A population-based cohort study
    (Public Library of Science, 2026-03-25) Abu-Ashour, Waseem; Gamble, John-Michael; Hawbolt, John; Sale, Joanna E. M.
    To examine the incidence of antidepressant medication reinitiation following a ≥ 6-month gap as a proxy for relapse in patients with major depressive disorder (MDD) discharged from hospital and followed in primary care, and to assess patterns of antidepressant use before and after reinitiation, as well as associations with socio-demographic and clinical variables. We conducted a population-based cohort study using seven linked administrative health databases from Newfoundland and Labrador, Canada. Adults (≥18 years) with a first hospitalization for MDD between June 2017 and March 2023 and a post-discharge antidepressant prescription of ≥30 days were included. The primary outcome was reinitiation of antidepressant pharmacotherapy following a ≥ 6-month treatment gap. Antidepressant treatment groups were defined as SSRI monotherapy (reference), SNRI monotherapy, other monotherapy, two-medication combination therapy, and three or more medication combinations. Time-varying Cox regression models were used to assess associations with reinitiation risk, adjusted for age, sex, socioeconomic status (SES), and length of hospital stay. Sensitivity and exploratory age-stratified analyses were conducted. Among 2,734 patients, 61% reinitiated antidepressant treatment after a ≥ 6-month gap. SSRI monotherapy was the most common initial regimen (34.3%), followed by 2-medication combinations (18.1%). Combination therapy was associated with lower reinitiation risk compared to SSRIs: HR = 0.67 (95% CI: 0.50–0.90) for 2-medication combinations and HR = 0.49 (95% CI: 0.40–0.60) for 3 + medication combinations. SNRI monotherapy conferred modest protection (HR = 0.84, 95% CI: 0.72–0.99). Age, sex, and SES were independently associated with reinitiation. Younger adults, males, and individuals in both high and low-income quintiles were at increased risk. Reinitiation of antidepressants after a ≥ 6-month gap was common following hospitalization for MDD. Combination therapy maybe associated with reduced reinitiation risk compared to SSRI monotherapy, with age-specific treatment effects, although residual confounding cannot be excluded. Further research, ideally RCTs, is needed before informing clinical decision-making.
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    Computational modeling-directed combination treatment with etanercept and mifepristone mitigates neuroinflammation in a mouse model of Gulf War Illness
    (Public Library of Science, 2026-03-17) Kelly, Kimberly A.; Felton, Christopher M.; Billig, Brenda K.; Yilmaz, Ali A.; O'Callaghan, James P.; Craddock, Travis J. A.; Broderick, Gordon; Klimas, Nancy; Michalovicz, Lindsay T.
    Gulf War Illness is a chronic multi-symptom disorder experienced by over 30% of veterans from the 1990–1991 Gulf War and is increasingly recognized to be driven by underlying persistent neuroinflammation resulting from chemical and physiological exposures experienced during deployment. Despite significant advances in identifying Gulf War-relevant exposures and underlying pathobiology, effective treatment strategies for Gulf War Illness are still largely lacking. Many studies that have evaluated potential therapies for Gulf War Illness have primarily focused on a single treatment. However, through a mechanistically informed computational evaluation of blood biomarkers and gene expression in veterans with Gulf War Illness, we identified that a combination of anti-inflammatory and anti-glucocorticoid treatment may prove effective in treating Gulf War Illness. Here, we have evaluated combined treatment with the anti-TNFα drug, etanercept, and anti-glucocorticoid, mifepristone, in an established long-term mouse model of Gulf War Illness of combined physiological stress and nerve agent exposure. Supporting results from the computational modeling of this treatment, we found that this drug combination significantly alleviates the underlying neuroinflammation associated with Gulf War Illness. The fusion of computational and in vivo preclinical treatment evaluation may provide a highly useful and translationally relevant means by which to identify successful treatment paradigms for Gulf War Illness.
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    Exploring the prevalence of reasons adolescents feel unwelcome at school
    (Public Library of Science, 2026-05-07) Lucibello, Kristen M.; Zheng, Lin; Leatherdale, Scott T.; Patte, Karen A.
    Cultivating positive school climates has been recognized as a global and Canadian priority for adolescent health, academic achievement, and development. Feeling welcome at school is an early contributor to overall positive school climate, although detailed understanding of different reasons adolescents may not feel welcome at school at the population-level is limited. The present study examined the prevalence of reasons students felt unwelcome at school and the characteristics of students endorsing each reason. Self-report survey data were used from students (N = 15,610, Mage ± SD = 15.6 ± 1.2, 48.1% girls) from 41 secondary schools in Ontario, Canada, that participated in the 2022–2023 wave of the COMPASS study. Frequencies calculated the prevalence of each reason for feeling unwelcome at school. Overall, 63.4% of students felt unwelcome at school for at least one reason. Appearance (29.6%), another reason (23.3%), marks at school (19.3%), emotional/psychological challenge (15.7%), and race/ethnicity/culture (10.6%) were the most common reasons for feeling unwelcome at school. Overweight perception, gender diverse identity, and low perceived relative financial affluence were overrepresented in participants that felt unwelcome at school relative to the overall sample. The many reasons adolescents feel unwelcome at school need to be addressed as schools focus on building positive and inclusive school climates, and should be considered across multiple levels (e.g., teacher training, students’ learning materials, policy).
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    Aluminum Nitride Photonic Integrated Circuits with Applications to Cold Atoms
    (University of Waterloo, 2026-06-02) Videnov, Nikolay
    Quantum sensing has become foundational to many modern technologies. Precision time keeping is core to the global positioning system (GPS), magnetometry is core to mineral discovery for mining. The ability to measure with unparalleled sensitivity has driven many major technological improvements across diverse fields. From simple neutral atom vapour cells to sophisticated ion traps, atoms are a preeminent quantum sensing platform. However, these systems remain difficult to make field-deployable, owing largely to the complexity and fragility of their optical systems. As the invention of the laser and commercialization of external cavity diode lasers (ECDL) enabled increasingly complex trapped atom experiments this thesis aims to take a step toward the next stage: scalable, robust, and portable trapped-atom-based quantum sensors. I argue that a primary limitation to achieving this goal lies in the reliance on bulk optical systems, which exhibit poor size, weight, and power (SWaP) characteristics, are prone to misalignment, and require specialized assembly. To overcome these limitations, I propose the use of photonic integrated circuits (PIC), leveraging fabrication tools and techniques from the semiconductor industry to create a versatile PIC "toolbox" for the trapped-atom community. The unique requirements of such systems motivate the choice of aluminum nitride (AlN) as the waveguiding material—a high-index, ultra-wide band gap, and electro-optically active medium that meets the optical and material needs of trapped-atom applications but has received relatively little attention compared to other established platforms. This thesis therefore details a reproducible nanofabrication process for AlN waveguides that achieves state-of-the-art propagation losses through the use of atomic layer deposition and rapid thermal annealing. Rather than treating this process as proprietary, the complete recipe is shared here for the benefit of the broader AlN research community. I also present the first demonstration of a hybrid ECDL incorporating an AlN photonic integrated circuit, an important milestone toward realizing fully integrated on-chip light sources. These hybrid ECDL operate near 852 nm and 650 nm, addressing optical transitions in cesium and barium ions. Finally, I describe a novel dual-mode phase shifter that combines electro-optic and thermo-optic tuning within a single fabrication layer, enabling both high-speed modulation and large index changes. Collectively, the work presented in this thesis represents a significant step toward fully integrated, chip-scale optical systems for trapped-atom experiments—paving the way for the next generation of compact, deployable quantum sensors.