Publications

Bridging biodiversity and ecosystem services through useful plant species
Nina Obiar
Isaac Eckert
Janelle Baker
Daniel Moerman
Laura J. Pollock
2025-03-27
PLANTS, PEOPLE, PLANET (publié)
doi.org
Genetic Analysis of Polyunsaturated Fatty Acids Biosynthesis Pathway Determines Four Distinct Thraustochytrid Types
Sou‐Yu Cheng
Yi‐Jing Chen
Hsiu-Chin Lin
Hsin‐Yang Chang
Ming‐Der Huang
ABSTRACT Thraustochytrids, diverse marine unicellular protists encompassing over 10 recognised genera, are renowned for synthesising polyuns… (voir plus)aturated fatty acids (PUFAs), with content and composition varying substantially across genera. While PUFAs are known to be produced via PUFA synthase (PUFA‐S) and/or elongase/desaturase (ELO/DES) pathways, the distinctions in genes involved remain unexplored. This study analysed PUFA biosynthetic genes in 19 thraustochytrid strains across six genera, categorising them into four types. Type I exclusively utilises the ELO/DES pathway, Type II employs both PUFA‐S and complete ELO/DES pathways, while Types III and IV primarily rely on PUFA‐S, with Type III lacking the canonical Δ9 desaturase and Type IV missing most desaturase and elongase enzymes. Notably, the Δ9 desaturase and ATP‐citrate lyase (ACLY) are exclusive to Types I and II, while β‐carotene hydroxylase (CrtZ) is absent in these types. ACLY absence suggests alternative acetyl‐CoA supply pathways in Types III and IV, whereas CrtZ absence implies either a lack of specific xanthophylls or alternative biosynthetic pathways in Types I and II. Synteny analysis revealed conserved genomic organisation of PUFA biosynthetic genes, indicating a shared evolutionary trajectory. This study provides insights into the genetic diversity underlying PUFA biosynthesis in thraustochytrids, while proposing putative evolutionary pathways for the four lineages.
2025-03-27
Environmental Microbiology (publié)
doi.org
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Gian Mario Favero
Parham Saremi
Emily Kaczmarek
Brennan Nichyporuk
Tal Arbel
2025-03-26
Medical Imaging with Deep Learning (présentation orale)
doi.org
openreview.net
debug-gym: A Text-Based Environment for Interactive Debugging
Xingdi Yuan
Morgane M Moss
Charbel Feghali
Chinmay Singh
Darya Moldavskaya
Drew MacPhee
Lucas Caccia
Matheus Pereira
Minseon Kim
Alessandro Sordoni
Marc-Alexandre Côté
2025-03-26
ArXiv (prépublication)
doi.org
arxiv.org
How do language models learn facts? Dynamics, curricula and hallucinations
Nicolas Zucchet
Jörg Bornschein
Stephanie Chan
Andrew Lampinen
Razvan Pascanu
Soham De
2025-03-26
ArXiv (prépublication)
doi.org
arxiv.org
PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar
Anita Kriz
Mohammad Havaei
Tal Arbel
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (voir plus)ta imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.
2025-03-26
MIDL.io/2025/Conference (présentation orale)
doi.org
openreview.net
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Béchard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
Christopher Pal
David Vázquez
Spandana Gella
Sai Rajeswar
Perouz Taslakian
2025-03-26
ArXiv (prépublication)
doi.org
arxiv.org
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Arthur Ouaknine
Étienne Laliberté
Yoshua Bengio
David Rolnick
Hugo Larochelle
2025-03-25
ArXiv (prépublication)
doi.org
arxiv.org
A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer.
Björn Morén
Hossein Jafarzadeh
S. Enger
2025-03-25
Comput. Biol. Medicine (publié)
doi.org
Perspectives on optimizing transport systems with supply-dependent demand
Emma Frejinger
Mike Hewitt
2025-03-25
INFOR. Information systems and operational research (publié)
doi.org
Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems
Dmytro Humeniuk
Foutse Khomh
2025-03-25
ArXiv (prépublication)
doi.org
arxiv.org
Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images
Pedro Vianna
Paria Mehrbod
Muawiz Chaudhary
Michael Eickenberg
Guy Wolf
Eugene Belilovsky
An Tang
Guy Cloutier
Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness… (voir plus) and availability. Deep learning methods have attracted considerable interest in this field, as they are capable of learning patterns in a collection of images and achieve clinically comparable levels of accuracy in steatosis grading. However, variations in patient populations, acquisition protocols, equipment, and operator expertise across clinical sites can introduce domain shifts that reduce model performance when applied outside the original training setting. In response, unsupervised domain adaptation techniques are being investigated to address these shifts, allowing models to generalize more effectively across diverse clinical environments. In this work, we propose a test-time batch normalization (TTN) technique designed to handle domain shift, especially for changes in label distribution, by adapting selected features of batch normalization (BatchNorm) layers in a trained convolutional neural network model. This approach operates in an unsupervised manner, allowing robust adaptation to new distributions without access to label data. The method was evaluated on two abdominal US datasets collected at different institutions, assessing its capability in mitigating domain shift for hepatic steatosis classification. The proposed method reduced the mean absolute error in steatosis grading by 37% and improved the area under the receiver operating characteristic curves (AUC) for steatosis detection from 0.78 to 0.97, compared to nonadapted models. These findings demonstrate the potential of the proposed method to address domain shift in US-based hepatic steatosis diagnosis, minimizing risks associated with deploying trained models in various clinical settings.
2025-03-25
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (publié)
doi.org