Publications

Clinical Care Trajectory Assessment of Children with Congenital Diaphragmatic Hernia and Neurodevelopmental Impairment
Alexandra Dimmer
Gabriel Altit
Sabrina Beauseigle
Elena Guadagno
Louise Koclas
Katryn Paquette
Ana Sant’Anna
Adam Shapiro
Dan Poenaru
Pramod Puligandla
2024-08-31
Journal of Pediatric Surgery (publié)
doi.org
Data Privacy for Record Linkage and Beyond
Shurong Lin
Eric D. Kolaczyk
In a data-driven world, two prominent research problems are record linkage and data privacy, among others. Record linkage is essential for i… (voir plus)mproving decision-making by integrating information of the same entities from different sources. On the other hand, data privacy research seeks to balance the need to extract accurate insights from data with the imperative to protect the privacy of the entities involved. Inevitably, data privacy issues arise in the context of record linkage. This article identifies two complementary aspects at the intersection of these two fields: (1) how to ensure privacy during record linkage and (2) how to mitigate privacy risks when releasing the analysis results after record linkage. We specifically discuss privacy-preserving record linkage, differentially private regression, and related topics.
2024-08-31
Social Science Research Network (publié)
doi.org
A Joint Temporal Model for Hospitalizations and ICU Admissions Due to COVID‐19 in Quebec
Mariana Carmona‐Baez
Alexandra M. Schmidt
Shirin Golchi
David L. Buckeridge
ABSTRACT Infectious respiratory diseases have been of interest in recent years for the great burden they place on health systems, for instan… (voir plus)ce, the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) that caused the global COVID‐19 pandemic. As many of these diseases might require hospitalization and even intensive care unit (ICU) admission, understanding the joint dynamics of hospitalizations and ICU admissions across time and different groups of the population remains of great importance. We aim to understand the joint evolution of hospital and ICU admissions given COVID‐19 test‐positive cases in the province of Quebec, Canada. We obtain the daily counts, by age group, on the number of confirmed COVID‐19 cases, the number of hospitalizations and the number of ICU admissions due to COVID‐19, from March 2020 through October 2021 in Quebec. We propose a joint Bayesian generalized dynamic linear model for the number of hospitalizations and ICU admissions to study their temporal trends and possible associations with sex and age group. Additionally, we use transfer functions to investigate if there is a memory effect of the number of cases on hospitalizations across the different age groups. The results suggest that there is a clear distinction in the patterns of hospitalizations and ICU admissions across age groups and that the number of cases has a persistent effect on the rate of hospitalization.
2024-08-31
Stat (publié)
doi.org
Do machine learning methods Make Better predictions in pharmacoepidemiology?
Ana Paula Pena-Gralle
Mireille E. Schnitzer
Sofia-Nada Boureguaa
Félix Morin
Marc-André Legault
Caroline Sirois
Alice Dragomir
Lucie Blais
2024-08-31
Annals of Epidemiology (publié)
doi.org
Predicting Five-Year All-Cause Mortality in COPD Patients Using Machine Learning
Ana Paula Pena-Gralle
Amélie Forget
Sofia-Nada Boureguaa
Marc-André Legault
Lucie Blais
2024-08-31
Annals of Epidemiology (publié)
doi.org
Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores
Gilbert Jabbour
Alexis Nolin-Lapalme
Olivier Tastet
Denis Corbin
Paloma Jordà
Achille Sowa
Jacques Delfrate
David Busseuil
Julie G. Hussin
Marie-Pierre Dubé
Jean-Claude Tardif
Léna Rivard
Laurent Macle
Julia Cadrin-Tourigny
Paul Khairy
Robert Avram
Rafik Tadros
Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study … (voir plus)introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS). Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping data sets: 70% for training, 10% for validation, and 20% for testing. The performance of ECG-AI, clinical models, and PGS was assessed in the test data set. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital data set. A total of 669 782 ECGs from 145 323 patients were included. Mean age was 61 ± 15 years, and 58% were male. The primary outcome was observed in 15% of patients, and the ECG-AI model showed an area under the receiver operating characteristic (AUC-ROC) curve of .78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval: 4.02–4.57). In a subgroup analysis of 2301 patients, ECG-AI outperformed CHARGE-AF (AUC-ROC = .62) and PGS (AUC-ROC = .59). Adding PGS and CHARGE-AF to ECG-AI improved goodness of fit (likelihood ratio test P .001), with minimal changes to the AUC-ROC (.76–.77). In the external validation cohort (mean age 59 ± 18 years, 47% male, median follow-up 1.1 year), ECG-AI model performance remained consistent (AUC-ROC = .77). ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and PGS.
2024-08-31
European Heart Journal (publié)
doi.org
TULIPS decorate the three-dimensional genome of PFA ependymoma
Michael J. Johnston
John J.Y. Lee
Bo Hu
Ana Nikolic
Elham Hasheminasabgorji
Audrey Baguette
Seungil Paik
Haifen Chen
Sachin Kumar
Carol C.L. Chen
Selin Jessa
Polina Balin
Vernon Fong
Melissa Zwaig
Kulandaimanuvel Antony Michealraj
Xun Chen
Yanlin Zhang
Srinidhi Varadharajan
Pierre Billon
Nikoleta Juretic … (voir 30 de plus)
Craig Daniels
Amulya Nageswara Rao
Caterina Giannini
Eric M. Thompson
Miklos Garami
Peter Hauser
Timea Pocza
Young Shin Ra
Byung-Kyu Cho
Seung-Ki Kim
Kyu-Chang Wang
Ji Yeoun Lee
Wieslawa Grajkowska
Marta Perek-Polnik
Sameer Agnihotri
Stephen Mack
Benjamin Ellezam
Alex Weil
Jeremy Rich
Guillaume Bourque
Jennifer A. Chan
V. Wee Yong
Mathieu Lupien
Jiannis Ragoussis
Claudia Kleinman
Jacek Majewski
Mathieu Blanchette
Nada Jabado
Michael D. Taylor
Marco Gallo
2024-08-31
Cell (publié)
doi.org
Virtual Reality for Pediatric Trauma Education - A Preliminary Face and Content Validation Study
Fabio Botelho
Said Ashkar
Shreenik Kundu
TJ Matthews
Elena Guadgano
Dan Poenaru
2024-08-31
Journal of Pediatric Surgery (publié)
doi.org
Herbarium collections remain essential in the age of community science
Isaac Eckert
Anne Bruneau
D. Metsger
Simon Joly
T. Dickinson
Laura J. Pollock
2024-08-30
Nature Communications (publié)
doi.org
Ongoing Dynamics of Peak Alpha Frequency Characterize Hypnotic Induction in Highly Hypnotic-Susceptible Individuals
Mathieu Landry
Jason da Silva Castanheira
Floriane Rousseaux
Pierre Rainville
David Ogez
Karim Jerbi
Hypnotic phenomena exhibit significant inter-individual variability, with some individuals consistently demonstrating efficient responses to… (voir plus) hypnotic suggestions, while others show limited susceptibility. Recent neurophysiological studies have added to a growing body of research that shows variability in hypnotic susceptibility is linked to distinct neural characteristics. Building on this foundation, our previous work identified that individuals with high and low hypnotic susceptibility can be differentiated based on the arrhythmic activity observed in resting-state electrophysiology (rs-EEG) outside of hypnosis. However, because previous work has largely focused on mean spectral characteristics, our understanding of the variability over time of these features, and how they relate to hypnotic susceptibility, is still limited. Here we address this gap using a time-resolved assessment of rhythmic alpha peaks and arrhythmic components of the EEG spectrum both prior to and following hypnotic induction. Using multivariate pattern classification, we investigated whether these neural features differ between individuals with high and low susceptibility to hypnosis. Specifically, we used multivariate pattern classification to investigate whether these non-stationary neural features could distinguish between individuals with high and low susceptibility to hypnosis before and after a hypnotic induction. Our analytical approach focused on time-resolved spectral decomposition to capture the intricate dynamics of neural oscillations and their non-oscillatory counterpart, as well as Lempel–Ziv complexity. Our results show that variations in the alpha center frequency are indicative of hypnotic susceptibility, but this discrimination is only evident during hypnosis. Highly hypnotic-susceptible individuals exhibit higher variability in alpha peak center frequency. These findings underscore how dynamic changes in neural states related to alpha peak frequency represent a central neurophysiological feature of hypnosis and hypnotic susceptibility.
2024-08-29
Brain Sciences (publié)
doi.org
Programmable Shape‐Preserving Soft Robotics Arm via Multimodal Multistability
Benyamin Shahryari
Hossein Mofatteh
Arian Sargazi
Armin Mirabolghasemi
David Meger
Abdolhamid Akbarzadeh
Inflatable multistable materials have significantly advanced the design of shape‐preserving soft robotic arms, offering substantial benefi… (voir plus)ts in terms of shape adaptability, energy efficiency, and safety, ensuring operational reliability even in the event of sudden power loss. However, existing strategies for realizing multistable arms often limit themselves to a single mode of multistability, commonly with rotationally symmetric designs favoring extension stability and asymmetric designs inducing bending stability. To address the limitation, this study introduces a pioneering platform termed multimodal multistability that utilizes geometrical frustration. A single cylindrical symmetric cell, designed for extension bistability, could achieve frustrated multistable states in bending by controlling the cell with multiple degrees of freedom incorporated pneumatic actuator. This platform extends the spectrum of attainable stable trajectories while preserving essential attributes of arms, such as load‐bearability, programmability, and reversibility of shape changes. Leveraging a pneumatic system with four degrees of freedom for pressure control, not only enables capturing previously unexplored stable configurations in mechanical metastructures but also allows for the control of their deformation modes. With applications spanning space exploration, medical instruments, and rescue missions, the multimodal multistability promises unparalleled flexibility and efficiency in the design and operation of soft robots.
2024-08-28
Advanced Functional Materials (publié)
doi.org
A long-context RNA foundation model for predicting transcriptome architecture
Ali Saberi
Benedict Choi
Simai Wang
Aldo Hernández-Corchado
Mohsen Naghipourfar
Arsham Mikaeili Namini
Vijay Ramani
Amin Emad
Hamed S. Najafabadi
Hani Goodarzi
Linking DNA sequence to genomic function remains one of the grand challenges in genetics and genomics. Here, we combine large-scale single-m… (voir plus)olecule transcriptome sequencing of diverse cancer cell lines with cutting-edge machine learning to build LoRNASH, an RNA foundation model that learns how the nucleotide sequence of unspliced pre-mRNA dictates transcriptome architecture—the relative abundances and molecular structures of mRNA isoforms. Owing to its use of the StripedHyena architecture, LoRNASH handles extremely long sequence inputs at base-pair resolution (∼65 kilobase pairs), allowing for quantitative, zero-shot prediction of all aspects of transcriptome architecture, including isoform abundance, isoform structure, and the impact of DNA sequence variants on transcript structure and abundance. We anticipate that our public data release and the accompanying frontier model will accelerate many aspects of RNA biotechnology. More broadly, we envision the use of LoRNASH as a foundation for fine-tuning of any transcriptome-related downstream prediction task, including cell-type specific gene expression, splicing, and general RNA processing.
2024-08-26
bioRxiv (prépublication)
doi.org