Publications

A novel high-dimensional model for identifying regional DNA methylation QTLs
Kaiqiong Zhao
Archer Y. Yang
Karim Oualkacha
Yixiao Zeng
Kathleen Klein
Marie Hudson
Inés Colmegna
Sasha Bernatsky
Celia M.T. Greenwood
Varying coefficient models offer the flexibility to learn the dynamic changes of regression coefficients. Despite their good interpretabilit… (see more)y and diverse applications, in high-dimensional settings, existing estimation methods for such models have important limitations. For example, we routinely encounter the need for variable selection when faced with a large collection of covariates with nonlinear/varying effects on outcomes, and no ideal solutions exist. One illustration of this situation could be identifying a subset of genetic variants with local influence on methylation levels in a regulatory region. To address this problem, we propose a composite sparse penalty that encourages both sparsity and smoothness for the varying coefficients. We present an efficient proximal gradient descent algorithm that scales to high-dimensional predictor spaces, providing sparse solutions for the varying coefficients. A comprehensive simulation study has been conducted to evaluate the performance of our approach in terms of estimation, prediction and selection accuracy. We show that the inclusion of smoothness control yields much better results over sparsity-only approaches. An adaptive version of the penalty offers additional performance gains. We further demonstrate the utility of our method in identifying regional mQTLs from asymptomatic samples in the CARTaGENE cohort. The methodology is implemented in the R package sparseSOMNiBUS, available on GitHub.
2024-12-30
PubMed (unknown)
doi.org
Sociodemographic characteristics of SARS-CoV-2 serosurveillance studies with diverse recruitment strategies, Canada, 2020 to 2023
Matthew J. Knight
Yuan Yu
Jiacheng Chen
Sheila F. O’Brien
David L. Buckeridge
Carmen Charlton
W. Alton Russell
Serological testing was a key component of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) surveillance. Social distancing inte… (see more)rventions, resource limitations, and the need for timely data led to serosurveillance studies using a range of recruitment strategies, which likely influenced study representativeness. Characterizing representativeness in surveillance is crucial to identify gaps in sampling coverage and to assess health inequities. We retrospectively analyzed three pre-existing longitudinal cohorts, two convenience samples using residual blood, and one de novo probabilistic survey conducted in Canada between April 2020 – November 2023. We calculated study specimen counts by age, sex, urbanicity, race/ethnicity, and neighborhood deprivation quintiles. We derived a ‘representation ratio’ as a simple metric to assess generalizability to a target population and various sociodemographic strata. The six studies included 1,321,675 specimens. When stratifying by age group and sex, 65% of racialized minority subgroups were moderately underrepresented (representation ratio < 0.75). Representation was generally higher for older Canadians, urban neighborhoods, and neighborhoods with low material deprivation. Rural representation was highest in a study that used outpatient laboratory blood specimens. Racialized minority representation was highest in a de novo probabilistic survey cohort. While no study had adequate representation of all subgroups, less traditional recruitment strategies were more representative of some population dimensions. Understanding demographic representativeness and barriers to recruitment are important considerations when designing population health surveillance studies.
2024-12-30
BMC Public Health (published)
doi.org
The Romantic Historicism and The Rise of the Historical Novel in the 19th Century Romanian Literature
A.R. Olteanu
2024-12-30
Philobiblon. Transylvanian Journal of Multidisciplinary Research in the Humanities (published)
doi.org
Sliding ferroelectric memories and synapses based on rhombohedral-stacked bilayer MoS2
Xiuzhen Li
Biao Qin
Yaxian Wang
Yue Xi
Zhiheng Huang
Mengze Zhao
Yalin Peng
Zitao Chen
Zitian Pan
Jundong Zhu
Chenyang Cui
Rong Yang
Wei Yang
Sheng Meng
Dongxia Shi
Xuedong Bai
Can Liu
Na Li
Jian Tang
Kaihui Liu … (see 3 more)
Kai-Wen Liu
Luojun Du
Guangyu Zhang
2024-12-29
Nature Communications (published)
doi.org
arxiv.org
Exploring Compound Loss Functions for Brain Tumor Segmentation
Anita Kriz
Raghav Mehta
Brennan Nichyporuk
Tal Arbel
2024-12-27
Lecture Notes in Computer Science (published)
doi.org
Enhancing Surgical Safety in Conflict Zones: Implementing the WHO Checklist in North Kivu
Jacques Fadhili Bake
Claude Kasereka Masumbuko
Zacharie Tsongo Kibendelwa
Dan Poenaru
Background: The WHO Surgical Safety Checklist (WHO Checklist) has been shown to effectively reduce surgical complications worldwide. However… (see more), implementing this checklist in conflict-affected regions like North Kivu, Democratic Republic of Congo (DRC), presents unique challenges. This study investigates the utilization of the WHO Checklist in hospitals across North Kivu to identify barriers and opportunities for improvement. Methods: A cross-sectional study was conducted across 11 hospitals (5 urban and 6 rural) in North Kivu. Surveys were administered to healthcare professionals, including surgeons, anesthesiologists, and nurses, to assess their knowledge, usage, and perceptions of the WHO Checklist. Data were analyzed using SPSS version 26. Results: The response rate was 80.3%, with a majority (59.2%) from urban hospitals. The use of the WHO Checklist was inconsistent; 60.1% reported it was not utilized in the operating room. No significant differences in checklist usage were found between urban and rural hospitals (p=0.516). Training significantly correlated with the completion rate of checklist phases (p0.001) but not with overall usage (p=0.057). Furthermore, there were no significant differences regarding the need for further training to
2024-12-25
Kivu Medical Journal (published)
doi.org
Torque-Aware Momentum
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Gintare Karolina Dziugaite
Razvan Pascanu
Sarath Chandar
Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely … (see more)used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.
2024-12-24
ArXiv (preprint)
doi.org
arxiv.org
DECIDE-Twin: A Framework for AI-Enabled Digital Twins in Clinical Decision-Making
Samira Abbasgholizadeh Rahimi
Ashkan Baradaran
Farbod Khameneifar
Genevieve Gore
Amalia M. Issa
Background: AI-enabled digital twins (DTs) are advanced virtual models of a complex real-world system, which have the potential to transform… (see more) clinical decision-making. Despite the growing interest in such DTs, the literature lacks a unified framework for their development and implementation. Objective: This study aims to map the existing knowledge on AI-enabled DTs for clinical decision-making, and develop a comprehensive framework for their development and implementation. Methods: Informed by frameworks established by Arksey and O'Malley, and the Joanna Briggs Institute, we performed a scoping review of studies on the development and implementation of AI-enabled DTs for clinical decision-making in any healthcare setting. The search strategy was developed by a librarian for three databases from the date of inception until August 2023. We also conducted a grey literature search on Google Scholar. One reviewer screened titles and abstracts, full-text articles, and charted data, and the second reviewer verified them. Quantitative data were summarized using frequency and proportions, and qualitative data were summarized using content analysis. Key steps in DT development were identified to create the DECIDE-Twin framework. Results: Eleven articles were included: seven reviews and four empirical studies. The reviews contained either a framework or information that was used to construct our comprehensive framework. The empirical studies reported the DT development, and one reported a common infrastructure for a wide range of DT applications. Conclusion: We developed the DECIDE-Twin framework that could serve as a guide for researchers and practitioners in DT development and implementation for clinical decision-making. Further research is needed to validate and implement this framework for various clinical applications.
2024-12-23
IEEE Journal of Biomedical and Health Informatics (published)
doi.org
Divergent Perception: Framing Creative Cognition Through the Lens of Sensory Flexibility
Antoine Bellemare‐Pepin
Karim Jerbi
Creativity is a cornerstone of human evolution and is typically defined as the multifaceted ability to produce novel and useful artifacts. A… (see more)lthough much research has focused on divergent thinking, growing evidence underscores the importance of perceptual processing in fostering creativity, particularly through perceptual flexibility. The present work aims to offer a framework that relates creativity to perception, showing how sensory affordances, especially in ambiguous stimuli, can contribute to the generation of novel ideas. In doing so, we contextualize the phenomenon of pareidolia, which involves seeing familiar patterns in noisy or ambiguous stimuli, as a key perceptual mechanism of idea generation—one of the central stages of the creative process. We introduce “divergent perception” to describe the process by which individuals actively engage with the perceptual affordances provided by ambiguous sensory information, and illustrate how this concept could account for the heightened creativity observed in psychedelic and psychotic states. Moreover, we explore how divergent perception relates to cognitive mechanisms crucial in creative thinking, particularly focusing on the role of attention. Finally, we discuss future paths for the exploration of divergent perception, including targeted manipulation of stimulus characteristics and the investigation of the intricate interplay between bottom‐up and top‐down cognitive processes.
2024-12-23
The Journal of creative behavior (published)
doi.org
Polaris: a universal tool for chromatin loop annotation in bulk and single-cell Hi-C data
Yusen Hou
Audrey Baguette
Mathieu Blanchette
Yanlin Zhang
Annotating chromatin loops is essential for understanding the 3D genome’s role in gene regulation, but current methods struggle with low c… (see more)overage, particularly in single-cell datasets. Chromatin loops are kilo-to mega-range structures that exhibit broader features, such as co-occurring loops, stripes, and domain boundaries along axial directions of Hi-C contact maps. However, existing tools primarily focus on detecting localized, highly-concentrated, interactions. Furthermore, the wide variety of available chromatin conformation datasets is rarely utilized in developing effective loop callers. Here, we present Polaris, a universal tool that integrates axial attention with a U-shaped backbone to accurately detect loops across different 3D genome assays. By leveraging extensive Hi-C contact maps in a pretrain-finetune paradigm, Polaris achieves consistent performance across various datasets. We compare Polaris against existing tools in loop annotation from both bulk and single-cell data and find that Polaris outperforms other programs across different cell types, species, sequencing depths, and assays.
2024-12-23
bioRxiv (preprint)
doi.org
Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning
Hao Zhou
Chengming Hu
Dun Yuan
Ye Yuan
Di Wu
Xue Liu
Zhu Han
Jianzhong (Charlie) Zhang
Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large langua… (see more)ge models (LLMs) have attracted considerable interest from academia and telecom industry. This work considers a novel edge-cloud deployment of foundation models in 6G networks. Specifically, it aims to minimize the service delay of foundation models by radio resource allocation and task offloading, i.e., offloading diverse content generation tasks to proper LLMs at the network edge or cloud. In particular, we first introduce the communication system model, i.e., allocating radio resources and calculating link capacity to support generated content transmission, and then we present the LLM inference model to calculate the delay of content generation. After that, we propose a novel in-context learning method to optimize the task offloading decisions. It utilizes LLM's inference capabilities, and avoids the difficulty of dedicated model training or fine-tuning as in conventional machine learning algorithms. Finally, the simulations demonstrate that the proposed edge-cloud deployment and in-context learning task offloading method can achieve satisfactory generation service quality without dedicated model training or fine-tuning.
2024-12-22
IEEE Wireless Communications Letters (published)
doi.org
arxiv.org
Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
Ge Zhang
Mohammad Alomrani
Hongjian Gu
Jiaming Zhou
Yaochen Hu
B. Wang
Qun Liu
Mark J. Coates
Yingxue Zhang
Jianye HAO
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational rea… (see more)soning problems such as kinship or spatial reasoning. In this paper, we present Path-of-Thoughts (PoT), a novel framework designed to tackle relation reasoning by decomposing the task into three key stages: graph extraction, path identification, and reasoning. Unlike previous approaches, PoT efficiently extracts a task-agnostic graph that identifies crucial entities, relations, and attributes within the problem context. Subsequently, PoT identifies relevant reasoning chains within the graph corresponding to the posed question, facilitating inference of potential answers. Experimental evaluations on four benchmark datasets, demanding long reasoning chains, demonstrate that PoT surpasses state-of-the-art baselines by a significant margin (maximum 21.3%) without necessitating fine-tuning or extensive LLM calls. Furthermore, as opposed to prior neuro-symbolic methods, PoT exhibits improved resilience against LLM errors by leveraging the compositional nature of graphs.
2024-12-22
ArXiv (preprint)
doi.org
arxiv.org