Publications

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
Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping
Vishal Batchu
A. Wilson
Betty Peng
Carl D. Elkin
Umangi Jain
Christopher Van Arsdale
Ross Goroshin
Varun Gulshan
The transition to renewable energy, particularly solar, is key to mitigating climate change. Google's Solar API aids this transition by esti… (voir plus)mating solar potential from aerial imagery, but its impact is constrained by geographical coverage. This paper proposes expanding the API's reach using satellite imagery, enabling global solar potential assessment. We tackle challenges involved in building a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views using deep learning models. Our models, trained on aligned satellite and aerial datasets, produce 25cm DSMs and roof segments. With ~1m DSM MAE on buildings, ~5deg roof pitch error and ~56% IOU on roof segmentation, they significantly enhance the Solar API's potential to promote solar adoption.
2024-08-25
ArXiv (prépublication)
doi.org
arxiv.org
Pushing the frontiers in climate modelling and analysis with machine learning
Veronika Eyring
William D. Collins
Pierre Gentine
Elizabeth A. Barnes
Marcelo Barreiro
Tom Beucler
Marc Bocquet
Christopher S. Bretherton
Hannah M. Christensen
Katherine Dagon
David John Gagne
David Hall
Dorit Hammerling
Stephan Hoyer
Fernando Iglesias-Suarez
Ignacio Lopez-Gomez
Marie C. McGraw
Gerald A. Meehl
Maria J. Molina
Claire Monteleoni … (voir 9 de plus)
Juliane Mueller
Michael S. Pritchard
David Rolnick
Jakob Runge
Philip Stier
Oliver Watt-Meyer
Katja Weigel
Rose Yu
Laure Zanna
2024-08-22
Nature Climate Change (publié)
doi.org
100 years of EEG for brain and behaviour research
Faisal Mushtaq
Dominik Welke
Anne Gallagher
Yuri G. Pavlov
Layla Kouara
Jorge Bosch-Bayard
Jasper J. F. van den Bosch
Mahnaz Arvaneh
Amy R. Bland
Maximilien Chaumon
Cornelius Borck
Xun He
Steven J. Luck
Maro G. Machizawa
Cyril Pernet
Aina Puce
Sidney Segalowitz
Christine Rogers
Muhammad Awais
Claudio Babiloni … (voir 75 de plus)
Neil W. Bailey
Sylvain Baillet
Robert C. A. Bendall
Daniel Brady
Maria L. Bringas-Vega
Niko Busch
Ana Calzada-Reyes
Armand Chatard
Peter E. Clayson
Michael X. Cohen
Jonathan Cole
Martin Constant
Alexandra Corneyllie
Damien Coyle
Damian Cruse
Ioannis Delis
Arnaud Delorme
Damien Fair
Tiago H. Falk
Matthias Gamer
Giorgio Ganis
Kilian Gloy
Samantha Gregory
Cameron Hassall
Katherine Hiley
Richard B. Ivry
Karim Jerbi
Michael Jenkins
Jakob Kaiser
Andreas Keil
Robert T. Knight
Silvia Kochen
Boris Kotchoubey
Olave Krigolson
Nicolas Langer
Heinrich R. Liesefeld
Sarah Lippé
Raquel E. London
Annmarie MacNamara
Scott Makeig
Welber Marinovic
Eduardo Martínez-Montes
Aleya A. Marzuki
Ryan K. Mathew
Christoph Michel
José d. R. Millán
Mark Mon-Williams
Lilia Morales-Chacón
Richard Naar
Gustav Nilsonne
Guiomar Niso
Erika Nyhus
Robert Oostenveld
Katharina Paul
Walter Paulus
Daniela M. Pfabigan
Gilles Pourtois
Stefan Rampp
Manuel Rausch
Kay Robbins
Paolo M. Rossini
Manuela Ruzzoli
Barbara Schmidt
Magdalena Senderecka
Narayanan Srinivasan
Yannik Stegmann
Paul M. Thompson
Mitchell Valdes-Sosa
Melle J. W. van der Molen
Domenica Veniero
Edelyn Verona
Bradley Voytek
Dezhong Yao
Alan C. Evans
Pedro Valdes-Sosa
2024-08-21
Nature Human Behaviour (inconnu)
doi.org
Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples
Yannan Shen
Russell Steele
Philip Abdelmalik
David L Buckeridge
Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the uti… (voir plus)lity of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing 'gold standard' dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop 'gold standard' dates about the onset of outbreaks due to new viral variants.
2024-08-21
Medical Informatics Europe (publié)
doi.org
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning
Swann Bessa
Darius Dabert
Max Bourgeat
Louis-Martin Rousseau
Quentin Cappart
2024-08-21
ArXiv (prépublication)
doi.org
arxiv.org
Non-invasive electroencephalography in awake cats: Feasibility and application to sensory processing in chronic pain
Aliénor Delsart
Aude Castel
Guillaume Dumas
Colombe Otis
Mathieu Lachance
Maude Barbeau-Grégoire
Bertrand Lussier
Franck Péron
Marc Hébert
Nicolas Lapointe
Maxim Moreau
Johanne Martel-Pelletier
Jean-Pierre Pelletier
Eric Troncy
2024-08-21
Journal of Neuroscience Methods (publié)
doi.org
Concurrent product layout design optimization and dependency management using a modified NSGA-III approach
Yann-Seing Law-Kam Cio
Giovanni Beltrame
Aurelian Vadean
Abolfazl Mohebbi
Sofiane Achiche
The complexity of mechatronic systems has increased with the significant advancements of technology in their components which makes their de… (voir plus)sign more challenging. This is due to the need for incorporating expertise from different domains as well as the increased number and complexity of components integrated into the product. To alleviate the burden of designing such products, many industries and researchers are attracted to the concept of modularization which is to identify a subset of system components that can form a module. To achieve this, a novel product-related dependency management approach is proposed in this paper with the support of an augmented design structure matrix. This approach makes it possible to model positive and negative dependencies and to compute the combination potency between components to form modules. This approach is then integrated into a modified non-dominated sorting genetic algorithm III to concurrently optimize the design and identify the modules. The methodology is exemplified through the case study of a layout design of an automatic greenhouse. By applying the proposed methodology to the case study, it was possible to generate concepts that decreased the number of modules from 9 down to 4 while ensuring the optimization of the design performance.
2024-08-20
Frontiers in Mechanical Engineering (publié)
doi.org
Switching between tasks can cause AI to lose the ability to learn
Clare Lyle
Razvan Pascanu
2024-08-20
Nature (publié)
doi.org
BlueCelluLab: Biologically Detailed Neural NetworkExperimentation API
Anıl Tuncel
Werner Van Geit
Mike Gevaert
Benjamin Torben-Nielsen
Darshan Mandge
İlkan Kılıç
Aurélien T. Jaquier
Eilif Muller
Lida Kanari
Henry Markram
The NEURON simulator, established in 1984 and continuously developed since, stands as\nthe preeminent tool for neuron simulation within comp… (voir plus)utational neuroscience. Its widespread\nadoption and compatibility with computational clusters and supercomputers underscore its\npivotal role in large-scale neuronal research. However, its integration with the Python pro-\ngramming language has introduced complexities, particularly concerning memory management\nand object lifecycle. To conceal these challenges from the user and seamlessly interface\nwith community standards for neural network representation data formats such as SONATA,\nwe introduce BlueCelluLab. The high-level Python API simplifies the execution of neural\nsimulations, ranging from single neurons to intricate networks, by managing complexities\nrelated to memory management and object lifecycle, thus providing a streamlined experience\nfor users. Today, BlueCelluLab is powering various Python packages, command line interfaces,\nweb applications, and data analysis workflows.
2024-08-16
Journal of Open Source Software (publié)
doi.org