Publications

Antibody discovery technology: innovation and outlook from classic to leading edge
Jian Tang
PengFei WANG
,
2026-03-19
Scientia Sinica Vitae (publié)
doi.org
Ca2+ transient detection and segmentation with the Astronomically motivated algorithm for Background Estimation And Transient Segmentation (Astro-BEATS)
Bi Fan
Anthony Bilodeau
Frédéric Beaupré
Theresa Wiesner
Christian Gagné
Flavie Lavoie-Cardinal
Renée Hložek
Fluorescence-based Ca…
2026-03-18
arXiv (prépublication)
doi.org
Distinct SMA beta bursts support the development of anticipatory postural control in children
Viktoriya O. Manyukhina
Fanny Barlaam
Judith Vergne
Anaëlle Bain
Oussama Abdoun
Sébastien Daligault
Claude Delpuech
Karim Jerbi
Sandrine Sonié
Mathilde Bonnefond
C. Schmitz
Abstract To compensate for self-generated movement-induced postural disturbances, the brain generates anticipatory postural adjustments (APA… (voir plus)), ensuring smooth, coordinated actions. APA development continues into late adolescence, yet the specific pathways and mechanisms that remain immature in children are poorly understood. We studied APA mechanisms in 24 children (7-12 years old) using magnetoencephalography (MEG) while they performed the naturalistic bimanual load-lifting task (BLLT). In the BLLT, participants lift a load placed on one forearm with the contralateral hand while keeping the postural forearm horizontal, as if lifting a glass from a tray. To counteract forearm deflection caused by unloading, the brain generates APAs, which involve anticipatory inhibition of the postural Biceps brachii . We found that stronger anticipatory Biceps brachii inhibition was associated with reduced excitability, as indexed by high-gamma (90-130 Hz) suppression, and increased high-beta power (19-29 Hz) in the contralateral Supplementary Motor Area (SMA). Analysis of transient beta events revealed two functionally distinct burst types: (1) 19-24 Hz bursts: time-locked to immediate high-gamma suppression correlated with 26-28 Hz beta power; predicted stronger muscle inhibition and received directed input from middle frontal cortex and precentral gyrus; (2) 24-29 Hz bursts: linked to delayed (∼100 ms) high-gamma suppression correlated with 8 Hz alpha power; predicted earlier and prolonged muscle inhibition and better forearm stabilization, but did not show directional influence from other regions. Results on anticipatory inhibition-related beta bursts replicated mechanisms reported in adults, suggesting that the efferent pathways and transient inhibitory processes underlying APA may already be mature in children. In contrast, higher-frequency beta bursts revealed a child-specific, complementary APA mechanism that may compensate for imprecise anticipatory inhibition. These results reveal two oscillatory mechanisms supporting APA in children and indicate that beta bursts may reflect both immediate cortical inhibition linked to muscle control and indirect alpha-mediated inhibition likely compensating for forearm instability.
2026-03-18
bioRxiv (prépublication)
doi.org
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
Jack Yi Wei
Narges Armanfard
Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to an… (voir plus)omaly-free training samples, fully unsupervised settings with unlabeled and potentially contaminated training data, and semi-supervised settings with limited anomaly labels. Existing deep learning approaches typically train dataset-specific models under the assumption of a single supervision regime, which limits their ability to leverage shared structures across anomaly detection tasks and to adapt to different supervision levels. We propose ICLAD, an in-context learning foundation model for tabular anomaly detection that generalizes across both datasets and supervision regimes. ICLAD is trained via meta-learning on synthetic tabular anomaly detection tasks, and at inference time, the model assigns anomaly scores by conditioning on the training set without updating model weights. Comprehensive experiments on 57 tabular datasets from ADBench show that our method achieves state-of-the-art performance across three supervision regimes, establishing a unified framework for tabular anomaly detection.
2026-03-18
arXiv (prépublication)
doi.org
arxiv.org
Listen First, Then Answer: Timestamp-Grounded Speech Reasoning
Jihoon Jeong
Pooneh Mousavi
Mirco Ravanelli
Cem Subakan
Large audio-language models (LALMs) can generate reasoning chains for their predictions, but it remains unclear whether these reasoning chai… (voir plus)ns remain grounded in the input audio. In this paper, we propose an RL-based strategy that grounds the reasoning outputs of LALMs with explicit timestamp annotations referring to relevant segments of the audio signal. Our analysis shows that timestamp grounding leads the model to attend more strongly to audio tokens during reasoning generation. Experiments on four speech-based benchmark datasets demonstrate that our approach improves performance compared to both zero-shot reasoning and fine-tuning without timestamp grounding. Additionally, grounding amplifies desirable reasoning behaviors, such as region exploration, audiology verification, and consistency, underscoring the importance of grounding mechanisms for faithful multimodal reasoning.
2026-03-18
arXiv (prépublication)
doi.org
arxiv.org
Additional file 1 of Beta power as a neural correlate of sensory features in autistic individuals
J. Chaudet
Julien Pichot
Amandine Pedoux
Mathis Fleury
Anna Maruani
Valérie Vantalon
Elise Humeau
Thomas Bourgeron
Josselin Houenou
Guillaume Dumas
Edouard Duchesnay
Richard Delorme
Anton Iftimovici
Aline Lefebvre
Supplementary Material 1.
2026-03-17
Figshare (inconnu)
doi.org
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
Liang Shi
Jiarui Lu
Junqi Liu
Chence Shi
Zhi Yang
Jian Tang
Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While … (voir plus)Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Conversely, recent deep generative models accelerate conformation generation but are typically either failing to model temporal relationship or built only for monomeric proteins. To bridge this gap, we introduce ATMOS, a novel generative framework based on State Space Models (SSM) designed to generate atom-level MD trajectories for biomolecular systems. ATMOS integrates a Pairformer-based state transition mechanism to capture long-range temporal dependencies, with a diffusion-based module to decode trajectory frames in an autoregressive manner. ATMOS is trained across crystal structures from PDB and conformation trajectory from large-scale MD simulation datasets including mdCATH and MISATO. We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and complex protein-ligand systems. By enabling efficient inference of atomic trajectory of motions, this work establishes a promising foundation for modeling biomolecular dynamics.
2026-03-17
arXiv (prépublication)
doi.org
arxiv.org
Beta power as a neural correlate of sensory features in autistic individuals
Julie Chaudet
Julien Pichot
Amandine Pedoux
Mathis Fleury
Anna Maruani
Valérie Vantalon
Elise Humeau
Thomas Bourgeron
Josselin Houenou
Guillaume Dumas
Edouard Duchesnay
Richard Delorme
Anton Iftimovici
Aline Lefebvre
2026-03-17
Journal of Neurodevelopmental Disorders (publié)
doi.org
CETP alternative splicing variation impacts human traits
Isabel Gamache
Marc‐André Legault
Jean‐Christophe Grenier
Éric Rhéaume
J.C. Tardif
Marie‐Pierre Dubé
Julie Hussin
Abstract The cholesteryl ester transfer protein (CETP) is an important protein in reverse cholesterol transport and has been identified as a… (voir plus) significant factor associated with cardiovascular disease (CVD), making it a widely studied pharmaceutical target. Three protein-coding isoforms of CETP exist, distinguished by the alternative splicing of one exon each. The isoform primarily responsible for cholesterol-related functions in the plasma is well studied, but specific functions of each isoform remain poorly understood. In this study, we demonstrate the significance of considering CETP’s isoforms in analyses of human traits. Using bulk RNA-seq data from multiple tissues, we characterized the expression patterns and genetic regulation determinants of CETP transcripts. Leveraging publicly available GWAS summary statistics, we conducted multivariable Mendelian Randomisation (MVMR) to estimate the impact of variation in isoform proportions on phenotypes, highlighting the importance of CETP’s isoforms in pituitary and thyroid glands. Furthermore, we uncovered tissue-specific associations between CETP’s isoforms and CVD-associated phenotypes. Additionally, we observed that the epistatic interaction previously reported between CETP and ADCY9 , a gene implicated in modulating a CETP modulator’s response, may be mediated through the regulation of alternative splicing of exon 9. Our results underscore the importance of a comprehensive understanding of CETP’s isoforms, which can significantly impact both fundamental and clinical research efforts.
2026-03-17
bioRxiv (prépublication)
doi.org
Cough acoustic analysis using artificial intelligence for COVID-19 detection: A comparative study of patient cohorts from Lima, Peru and Montreal, Canada
A. Zimmer
Vijay Ravi
Patricia Espinoza-Lopez
George P. Kafentzis
Mirco Ravanelli
Samira Abbasgholizadeh Rahimi
Madhukar Pai
Cesar Ugarte-Gil
Serge Lapierre
2026-03-17
Annals of Epidemiology (publié)
doi.org
MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
Philippe Formont
Maxime Darrin
Ismail Ben Ayed
Pablo Piantanida
Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex problem-solving tasks.… (voir plus) Motivated by these advances, several works have explored the application of reasoning LLMs to drug discovery and molecular design. However, most existing approaches either focus on evaluation or rely on training setups that require ground-truth labels, such as molecule pairs with known property modifications. Such supervision is unavailable in \textit{de novo} molecular generation, where the objective is to generate novel molecules that optimize a desirability score without prior knowledge of high-scoring candidates. To bridge this gap, we introduce MolRGen, a large-scale benchmark and dataset for training and evaluating reasoning-based LLMs on \textit{de novo} molecular generation. Our contributions are threefold. First, we propose a setting to evaluate and train models for \textit{de novo} molecular generation and property prediction. Second, we introduce a novel diversity-aware top-
2026-03-17
arXiv (prépublication)
doi.org
arxiv.org
Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs with Unbounded Costs
Abhishek Gupta
Aditya Mahajan
Markov decision processes (MDPs) is viewed as an optimization of an objective function over certain linear operators over general function s… (voir plus)paces. Using the well-established perturbation theory of linear operators, this viewpoint allows one to identify derivatives of the objective function as a function of the linear operators. This leads to generalization of many well-known results in reinforcement learning to cases with generate state and action spaces. Prior results of this type were only established in the finite-state finite-action MDP settings and in settings with certain linear function approximations. The framework also leads to new low-complexity PPO-type reinforcement learning algorithms for general state and action space MDPs.
2026-03-17
arXiv (prépublication)
doi.org
arxiv.org