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 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… (voir plus)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 (publié)
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… (voir plus) 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 (publié)
doi.org
Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog
Lautaro Estienne
Gabriel Ben Zenou
Nona Naderi
Jackie Chi Kit Cheung
Pablo Piantanida
As AI systems take on collaborative roles, they must reason about shared goals and beliefs-not just generate fluent language. The Rational S… (voir plus)peech Act (RSA) framework offers a principled approach to pragmatic reasoning, but existing extensions face challenges in scaling to multi-turn, collaborative scenarios. In this paper, we introduce Collaborative Rational Speech Act (CRSA), an information-theoretic (IT) extension of RSA that models multi-turn dialog by optimizing a gain function adapted from rate-distortion theory. This gain is an extension of the gain model that is maximized in the original RSA model but takes into account the scenario in which both agents in a conversation have private information and produce utterances conditioned on the dialog. We demonstrate the effectiveness of CRSA on referential games and template-based doctor-patient dialogs in the medical domain. Empirical results show that CRSA yields more consistent, interpretable, and collaborative behavior than existing baselines-paving the way for more pragmatic and socially aware language agents.
2025-07-17
arXiv (Cornell University) (prépublication)
doi.org
arxiv.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… (voir plus)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 (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
2025-07-17
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
2025-07-17
bioRxiv (prépublication)
doi.org
A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort
Jakub Kopal
Guillaume Huguet
Justin Marotta
Shambhavi Aggarwal
Nicole Osayande
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… (voir plus)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 (publié)
doi.org
A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort
Jakub Kopal
Guillaume Huguet
Justin Marotta
Shambhavi Aggarwal
Nicole Osayande
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… (voir plus)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 (publié)
doi.org
Quantification of head and neck cancer patients' anatomical changes during radiotherapy: Toward the prediction of replanning need
Odette Rios‐Ibacache
James Manalad
Kayla O'Sullivan‐Steben
Emily Poon
Luc Galarneau
Julia Khriguian
George Shenouda
J. Kildea
Abstract Background Head and neck cancer (HNC) patients undergoing radiotherapy (RT) may experience anatomical changes during treatment, whi… (voir plus)ch can compromise the validity of the initial treatment plan, necessitating replanning. However, ad hoc replanning disrupts clinical workflows and increases workload. Currently, no standardized method exists to quantify anatomical variation that necessitates replanning. Purpose This project aimed to create geometrical metrics to describe anatomical changes in HNC patients during RT. The usefulness of these metrics was evaluated by a univariate analysis and through machine learning (ML) models to predict the need for replanning. Methods A cohort of 150 HNC patients treated at McGill University Health Centre was analyzed. Based on the shapes of the RT structures (body, PTV, mandible, neck, and submandibular contours), we developed 43 metrics and automatically calculated them through a Python pipeline that we called HNGeoNatomyX. Univariate analysis using linear regression was conducted to obtain the rate of change of each metric. We also obtained the relative variation of each metric between the pre‐treatment and replanning‐requested scans. Fraction‐specific ML models (incorporating information available up to and including the specific fraction) for fractions 5, 10, and 15 were built using metrics, clinical data, and feature selection techniques. Model performance was estimated with repeated stratified 5‐fold cross‐validation resampling technique and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results Univariate analysis showed that body‐ and neck‐related metrics were most predictive of replanning need. Our best specific multivariate models for fractions 5, 10, and 15 yielded testing scores of 0.82, 0.70, and 0.79, respectively. Our models early predicted replanning for 76% of the true positives. Conclusions The created metrics have the potential to characterize and distinguish which patients will necessitate RT replanning. They show promise in guiding clinicians to evaluate RT replanning for HNC patients and streamline workflows.
2025-07-17
ArXiv (prépublication)
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… (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.
2025-07-16
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.
2025-07-16
EWRL/2025/Workshop (poster)
openreview.net
openreview.net
Sample, Predict, then Proceed: Self-Verification Sampling for Tool Use of LLMs
Shangmin Guo
Omar Darwiche Domingues
Raphaël Avalos
Aaron Courville
Florian Strub
Tool use in stateful environments presents unique challenges for large language models (LLMs), where existing test-time compute strategies r… (voir plus)elying on repeated trials in the environment are impractical. We propose dynamics modelling (DyMo), a method that augments LLMs with a state prediction capability alongside function calling during post-training. This enables LLMs to predict the future states of their actions through an internal environment model. On the Berkeley Function Calling Leaderboard V2, DyMo improves success rates and significantly reduces hallucinations. We further integrate the internal environment model into self-verification sampling (SVS), and show that this substantially improves pass^k over number of trials k, and allows the model to refuse unreliable outputs. Together, DyMo and SVS greatly enhance the effectiveness and reliability of LLMs for tool use. We believe this work charts a path towards scalable planning RL methods for LLM inference without repeatedly querying the oracle environment.
2025-07-16
EWRL/2025/Workshop (poster)
openreview.net
openreview.net