Publications

The impact of statistical adjustment for assay performance on inferences from SARS-CoV-2 serological surveillance studies
Jiacheng Chen
Yuan Yu
Sheila F O’Brien
Carmen Charlton
Steven J. Drews
Jane M Heffernan
Amber M Smith
Y. Nakagama
Yasutoshi Kido
David Buckeridge
W Alton Russell
Choice of immunoassay influences population seroprevalence estimates. Post-hoc adjustments for assay performance could improve comparability… (voir plus) of estimates across studies and enable pooled analyses. We assessed post-hoc adjustment methods using data from 2021–2023 SARS-CoV-2 serosurveillance studies in Alberta, Canada: one that tested 124,008 blood donations using Roche immunoassays (SARS-CoV-2 nucleocapsid total antibody and anti-SARS-CoV-2 S) and another that tested 214,780 patient samples using Abbott immunoassays (SARS-CoV-2 IgG and anti-SARS-CoV-2 S). Comparing datasets, seropositivity for antibodies against nucleocapsid (anti-N) diverged after May 2022 due to differential loss of sensitivity as a function of time since infection. The commonly used Rogen-Gladen adjustment did not reduce this divergence. Regression-based adjustments using the assays’ semi-quantitative results produced more similar estimates of anti-N seroprevalence and rolling incidence proportion (proportion of individuals infected in recent months). Seropositivity for antibodies targeting SARS-CoV-2 spike protein was similar without adjustment, and concordance was not improved when applying an alternative, functional threshold. These findings suggest that assay performance substantially impacted population inferences from SARS-CoV-2 serosurveillance studies in the Omicron period. Unlike methods that ignore time-varying assay sensitivity, regression-based methods using the semi-quantitative assay resulted in increased concordance in estimated anti-N seropositivity and rolling incidence between cohorts using different assays.
2025-07-22
American Journal of Epidemiology (publié)
doi.org
The impact of statistical adjustment for assay performance on inferences from SARS-CoV-2 serological surveillance studies
Jiacheng Chen
Yuan Yu
Sheila F O’Brien
Carmen Charlton
Steven J. Drews
Jane M Heffernan
Amber M Smith
Yu Nakagama
Yasutoshi Kido
David Buckeridge
W Alton Russell
2025-07-22
American Journal of Epidemiology (publié)
doi.org
Corrigendum to "Child- and Proxy-reported Differences in Patient-reported Outcome and Experience Measures in Pediatric Surgery: Systematic Review and Meta-analysis" [Journal of Pediatric Surgery 60 (2025) 162172].
Zanib Nafees
Siena O'Neill
Alexandra Dimmer
Elena Guadagno
Julia Ferreira
Nancy Mayo
Dan Poenaru
2025-07-21
Journal of Pediatric Surgery (publié)
doi.org
Corrigendum to "Child- and Proxy-reported Differences in Patient-reported Outcome and Experience Measures in Pediatric Surgery: Systematic Review and Meta-analysis" [Journal of Pediatric Surgery 60 (2025) 162172].
Zanib Nafees
Siena O'Neill
Alexandra Dimmer
Elena Guadagno
Julia Ferreira
Nancy Mayo
Dan Poenaru
2025-07-21
Journal of Pediatric Surgery (publié)
doi.org
Corrigendum to "Virtual Reality for Pediatric Trauma Education - A Preliminary Face and Content Validation Study" [Journal of Pediatric Surgery 60 (2025) 161951].
F. Botelho
Said Ashkar
Shreenik Kundu
Tj Matthews
Elena Guadagno
Dan Poenaru
2025-07-21
Journal of Pediatric Surgery (publié)
doi.org
Corrigendum to "Virtual Reality for Pediatric Trauma Education - A Preliminary Face and Content Validation Study" [Journal of Pediatric Surgery 60 (2025) 161951].
F. Botelho
Said Ashkar
Shreenik Kundu
Tj Matthews
Elena Guadagno
Dan Poenaru
2025-07-21
Journal of Pediatric Surgery (publié)
doi.org
Telomere-to-telomere assembly detects genomic diversity in Canadian strains of Borrelia burgdorferi
Atia B. Amin
Ana Victoria Ibarra Meneses
Simon Gagnon
Georgi Merhi
Martin Olivier
Momar Ndao
Mathieu Blanchette
Christopher Fernandez-Prada
David Langlais
2025-07-20
bioRxiv (prépublication)
doi.org
Employing Machine Learning to Predict Medical Trainees’ Psychophysiological Responses and Self- and Socially- Shared Regulated Learning Strategies While Completing Medical Simulations
Matthew Moreno
Keerat Grewal
Maria Cutumisu
Jason M. Harley
2025-07-18
Educational Psychology Review (publié)
doi.org
Enhancing STED Microscopy via Fluorescence Lifetime Unmixing and Filtering in Two-Species SPLIT-STED
Andréanne Deschênes
Antoine Ollier
Marie Lafontaine
Albert Michaud-Gagnon
Jeffrey-Gabriel Steavan Santiague
Anthony Bilodeau
Christian Gagné
Paul De Koninck
Flavie Lavoie-Cardinal
2025-07-18
bioRxiv (prépublication)
doi.org
Quantification of head and neck cancer patients'anatomical changes during radiotherapy: prediction of replanning need
Odette Rios-Ibacache
James Manalad
Kayla O'Sullivan‐Steben
Emily Poon
Luc Galarneau
Julia Khriguian
Georges Shenouda
John Kildea
2025-07-18
ArXiv (prépublication)
arxiv.org
arxiv.org
Adaptive Resolution Residual Networks — Generalizing Across Resolutions Easily and Efficiently
Léa Demeule
Mahtab Sandhu
Glen Berseth
The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, most deep learning arc… (voir plus)hitectures are fixed-resolution; they consider a single resolution at training and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. In contrast, other deep learning architectures are adaptive-resolution; they directly allow various resolutions to be processed at training and inference time. This provides computational adaptivity but either sacrifices robustness or compatibility with mainstream layers, which hinders their use. In this work, we introduce Adaptive Resolution Residual Networks (ARRNs) to surpass this tradeoff. We construct ARRNs from Laplacian residuals, which serve as generic adaptive-resolution adapters for fixed-resolution layers. We use smoothing filters within Laplacian residuals to linearly separate input signals over a series of resolution steps. We can thereby skip Laplacian residuals to cast high-resolution ARRNs into low-resolution ARRNs that are computationally cheaper yet numerically identical over low-resolution signals. We guarantee this result when Laplacian residuals are implemented with perfect smoothing kernels. We complement this novel component with Laplacian dropout, which randomly omits Laplacian residuals during training. This regularizes for robustness to a distribution of lower resolutions. This also regularizes for numerical errors that may occur when Laplacian residuals are implemented with approximate smoothing kernels. We provide a solid grounding for the advantageous properties of ARRNs through a theoretical analysis based on neural operators, and empirically show that ARRNs embrace the challenge posed by diverse resolutions with computational adaptivity, robustness, and compatibility with mainstream layers.
2025-07-17
TMLR (accepté)
openreview.net
openreview.net
Convergence of regularized agent-state based Q-learning in POMDPs
Amit Sinha
Matthieu Geist
Aditya Mahajan
In this paper, we present a framework to understand the convergence of commonly used Q-learning reinforcement learning algorithms in practic… (voir plus)e. Two salient features of such algorithms are: (i) the Q-table is recursively updated using an agent state (such as the state of a recurrent neural network) which is not a belief state or an information state and (ii) policy regularization is often used to encourage exploration and stabilize the learning algorithm. We investigate the simplest form of such Q-learning algorithms which we call regularized agent-state based Q-learning (RASQL) and show that it converges under mild technical conditions to the fixed point of an appropriately defined regularized MDP, which depends on the stationary distribution induced by the behavioral policy. We also show that a similar analysis continues to work for a variant of RASQL that learns periodic policies. We present numerical examples to illustrate that the empirical convergence behavior matches with the proposed theoretical limit.
2025-07-17
EWRL/2025/Workshop (poster)
openreview.net
openreview.net