Publications

1351. Predictors of Loss of Infectivity Among Healthcare Workers with Primary and Recurrent SARS-CoV-2 infection: An Observational Cohort Study

Stefka Dzieciolowska

Yves Longtin

Hugues Charest

Tonya Roy

Judith Fafard

Inès Levade

Jean Longtin

Leighanne Parkes

Jasmin Villeneuve

Patrice Savard

J. Corbeil

Gaston De Serres

Abstract Background Factors associated with loss of infectivity in healthcare workers (HCWs) with COVID-19 are poorly understood. Understand… (voir plus)ing predictive factors could help optimize return-to-work criteria and minimize absenteeism. Methods Prospective observational cohort study of HCWs with COVID-19 conducted between Feb 20 2022 and March 6 2023 in 20 institutions in Montreal, Canada, with clinical/laboratory follow-up on Day 5, 7 and 10 of infection. Infectivity was determined by viral culture (Vero E6 cells) on nasopharyngeal swabs. Predictors of loss of infectivity were investigated by univariate and multivariate logistic regression. Results Overall, 121 participants (79.3% female, mean age 40 years) were recruited. Most (n=107, 88.4%) had received ≥3 vaccines and 20 (16.5%) had a history of prior COVID-19. The proportion of HCWs with a positive viral culture decreased from 71.9% on day 5 of infection to 18.2% on day 10. The proportion of HCWs with a positive RT-PCR decreased from 93.3% (112/120) on day 5 (median Ct value, 23.4 [IQR, 20.6-27.9]) to 61.2% (74/120) on day 10 (median Ct value, 32.5 [IQR, 28.5 to undetectable]). Rapid antigen detection test (RADT) positivity decreased from 81.5% on day 5 to 34.2% on day 10. Participants with recurrent COVID-19 had lower likelihood of infectivity at each visit (OR on day 5, 0.14; 95% CI 0.05-0.40; p 0.001; OR on day 7, 0.04; 95% CI, 0.01-0.33; p=0.003) and none were infective on day 10 (p=0.02). At each visit, recurrent cases had higher median RT-PCR Ct values than primary infections (p 0.03) and were more likely to have a negative RADT result (p 0.01). By multivariate analysis, ongoing infectivity was associated with a RT-PCR Ct value 23 (adjusted OR [aOR] on day 5, 22.75; p 0.001; aOR on Day 7, 182.30; p 0.001; and aOR on Day 10; 24.71; p=0.02). A history of previous COVID-19 was associated with a lower probability of infectivity on Day 5 (aOR, 0.005; p=0.003). By contrast, symptom improvement (including fever) and RADT result were not independent predictors of loss of infectivity. Conclusion A lower RT-PCR Ct value is associated with ongoing infectivity, whereas COVID-19 reinfection is a predictor of loss of infectivity. These findings could help optimize return-to-work algorithms. Disclosures All Authors: No reported disclosures

2023-11-26

Open Forum Infectious Diseases (publié)

doi.org

Author Correction: 30×30 biodiversity gains rely on national coordination

Isaac Eckert

Andrea Brown

Dominique Caron

Federico Riva

Laura J. Pollock

2023-11-26

Nature Communications (publié)

doi.org

Exploring the multidimensional nature of repetitive and restricted behaviors and interests (RRBI) in autism: neuroanatomical correlates and clinical implications

Aline Lefebvre

Nicolas Traut

Amandine Pedoux

Anna Maruani

Anita Beggiato

Monique Elmaleh

David Germanaud

Anouck Amestoy

Myriam Ly‐Le Moal

Christopher H. Chatham

Lorraine Murtagh

Manuel Bouvard

Marianne Alisson

Marion Leboyer

Thomas Bourgeron

Roberto Toro

Guillaume Dumas

Clara A. Moreau

Richard Delorme

2023-11-26

Molecular Autism (publié)

doi.org

scGeneRythm: Using Neural Networks and Fourier Transformation to Cluster Genes by Time-Frequency Patterns in Single-Cell Data

Yiming Jia

Hao Wu

Jun Ding

2023-11-26

bioRxiv (prépublication)

doi.org

The search for the lost attractor

Mario Pasquato

Syphax Haddad

Pierfrancesco Di Cintio

Alexandre Adam

Pablo Lemos

No'e Dia

Mircea Petrache

Ugo Niccolo Di Carlo

Alessandro A. Trani

Laurence Perreault-Levasseur

Yashar Hezaveh

2023-11-26

ArXiv (prépublication)

arxiv.org

Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning

Jiaqi Li

Rui Wang

Yuanhao Lai

Changjian Shui

Sabyasachi Sahoo

Charles X. Ling

Shichun Yang

Boyu Wang

Christian Gagné

Fan Zhou

Continual learning aims to learn a series of tasks sequentially without forgetting the knowledge acquired from the previous ones. In this wo… (voir plus)rk, we propose the Hessian Aware Low-Rank Perturbation algorithm for continual learning. By modeling the parameter transitions along the sequential tasks with the weight matrix transformation, we propose to apply the low-rank approximation on the task-adaptive parameters in each layer of the neural networks. Specifically, we theoretically demonstrate the quantitative relationship between the Hessian and the proposed low-rank approximation. The approximation ranks are then globally determined according to the marginal increment of the empirical loss estimated by the layer-specific gradient and low-rank approximation error. Furthermore, we control the model capacity by pruning less important parameters to diminish the parameter growth. We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method. Empirical results show that our method performs better on different benchmarks, especially in achieving task order robustness and handling the forgetting issue. The source code is at https://github.com/lijiaqi/HALRP.

2023-11-25

ArXiv (prépublication)

doi.org

arxiv.org

Low Compute Unlearning via Sparse Representations

Vedant Shah

Frederik Träuble

Ashish Malik

Hugo Larochelle

Michael Curtis Mozer

Sanjeev Arora

Yoshua Bengio

Anirudh Goyal

Machine unlearning, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible … (voir plus)using existing techniques. We propose a low-compute unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set and incurs negligible damage to the model's performance on the rest of the dataset. We evaluate the proposed technique on the problem of class unlearning using four datasets: CIFAR-10, CIFAR-100, LACUNA-100 and ImageNet-1k. We compare the proposed technique to SCRUB, a state-of-the-art approach which uses knowledge distillation for unlearning. Across all four datasets, the proposed technique performs as well as, if not better than SCRUB while incurring almost no computational cost.

2023-11-25

arXiv (prépublication)

doi.org

openreview.net

Gaining Biological Insights through Supervised Data Visualization

Jake S. Rhodes

Adrien Aumon

Sacha Morin

Marc Girard

Catherine Larochelle

Boaz Lahav

Elsa Brunet-Ratnasingham

Amélie Pagliuzza

Lorie Marchitto

Wei Zhang

Adele Cutler

Francois Grand’Maison

Anhong Zhou

Andrés Finzi

Nicolas Chomont

Daniel E. Kaufmann

Stephanie Zandee

Alexandre Prat

Guy Wolf

Kevin R. Moon

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHAT… (voir plus)E, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE’s prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

2023-11-22

bioRxiv (prépublication)

doi.org

Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles

Luca Scimeca

Alexander Rubinstein

Damien Teney

Seong Joon Oh

Armand Mihai Nicolicioiu

Yoshua Bengio

Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut lea… (voir plus)rning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In this work, we propose

2023-11-22

ArXiv (prépublication)

openreview.net