Publications

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

2025-07-20

Journal of Pediatric Surgery (published)

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

2025-07-20

Journal of Pediatric Surgery (published)

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 L Charlton

Steven J Drews

Jane M Heffernan

Amber M Smith

Yu Nakagama

Yasutoshi Kido

David L Buckeridge

W Alton Russell

Abstract Choice of immunoassay influences population seroprevalence estimates. Post hoc adjustments for assay performance could improve comp… (see more)arability of estimates across studies and enable pooled analyses. We assessed post hoc adjustment methods using data from 2021 to 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 Rogan-Gladen adjustment did not reduce this divergence. Regression-based adjustments using the assays’ semiquantitative 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 semiquantitative assay resulted in increased concordance in estimated anti-N seropositivity and rolling incidence between cohorts using different assays.

2025-07-18

American Journal of Epidemiology (published)

doi.org

Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings

Kang Jin

Zuobai Zhang

Ke Zhang

Francesca Viggiani

Claire Callahan

Jian Tang

Bruce J. Aronow

Jian Shu

Single-cell spatial transcriptomics such as in-situ hybridization or sequencing technologies can provide subcellular resolution that enables… (see more) the identification of individual cell identities, locations, and a deep understanding of subcellular mechanisms. However, accurate segmentation and annotation that allows individual cell boundaries to be determined remains a major challenge that limits all the above and downstream insights. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn latent representations of spatial colocalization relationships. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. Graph embeddings for the cell annotation are transferred as a component of multi-modal input for cell segmentation, which is employed to enrich gene relationships throughout the process. To evaluate performance, we benchmarked Bering with state-of-the-art methods and observed significant improvement in cell segmentation accuracies and numbers of detected transcripts across various spatial technologies and tissues. To streamline segmentation processes, we constructed expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation, demonstrating the generalizability of Bering.

2025-07-17

Nature Communications (published)

doi.org

Dynamical model and geometric insights in the discontinuity theory of immunity

Christian Mauffette Denis

Victoria Mochulska

Maya Dagher

Vincent Verbavatz

François X.P. Bourassa

Grégoire Altan-Bonnet

Paul François

The immune system’s most basic task is to decide what is “self” and “non-self”, but a precise definition of self versus non-self r… (see more)emains challenging. According to the discontinuity theory of immunity, effector responses depend on how quickly an antigenic stimulus changes: rapid change triggers an immune response, whereas gradual change fosters tolerance. We present a model of adaptive immune dynamics including T cells, Tregs and cytokines that reproduces the hallmarks of the discontinuity theory. The model allows for sharp discrimination between acute and chronic infections based on the growth rate of the immune challenge, and vaccination-like acute dynamics upon presentation of a bolus of immune challenge. We further show that the model behavior only depends on a handful of testable assumptions that we map to geometric constraints in phase space. This suggests that the model properties are generic and robust across alternative mechanistic details. We also examine the impact of multiple concurrent immune challenges in this model, and demonstrate the occurrence of dynamical antagonism, wherein, in some parameter regimes, slow-growing challenges hinder acute responses to fast-growing ones, with further counter-intuitive behaviors for sequential co-infections. Together, these results place the discontinuity theory on firm mathematical footing and encourage further investigation of interferences of multi-agent immune challenges, from chronic viral co-infections to cancer immunoediting.

2025-07-17

bioRxiv (preprint)

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

2025-07-17

Educational Psychology Review (published)

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

2025-07-17

bioRxiv (preprint)

doi.org

A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B. T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Danilo Bzdok

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging-genetics field has st… (see more)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, brain architecture, environment, human health and daily living skills. Here we interrogate the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and their corresponding implications on cognitive, behavioural, psychosocial and socioeconomic traits. We design a holistic pattern-learning framework that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 938 behavioural variables spanning 20 categories in 7,338 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks and specific parameters of the family wellbeing, including increased parental and child stress, anxiety and depression, or neighbourhood dynamics such as decreased safety. We thus find effects extending beyond the impairment of cognitive ability or language capacity which have been previously reported. Our investigation spotlights the interplay between genetic variation and subjective life quality in adolescents and their families.

2025-07-17

Nature Biomedical Engineering (published)

doi.org

A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B. T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Danilo Bzdok

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging-genetics field has st… (see more)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, brain architecture, environment, human health and daily living skills. Here we interrogate the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and their corresponding implications on cognitive, behavioural, psychosocial and socioeconomic traits. We design a holistic pattern-learning framework that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 938 behavioural variables spanning 20 categories in 7,338 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks and specific parameters of the family wellbeing, including increased parental and child stress, anxiety and depression, or neighbourhood dynamics such as decreased safety. We thus find effects extending beyond the impairment of cognitive ability or language capacity which have been previously reported. Our investigation spotlights the interplay between genetic variation and subjective life quality in adolescents and their families.

2025-07-17

Nature Biomedical Engineering (published)

doi.org

On the Fundamental Limitations of Dual Static CVaR Decompositions in Markov Decision Processes

Mathieu Godbout

Audrey Durand

2025-07-17

ArXiv (preprint)

doi.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… (see more)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.

2025-07-16

Transactions on Machine Learning Research (accepted)

doi.org

openreview.net

Autoregressive Speech Enhancement via Acoustic Tokens

Luca Della Libera

Yusuf Cem Sübakan

Mirco Ravanaelli

2025-07-16

ArXiv (preprint)

doi.org

arxiv.org