Publications

Existing eHealth Solutions for Older Adults Living With Neurocognitive Disorders (Mild and Major) or Dementia and Their Informal Caregivers: Protocol for an Environmental Scan
Ambily Jose
Maxime Sasseville
Samantha Dequanter
Ellen Gorus
Anik Giguère
Anne Bourbonnais
Samira Abbasgholizadeh Rahimi
Ronald Buyl
Marie-Pierre Gagnon
Dementia is one of the main public health priorities for current and future societies worldwide. Over the past years, eHealth solutions have… (see more) added numerous promising solutions to enhance the health and wellness of people living with dementia-related cognitive problems and their primary caregivers. Previous studies have shown that an environmental scan identifies the knowledge-to-action gap meaningfully. This paper presents the protocol of an environmental scan to monitor the currently available eHealth solutions targeting dementia and other neurocognitive disorders against selected attributes. This study aims to identify the characteristics of currently available eHealth solutions recommended for older adults with cognitive problems and their informal caregivers. To inform the recommendations regarding eHealth solutions for these people, it is important to obtain a comprehensive view of currently available technologies and document their outcomes and conditions of success. We will perform an environmental scan of available eHealth solutions for older adults with cognitive impairment or dementia and their informal caregivers. Potential solutions will be initially identified from a previous systematic review. We will also conduct targeted searches for gray literature on Google and specialized websites covering the regions of Canada and Europe. Technological tools will be scanned based on a preformatted extraction grid. The relevance and efficiency based on the selected attributes will be assessed. We will prioritize relevant solutions based on the needs and preferences identified from a qualitative study among older adults with cognitive impairment or dementia and their informal caregivers. This environmental scan will identify eHealth solutions that are currently available and scientifically appraised for older adults with cognitive impairment or dementia and their informal caregivers. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate eHealth solutions according to their needs and preferences based on trustable information. DERR1-10.2196/41015
2022-07-31
JMIR Research Protocols (published)
doi.org
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh
C. M. Urry
Amrit Rau
Laurence Perreault-Levasseur
Miles Cranmer
Kevin Schawinski
Dominic Stark
Chuan Tian
Ryan Ofman
Tonima Tasnim Ananna
Connor Auge
N. Cappelluti
D. B. Sanders
Ezequiel Treister
We introduce a novel machine-learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large num… (see more)bers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy’s bulge-to-total-light ratio (L B /L T ), effective radius (R e ), and flux (F). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a spatial transformer network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match z 0.25 galaxies in Hyper Suprime-Cam Wide g-band images, we demonstrate that GaMPEN achieves typical errors of 0.1 in L B /L T , 0.″17 (∼7%) in R e , and 6.3 × 104 nJy (∼1%) in F. GaMPEN's predicted uncertainties are well calibrated and accurate (5% deviation)—for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We a
2022-07-31
The Astrophysical Journal (published)
doi.org
arxiv.org
Induced pluripotent stem cells display a distinct set of MHC I-associated peptides shared by human cancers
Anca Apavaloaei
Leslie Hesnard
Marie-Pierre Hardy
Basma Benabdallah
Gregory Ehx
Catherine Thériault
Jean-Philippe Laverdure
Chantal Durette
Joël Lanoix
Mathieu Courcelles
Nandita Noronha
Kapil Dev Chauhan
Sébastien Lemieux
Christian Beauséjour
Mick Bhatia
Pierre Thibault
Claude Perreault
2022-07-31
Cell Reports (published)
doi.org
Information Gain Sampling for Active Learning in Medical Image Classification
Raghav Mehta
Changjian Shui
Brennan Nichyporuk
Tal Arbel
2022-07-31
ArXiv (preprint)
doi.org
arxiv.org
A portrait of the different configurations between digitally-enabled innovations and climate governance
Pierre J. C. Chuard
Jennifer Garard
Karsten A. Schulz
Nilushi Kumarasinghe
David Rolnick
Damon Matthews
2022-07-31
Earth System Governance (published)
doi.org
The generalizability of pre-processing techniques on the accuracy and fairness of data-driven building models: a case study
Ying Sun
Benjamin C. M. Fung
Fariborz Haghighat
2022-07-31
Energy and Buildings (published)
doi.org
Single‐pass stratified importance resampling
Ege Ciklabakkal
Adrien Gruson
Iliyan Georgiev
D. Nowrouzezahrai
Toshiya Hachisuka
Resampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired t… (see more)arget. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space‐filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling‐based rendering problems.
2022-07-29
Computer Graphics Forum (published)
doi.org
Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study
Lara J. Kanbar
Wissam Shalish
Charles C. Onu
Samantha Latremouille
Lajos Kovacs
Martin Keszler
Sanjay Chawla
Karen A. Brown
Doina Precup
Robert E. Kearney
Guilherme M. Sant’Anna
2022-07-28
Pediatric Research (published)
doi.org
BioCaster in 2021: automatic disease outbreaks detection from global news media
Zaiqiao Meng
Anya Okhmatovskaia
Maxime Polleri
Yannan Shen
Guido Powell
Zihao Fu
Iris Ganser
Meiru Zhang
Nicholas B. King
David Buckeridge
Nigel Collier
SUMMARY: BioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. … (see more)Following a 6-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here, we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports and a new cloud-based visualization dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community. AVAILABILITY AND IMPLEMENTATION: BioCaster web-portal is freely accessible at http://biocaster.org.
2022-07-27
Bioinformatics (published)
doi.org
A Parsimonious Description of Global Functional Brain Organization in Three Spatiotemporal Patterns
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Jorge A. Salas
Catie Chang
B.T. Thomas Yeo
Lucina Q. Uddin
Shella D. Keilholz
Resting-state functional MRI has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-… (see more)scale organization can be divided into two broad categories - zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. Here we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern, and the functional connectome network structure are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses, and unifies phenomena in resting-state functional MRI previously thought distinct.
2022-07-27
Nature Neuroscience (published)
doi.org
Explanatory latent representation of heterogeneous spatial maps of task-fMRI in large-scale datasets
Mariam Zabihi
Seyed Mostafa Kia
Thomas Wolfers
Stijn de Boer
Charlotte Fraza
Sourena Soheili-Nezhad
Richard Dinga
Alberto Llera Arenas
Danilo Bzdok
Christian F. Beckmann
Andre Marquand
Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral ma… (see more)pping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.
2022-07-26
bioRxiv (preprint)
doi.org
Global fMRI signal topography differs systematically across the lifespan
Jason S. Nomi
Danilo Bzdok
Jingwei Li
Taylor Bolt
Catie Chang
Salome Kornfeld
Zachary T. Goodman
B.T. Thomas Yeo
R. Nathan Spreng
Lucina Q. Uddin
The global signal (GS) in resting-state fMRI, known to contain artifacts and non-neuronal physiological signals, also contains important neu… (see more)ral information related to individual state and trait characteristics. Here we show distinct linear and curvilinear lifespan patterns of GS topography in a cross-sectional lifespan sample, demonstrating its importance for consideration in studies of development and aging. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across the lifespan. The thalamus has stronger coupling in older-age individuals compared with younger-aged individuals, while the putamen has stronger coupling in younger individuals compared with older individuals. The subcortical nucleus basalis shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where coupling with the GS is stronger at early and old age, with weaker coupling in middle age. This differentiation in coupling strength between subcortical and cortical brain activity across the lifespan supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on the lifespan stage of individuals. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.
2022-07-26
bioRxiv (preprint)
doi.org