Publications

Single-Cell Multi-Omics Profiling of Immune Cells Isolated from Atherosclerotic Plaques in Male ApoE Knockout Mice Exposed to Arsenic
Kiran Makhani
Xiuhui Yang
France Dierick
Nivetha Subramaniam
Natascha Gagnon
Talin Ebrahimian
Stephanie Lehoux
Hao Wu
Jun Ding
Koren K. Mann
Millions worldwide are exposed to elevated levels of arsenic that significantly increase their risk of developing atherosclerosis, a patholo… (see more)gy primarily driven by immune cells. While the impact of arsenic on immune cell populations in atherosclerotic plaques has been broadly characterized, cellular heterogeneity is a substantial barrier to in-depth examinations of the cellular dynamics for varying immune cell populations. This study aimed to conduct single-cell multi-omics profiling of atherosclerotic plaques in apolipoprotein E knockout (ApoE–/–) mice to elucidate transcriptomic and epigenetic changes in immune cells induced by arsenic exposure. The ApoE–/– mice were fed a high-fat diet and were exposed to either 200 ppb arsenic in drinking water or a tap water control, and single-cell multi-omics profiling was performed on atherosclerotic plaque-resident immune cells. Transcriptomic and epigenetic changes in immune cells were analyzed within the same cell to understand the effects of arsenic exposure. Our data revealed that the transcriptional profile of macrophages from arsenic-exposed mice were significantly different from that of control mice and that differences were subtype specific and associated with cell–cell interaction and cell fates. Additionally, our data suggest that differences in arsenic-mediated changes in chromosome accessibility in arsenic-exposed mice were statistically more likely to be due to factors other than random variation compared to their effects on the transcriptome, revealing markers of arsenic exposure and potential targets for intervention. These findings in mice provide insights into how arsenic exposure impacts immune cell types in atherosclerosis, highlighting the importance of considering cellular heterogeneity in studying such effects. The identification of subtype-specific differences and potential intervention targets underscores the significance of understanding the molecular mechanisms underlying arsenic-induced atherosclerosis. Further research is warranted to validate these findings and explore therapeutic interventions targeting immune cell dysfunction in arsenic-exposed individuals. https://doi.org/10.1289/EHP14285
2024-12-31
Environmental Health Perspectives (published)
doi.org
Software and Data for "Capacity Planning in Stable Matching"
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Ignacio Rios
Alfredo Torrico
2024-12-31
Operational Research (published)
doi.org
Sparse Polygenic Risk Score Inference with the Spike-and-Slab LASSO
Junyi Song
Shadi Zabad
Archer Yang
Simon Gravel
Yuemei Li
2024-12-31
Bioinform. (published)
doi.org
Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy
Abdul Al-Shawwa
Michael Craig
Kalum Ost
David Anderson
Steve Casha
W Bradley Jacobs
Nathan Evaniew
Saswati Tripathy
Jacques Bouchard
Peter Lewkonia
Fred Nicholls
Alex Soroceanu
Ganesh Swamy
Kenneth C Thomas
Stephan duPlessis
Michael MH Yang
Julien Cohen-Adad
Nicholas Dea
Jefferson R Wilson
David W Cadotte
Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Clinical guidelines regarding surg… (see more)ery for patients with mild DCM and minimal symptoms remain uncertain. This study aims to identify imaging and clinical predictors of neurological deterioration in mild DCM and explore pathophysiological correlates to guide clinical decision-making. Patients with mild DCM underwent advanced MRI scans that included T2-weighted, diffusion tensor imaging and magnetisation transfer (MT) sequences, along with clinical outcome measures at baseline and 6-month intervals after enrolment. Quantitative MRI (qMRI) metrics were derived above and below maximally compressed cervical levels (MCCLs). Various machine learning (ML) models were trained to predict 6 month neurological deterioration, followed by global and local model interpretation to assess feature importance. A total of 49 patients were followed for a maximum of 2 years, contributing 110 6-month data entries. Neurological deterioration occurred in 38% of cases. The best-performing ML model, combining clinical and qMRI metrics, achieved a balanced accuracy of 83%, and an area under curve-receiver operating characteristic of 0.87. Key predictors included MT ratio (demyelination) above the MCCL in the dorsal and ventral funiculi and moderate tingling in the arm, shoulder or hand. qMRI metrics significantly improved predictive performance compared to models using only clinical (bal. acc=68.1%) or imaging data (bal. acc=57.4%). Reduced myelin content in the dorsal and ventral funiculi above the site of compression, combined with sensory deficits in the hands and gait/balance disturbances, predicts 6-month neurological deterioration in mild DCM and may warrant early surgical intervention.
2024-12-31
BMJ Neurology Open (published)
doi.org
A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Margarida Carvalho
Guy Desaulniers
2024-12-31
Int. Trans. Oper. Res. (published)
doi.org
arxiv.org
Structure–function coupling and decoupling during movie watching and resting state: Novel insights bridging EEG and structural imaging
Venkatesh Subramani
Giulia Lioi
Karim Jerbi
Nicolas Farrugia
The intricate structural and functional architecture of the brain enables a wide range of cognitive processes ranging from perception and ac… (see more)tion to higher-order abstract thinking. Despite important progress, the relationship between the brain’s structural and functional properties is not yet fully established. In particular, the way the brain’s anatomy shapes its electrophysiological dynamics remains elusive. The electroencephalography (EEG) activity recorded during naturalistic tasks is thought to exhibit patterns of coupling with the underlying brain structure that vary as a function of behavior. Yet these patterns have not yet been sufficiently quantified. We address this gap by jointly examining individual Diffusion-Weighted Imaging (DWI) scans and continuous EEG recorded during video-watching and resting state, using a Graph Signal Processing (GSP) framework. By decomposing the structural graph into Eigenmodes and expressing the EEG activity as an extension of anatomy, GSP provides a way to quantify the structure-function coupling. We elucidate how the structure shapes function during naturalistic tasks such as movie-watching and how this association is modulated by tasks. We quantify the coupling relationship in a region-, time-, frequency-resolved manner. First of all, our findings indicate that the EEG activity in the sensorimotor cortex is strongly coupled with brain structure, while the activity in higher-order systems is less constrained by anatomy, i.e., shows more flexibility. In addition, we found that watching videos was associated with stronger structure-function coupling in the sensorimotor cortex, as compared to resting-state data. Second, time-resolved analysis revealed that the unimodal systems undergo minimal temporal fluctuation in structure-function association, and the transmodal system displays highest temporal fluctuations, with the exception of PCC seeing low fluctuations. Lastly, our frequency-resolved analysis revealed a consistent topography across different EEG rhythms, suggesting a similar relationship with the anatomical structure across frequency bands. Together, this unprecedented characterization of the link between structure and function using continuous EEG during naturalistic behavior underscores the role of anatomy in shaping ongoing cognitive processes. Taken together, by combining the temporal and spectral resolution of EEG and the methodological advantages of GSP, our work sheds new light onto the anatomo-functional organization of the brain.
2024-12-31
Imaging Neuroscience (published)
doi.org
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Yoshua Bengio
Michael Cohen
Damiano Fornasiere
Joumana Ghosn
Pietro Greiner
Matt MacDermott
Sören Mindermann
Adam Oberman
Jesse Richardson
Oliver Richardson
Marc-Antoine Rondeau
Pierre-Luc St-Charles
David Williams-King
The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue go… (see more)als across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.
2024-12-31
arXiv (preprint)
doi.org
arxiv.org
A Survey of Contextual Optimization Methods for Decision-Making under Uncertainty
Utsav Sadana
Abhilash Chenreddy
Erick Delage
Alexandre Forel
Emma Frejinger
Thibaut Vidal
2024-12-31
European Journal of Operational Research (published)
doi.org
arxiv.org
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
Prateek Yadav
Colin Raffel
Mohammed Muqeeth
Lucas Caccia
Haokun Liu
Tianlong Chen
Mohit Bansal
Leshem Choshen
Alessandro Sordoni
The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particula… (see more)r domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field.
2024-12-31
Trans. Mach. Learn. Res. (published)
doi.org
openreview.net
A Systematic Review of the Empirical Use of the CCHS-MH (Canadian Community Health Survey–Mental Health) Survey
Maria Cutumisu
2024-12-31
Proceedings of the 2025 AERA Annual Meeting (published)
doi.org
Task Mapping Strategies for Electric Power System Simulations on Heterogeneous Clusters
Julie Durette
Gunes Karabulut Kurt
Antoine Lesage-Landry
In this work, we propose improved task mapping strategies for real-time electric power system simulations on heterogeneous computing cluster… (see more)s, considering both heterogeneous communication links and processing capacities, with a focus on bottleneck objectives. We approach the problem through two complementary models: the bottleneck quadratic semi-assignment problem (BQSAP), which optimizes task configuration for a fixed number of computing nodes while minimizing communication and computation costs; and the variable-size bin packing problem with quadratic communication constraints (Q-VSBPP), which minimizes the required number of computing nodes, valuable for resource provisioning scenarios. We extend the PuLP library to solve approximately both problems, explicitly including communication costs and processing constraints, and formalize the nomenclature and definitions for bottleneck objectives in graph partitioning. This formalization fills a gap in the existing literature and provides a framework for the rigorous analysis and application of task mapping techniques to real-time electric power system simulation. Finally, we provide a quantitative study and benchmark the extended PuLP library with the SCOTCH partitioning library in the context of real-time electromagnetic transient (EMT) simulation task mapping.
2024-12-31
SmartGridComm (published)
doi.org
A Text-guided Protein Design Framework
Shengchao Liu
Yutao Zhu
Yanjing Li
Zhuoxinran Li
Jiarui Lu
Zhao Xu
Weili Nie
Anthony Gitter
Chaowei Xiao
Jian Tang
Arvind Ramanathan
Hongyu Guo
Anima Anandkumar
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowled… (see more)ge curated by humans in the text format describing proteins' high-level functionalities. Yet, whether the incorporation of such text data can help protein design tasks has not been explored. To bridge this gap, we propose ProteinDT, a multi-modal framework that leverages textual descriptions for protein design. ProteinDT consists of three subsequent steps: ProteinCLAP which aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality, and a decoder that creates the protein sequences from the representation. To train ProteinDT, we construct a large dataset, SwissProtCLAP, with 441K text and protein pairs. We quantitatively verify the effectiveness of ProteinDT on three challenging tasks: (1) over 90% accuracy for text-guided protein generation; (2) best hit ratio on 12 zero-shot text-guided protein editing tasks; (3) superior performance on four out of six protein property prediction benchmarks.
2024-12-31
Nat. Mac. Intell. (published)
doi.org
arxiv.org