Publications

Reference radiation selection is confirmed as a significant source of relative biological effectiveness variation for neutrons.

Laura C Paterson

Stephen Pecoskie

Farrah Norton

Norma Ybarra

J. Kildea

Richard B Richardson

2025-12-08

International Journal of Radiation Biology (publié)

doi.org

Cell-type specific transcriptional modulation by psilocybin induces sustained plasticity in mouse medial prefrontal cortex

Heike Schuler

Delong Zhou

Chloé Savignac

Vedrana Cvetkovska

Yiu-Chung Tse

Juliet Meccia

Joëlle Lopez

Ashot S. Harutyunyan

Jiannis Ragoussis

Danilo Bzdok

Rosemary C. Bagot

Despite enormous interest in psychedelics for psychiatric interventions, potential underlying biological mechanisms remain unclear. Here, we… (voir plus) confirm that a single dose of psilocybin increases synaptic transmission in mouse medial prefrontal cortex. Using scRNA-sequencing, we identify cell-type specific mechanisms of sustained neuroplastic effects. We show that, 24h post-psilocybin, expression of plasticity-related genes is increased in excitatory neurons and that transcription in a type of deep layer near projecting neuron, L5/6 NP, is robustly altered. Analyzing receptor expression patterns reveals that this cell-type specificity does not align with 5-HT 2A expression but aligns with 5-HT 2C expression patterns. Further, multivariate analyses identify psilocybin-induced gene expression patterns in L5/6 NP neurons predict 5-HT 2C , but not 5-HT 2A , transcript levels. Pharmacologic manipulation with a 5-HT 2C antagonist attenuates the post-acute sustained effect of psilocybin on synaptic transmission, highlighting 5-HT 2C signaling and L5/6 NP neurons as key mediators of psychedelic drug action’s sustained neuroplastic effects in mPFC.

2025-12-07

bioRxiv (prépublication)

doi.org

SpaTM: Topic Models for Inferring Spatially Informed Transcriptional Programs

Adrien Osakwe

Wenqi Dong

Qihuang Zhang

Robert Sladek

Yuemei Li

Spatial transcriptomics has revolutionized our ability to characterize tissues and diseases by contextualizing gene expression with spatial … (voir plus)organization. Available methods require researchers to either train a model using histology-based annotations or use annotation-free clustering approaches to uncover spatial domains. However, few methods provide researchers with a way to jointly analyze spatial data from both annotation-free and annotation-guided perspectives using consistent inductive biases and levels of interpretability. A single framework with consistent inductive biases ensures coherence and transferability across tasks, reducing the risks of conflicting assumptions. To this end, we propose the Spatial Topic Model (SpaTM), a topic-modeling framework capable of annotation-guided and annotation-free analysis of spatial transcriptomics data. SpaTM can be used to learn gene programs that represent histology-based annotations while providing researchers with the ability to infer spatial domains with an annotation-free approach if manual annotations are limited or noisy. We demonstrate SpaTM’s interpretability with its use of topic mixtures to represent cell states and transcriptional programs and how its intuitive framework facilitates the integration of annotation-guided and annotation-free analyses of spatial data with downstream analyses such as cell type deconvolution. Finally, we demonstrate how both approaches can be used to extend the analysis of large-scale snRNA-seq atlases with the inference of cell proximity and spatial annotations in human brains with Major Depressive Disorder.

2025-12-07

Briefings in Bioinformatics (publié)

doi.org

International AI Safety Report Second Key Update: Technical Safeguards and Risk Management

Yoshua Bengio

Stephen Clare

Carina Prunkl

Maksym Andriushchenko

BEN BUCKNALL

Philip Fox

Nestor Maslej

Conor McGlynn

Malcolm Murray

Shalaleh Rismani

Stephen Casper

Jessica Newman

Daniel Privitera

Sören Mindermann

Daron Acemoglu

Thomas G. Dietterich

Fredrik Heintz

Geoffrey Hinton

Nick Jennings

Susan Leavy … (voir 17 de plus)

Teresa Ludermir

Vidushi Marda

Helen Margetts

John McDermid

Jane Munga

Arvind Narayanan

Alondra Nelson

Clara Neppel

Sarvapali D. (Gopal) Ramchurn

Stuart Russell

Marietje Schaake

Bernhard Schölkopf

Alvaro Soto

Lee Tiedrich

Gael Varoquaux

Andrew Yao

Ya-Qin Zhang

This is the Second Key Update to the 2025 International AI Safety Report. The First Key Update (1) discussed developments in the capabilitie… (voir plus)s of general-purpose AI models and systems and associated risks. This Key Update covers how various actors, including researchers, companies, and governments, are approaching risk management and technical mitigations for AI. The past year has seen important developments in AI risk management, including better techniques for training safer models and monitoring their outputs. While this represents tangible progress, significant gaps remain. It is often uncertain how effective current measures are at preventing harms, and effectiveness varies across time and applications. There are many opportunities to further strengthen existing safeguard techniques and to develop new ones. This Key Update provides a concise overview of critical developments in risk management practices and technical risk mitigation since the publication of the 2025 AI Safety Report in January. It highlights where progress is being made and where gaps remain. Above all, it aims to support policymakers, researchers, and the public in navigating a rapidly changing environment, helping them to make informed and timely decisions about the governance of general-purpose AI. Professor Yoshua BengioUniversité de Montréal / LawZero /Mila – Quebec AI Institute & Chair

2025-12-06

SuperIntelligence - Robotics - Safety & Alignment (publié)

doi.org

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction

Emily Jin

Andrei Cristian Nica

Mikhail Galkin

Jarrid Rector-Brooks

Kin Long Kelvin Lee

Santiago Miret

Frances H. Arnold

Michael M. Bronstein

Avishek Bose

Alexander Tong

Cheng-Hao Liu

2025-12-06

ArXiv (prépublication)

doi.org

arxiv.org

Large Language Model Applications in the Algebra Domain: A Systematic Review

Yajie Song

Yimei Zhang

Doina Precup

Reihaneh Rabbany

Maria Cutumisu

2025-12-05

Technology, Knowledge and Learning (publié)

doi.org

Securing the Model Context Protocol: Defending LLMs Against Tool Poisoning and Adversarial Attacks

Saeid Jamshidi

Kawser Wazed Nafi

Arghavan Moradi Dakhel

Negar Shahabi

Foutse Khomh

Naser Ezzati-Jivan

2025-12-05

ArXiv (prépublication)

doi.org

arxiv.org

Adsorption energies are necessary but not sufficient to identify good catalysts

Alexander Davis

Alexandre AGM Duval

Oleksandr Voznyy

Alex Hern'andez-Garcia

David Rolnick

Félix Therrien

2025-12-04

ArXiv (prépublication)

arxiv.org

Evaluation and improvement of algorithmic fairness for COVID-19 severity classification using Explainable Artificial Intelligence-based bias mitigation

Shayan Nejadshamsi

Charlene H. Chu

Katherine S. McGilton

Xiaoxiao Li

Charlene Ronquillo

Samira Abbasgholizadeh-Rahimi

The COVID-19 pandemic has highlighted the growing reliance on machine learning (ML) models for predicting disease severity, which is importa… (voir plus)nt for clinical decision-making and equitable resource allocation. While achieving high predictive accuracy is important, ensuring fairness in the prediction output of these models is equally important to prevent bias-driven disparities in healthcare. This study evaluates fairness in a machine learning-based COVID-19 severity classification model and proposes an Explainable AI (XAI)-based bias mitigation strategy to address sex-related bias. Using data from the Quebec Biobank, we developed an XGBoost-based multi-class classification model. Fairness was assessed using Subset Accuracy Parity Difference (SAPD) and Label-wise Equal Opportunity Difference (LEOD) metrics. Four bias mitigation strategies were implemented and evaluated: Fair Representation Learning, Fair Classifier Using Constraints, Adversarial Debiasing, and our proposed XAI-based method utilizing SHapley Additive exPlanations (SHAP) method for feature importance analysis. The study cohort included 1642 COVID-19 positive older adults (mean age: 77.5), balance equally between males and females. The baseline (unmitigated) classification model achieved 90.68% accuracy but exhibited a 10.11% Subset Accuracy Parity Difference between sexes, indicating a relatively large bias. The introduced XAI-based method demonstrated a better trade-off between model performance and fairness compared to existing bias mitigation methods by identifying sex-sensitive feature interactions and integrating them into the model re-training. Traditional fairness interventions often compromise accuracy to a greater extent. Our XAI-based method achieves the best balance between classification performance and bias, enhancing its clinical applicability. The XAI-driven bias mitigation intervention effectively reduces sex-based disparities in COVID-19 severity prediction without the significant accuracy loss observed in traditional methods. This approach provides a framework for developing fair and accurate clinical decision support systems for older adults, which ensures equitable care in clinical risk stratification and resource allocation.

2025-12-04

JAMIA Open (publié)

doi.org

Neural Coherence : Find higher performance to out-of-distribution tasks from few samples

Simon Guiroy

Mats Richter

A. Chandar

Christopher Pal

2025-12-04

ArXiv (prépublication)

doi.org

arxiv.org

Probing the effectiveness of World Models for Spatial Reasoning through Test-time Scaling

Saurav Jha

Muhammad Jehanzeb Mirza

Wei Lin

Shiqi Yang

A. Chandar

Vision-Language Models (VLMs) remain limited in spatial reasoning tasks that require multi-view understanding and embodied perspective shift… (voir plus)s. Recent approaches such as MindJourney attempt to mitigate this gap through test-time scaling where a world model imagines action-conditioned trajectories and a heuristic verifier selects helpful views from such trajectories. In this work, we systematically examine how such test-time verifiers behave across benchmarks, uncovering both their promise and their pitfalls. Our uncertainty-based analyses show that MindJourney's verifier provides little meaningful calibration, and that random scoring often reduces answer entropy equally well, thus exposing systematic action biases and unreliable reward signals. To mitigate these, we introduce a Verification through Spatial Assertions (ViSA) framework that grounds the test-time reward in verifiable, frame-anchored micro-claims. This principled verifier consistently improves spatial reasoning on the SAT-Real benchmark and corrects trajectory-selection biases through more balanced exploratory behavior. However, on the challenging MMSI-Bench, none of the verifiers, including ours, achieve consistent scaling, suggesting that the current world models form an information bottleneck where imagined views fail to enrich fine-grained reasoning. Together, these findings chart the bad, good, and ugly aspects of test-time verification for world-model-based reasoning. Our code is available at https://github.com/chandar-lab/visa-for-mindjourney.

2025-12-04

ArXiv (prépublication)

doi.org

arxiv.org

Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction

Vincent Pauline

Kirill Neklyudov

2025-12-03