Publications

High-effect gene-coding variants impact cognition, mental well-being, and neighborhood safety substrates in brain morphology
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… (see more)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, environment, human health, daily living skills and outcomes. Hence, we interrogated the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and corresponding implications on cognitive, behavioral, psychosocial, and socioeconomic traits. Specifically, we designed a holistic pattern-learning algorithm that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 962 behavioral variables spanning 20 categories in 7,657 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks, and specific parameters of the family well-being (increased parental and child stress, anxiety and depression) or neighborhood dynamics (decreased safety); effects extending beyond the impairment of cognitive ability or language capacity, dominantly reported in the CNV literature. Our investigation thus spotlights a far-reaching interplay between genetic variation and subjective life quality in adolescents and their families.
2024-05-21
medRxiv (published)
doi.org
Static graph approximations of dynamic contact networks for epidemic forecasting
Razieh Shirzadkhani
Shenyang Huang
Abby Leung
Reihaneh Rabbany
Epidemic modeling is essential in understanding the spread of infectious diseases like COVID-19 and devising effective intervention strategi… (see more)es to control them. Recently, network-based disease models have integrated traditional compartment-based modeling with real-world contact graphs and shown promising results. However, in an ongoing epidemic, future contact network patterns are not observed yet. To address this, we use aggregated static networks to approximate future contacts for disease modeling. The standard method in the literature concatenates all edges from a dynamic graph into one collapsed graph, called the full static graph. However, the full static graph often leads to severe overestimation of key epidemic characteristics. Therefore, we propose two novel static network approximation methods, DegMST and EdgeMST, designed to preserve the sparsity of real world contact network while remaining connected. DegMST and EdgeMST use the frequency of temporal edges and the node degrees respectively to preserve sparsity. Our analysis show that our models more closely resemble the network characteristics of the dynamic graph compared to the full static ones. Moreover, our analysis on seven real-world contact networks suggests EdgeMST yield more accurate estimations of disease dynamics for epidemic forecasting when compared to the standard full static method.
2024-05-21
Scientific Reports (published)
doi.org
Investigation of dosimetric characteristics of radiochromic film in response to alpha particles emitted from Americium-241.
Victor D. Diaz‐Martinez
Mélodie Cyr
S. Devic
Nada Tomic
David F. Lewis
S. Enger
BACKGROUND In radiotherapy, it is essential to deliver prescribed doses to tumors while minimizing damage to surrounding healthy tissue. Acc… (see more)urate measurements of absorbed dose are required for this purpose. Gafchromic® external beam therapy (EBT) radiochromic films have been widely used in radiotherapy. While the dosimetric characteristics of the EBT3 model film have been extensively studied for photon and charged particle beams (protons, electrons, and carbon ions), little research has been done on α
2024-05-19
Medical Physics (Lancaster) (published)
doi.org
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Milos Nikolic
Ghouthi Boukli hacene
Ciaran Bannon
Alberto Delmas Lascorz
Matthieu Courbariaux
Omar Mohamed Awad
Yoshua Bengio
Isak Edo Vivancos
Vincent Gripon
Andreas Moshovos
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time … (see more)and energy benefits on existing hardware designs that support short bitlengths. However, the question of finding the minimum bitlength for a desired accuracy remains open. We introduce a training method for minimizing inference bitlength at any granularity while maintaining accuracy. Namely, we propose a regularizer that penalizes large bitlength representations throughout the architecture and show how it can be modified to minimize other quantifiable criteria, such as number of operations or memory footprint. We demonstrate that our method learns thrifty representations while maintaining accuracy. With ImageNet, the method produces an average per layer bitlength of 4.13, 3.76 and 4.36 bits on AlexNet, ResNet18 and MobileNet V2 respectively, remaining within 2.0%, 0.5% and 0.5% of the base TOP-1 accuracy.
2024-05-18
2024 IEEE International Symposium on Circuits and Systems (ISCAS) (published)
doi.org
arxiv.org
Coordination among leaf and fine-root traits along a strong natural soil fertility gradient
Xavier Guilbeault-Mayers
Hans Lambers
Étienne Laliberté
2024-05-17
Plant and Soil (published)
doi.org
Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data
Marcelin Joanis
Andrea Lodi
Igor Sadoune
2024-05-17
Computational Economics (published)
doi.org
arxiv.org
Automatic segmentation of Organs at Risk in Head and Neck cancer patients from CT and MRI scans
Sébastien Quetin
Andrew Heschl
Mauricio Murillo
Rohit Murali
S. Enger
Farhad Maleki
Background and purpose: Deep Learning (DL) has been widely explored for Organs at Risk (OARs) segmentation; however, most studies have focus… (see more)ed on a single modality, either CT or MRI, not both simultaneously. This study presents a high-performing DL pipeline for segmentation of 30 OARs from MRI and CT scans of Head and Neck (H&N) cancer patients. Materials and methods: Paired CT and MRI-T1 images from 42 H&N cancer patients alongside annotation for 30 OARs from the H&N OAR CT&MR segmentation challenge dataset were used to develop a segmentation pipeline. After cropping irrelevant regions, rigid followed by non-rigid registration of CT and MRI volumes was performed. Two versions of the CT volume, representing soft tissues and bone anatomy, were stacked with the MRI volume and used as input to an nnU-Net pipeline. Modality Dropout was used during the training to force the model to learn from the different modalities. Segmentation masks were predicted with the trained model for an independent set of 14 new patients. The mean Dice Score (DS) and Hausdorff Distance (HD) were calculated for each OAR across these patients to evaluate the pipeline. Results: This resulted in an overall mean DS and HD of 0.777 +- 0.118 and 3.455 +- 1.679, respectively, establishing the state-of-the-art (SOTA) for this challenge at the time of submission. Conclusion: The proposed pipeline achieved the best DS and HD among all participants of the H&N OAR CT and MR segmentation challenge and sets a new SOTA for automated segmentation of H&N OARs.
2024-05-16
ArXiv (preprint)
doi.org
arxiv.org
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Holly Steach
Siddharth Viswanath
Yixuan He
Xitong Zhang
Natalia Ivanova
Matthew Hirn
Michael Perlmutter
Smita Krishnaswamy
2024-05-16
Lecture Notes in Computer Science (published)
doi.org
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Liam Hodgson
Yue Li
Yasser Iturria-Medina
Jo Anne Stratton
Guy Wolf
Smita Krishnaswamy
David A. Bennett
Danilo Bzdok
Late onset Alzheimer’s disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface… (see more). AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain’s major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject’s progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci. Designing a supervised latent factor framework for snRNA-seq human brain, the authors find distinct Alzheimer’s-predictive gene modules across celltypes, suggesting subcelltype disease progression trajectories.
2024-05-16
Communications Biology (published)
doi.org
Cognitive, interpersonal, and intrapersonal deeper learning domains: A systematic review of computational thinking
Hao-Yue Jin
Maria Cutumisu
2024-05-15
Education and Information Technologies : Official Journal of the IFIP technical committee on Education (published)
doi.org
Data Selection for Transfer Unlearning
Nazanin Mohammadi Sepahvand
Vincent Dumoulin
Eleni Triantafillou
Gintare Karolina Dziugaite
2024-05-15
ArXiv (preprint)
doi.org
arxiv.org
Sustained IFN signaling is associated with delayed development of SARS-CoV-2-specific immunity
Elsa Brunet-Ratnasingham
Sacha Morin
Haley E. Randolph
Marjorie Labrecque
Justin Bélair
Raphaël Lima-Barbosa
Amélie Pagliuzza
Lorie Marchitto
Michael Hultström
Julia Niessl
Rose Cloutier
Alina M. Sreng Flores
Nathalie Brassard
Mehdi Benlarbi
Jérémie Prévost
Shilei Ding
Sai Priya Anand
Gérémy Sannier
Anders Larsson
Dick Wågsäter … (see 27 more)
Eric Bareke
Hugo Zeberg
Miklos Lipcsey
Robert Frithiof
Anders Larsson
Sirui Zhou
Tomoko Nakanishi
David Morrison
Dani Vezina
Catherine Bourassa
Gabrielle Gendron-Lepage
Halima Medjahed
Floriane Point
Jonathan Richard
Catherine Larochelle
Alexandre Prat
Elsa Brunet-Ratnasingham
Nathalie Arbour
Madeleine Durand
J Brent Richards
Kevin Moon
Nicolas Chomont
Andrés Finzi
Martine Tétreault
Luis Barreiro
Guy Wolf
Daniel E. Kaufmann
Plasma RNAemia, delayed antibody responses and inflammation predict COVID-19 outcomes, but the mechanisms underlying these immunovirological… (see more) patterns are poorly understood. We profile 782 longitudinal plasma samples from 318 hospitalized patients with COVID-19. Integrated analysis using k-means reveals four patient clusters in a discovery cohort: mechanically ventilated critically-ill cases are subdivided into good prognosis and high-fatality clusters (reproduced in a validation cohort), while non-critical survivors segregate into high and low early antibody responders. Only the high-fatality cluster is enriched for transcriptomic signatures associated with COVID-19 severity, and each cluster has distinct RBD-specific antibody elicitation kinetics. Both critical and non-critical clusters with delayed antibody responses exhibit sustained IFN signatures, which negatively correlate with contemporaneous RBD-specific IgG levels and absolute SARS-CoV-2-specific B and CD4+ T cell frequencies. These data suggest that the “Interferon paradox” previously described in murine LCMV models is operative in COVID-19, with excessive IFN signaling delaying development of adaptive virus-specific immunity.
2024-05-15
Nature Communications (published)
doi.org