Publications

TCR/Chimeric Antigen Receptor (CAR) Cross-Antagonism to Fine-Tune CAR-T cell Immunotherapy

Grégoire Altan-Bonnet

Taisuke Kondo

François X. P. Bourassa

Sooraj R. Achar

Justyn DuSold

Pablo Cespedes

Madison Wahlsten

Guillaume Gaud

Paul E Love

Mike Dustin

Paul François

Naomi Taylor

Chimeric antigen receptor (CAR) T cells, are created by extracting T cells from a cancer patient, engineering them to express a CAR targetin… (voir plus)g a tumor specific molecule, then reintroducing them back into the patient. A patient’s T cells contain their own endogenous T cell receptors (TCRs) however, which could potentially interact with the exogenous CAR inserted into the cell. In this study, we examine how TCR and CAR signals interact upon CAR-T activation. We show that weak TCR stimulation can reduce (antagonize) or increase overall CAR-T response, both in vitro and in vivo, across multiple tumor models, in both mouse and human T cells. We further show that the behavior of these TCR/CAR interactions can be manipulated by changing various characteristics of the TCR, CAR, and associated ligands. While this behavior is complex, we show that it can be described by a single mathematical model based on the adaptive kinetic proofreading scheme of ligand discrimination. We conclude by presenting potential applications for cancer immunotherapy. Intramural Research Program of the National Cancer Institute

2023-05-01

Journal of Immunology (publié)

doi.org

The study of plasticity has always been about gradients

Blake Richards

Konrad Paul Kording

2023-05-01

The Journal of Physiology (publié)

doi.org

UnityLint: A Bad Smell Detector for Unity

Matteo Bosco

Pasquale Cavoto

Augusto Ungolo

Biruk Asmare Muse

Foutse Khomh

Vittoria Nardone

Massimiliano Di Penta

The video game industry is particularly rewarding as it represents a large portion of the software development market. However, working in t… (voir plus)his domain may be challenging for developers, not only because of the need for heterogeneous skills (from software design to computer graphics), but also for the limited body of knowledge in terms of good and bad design and development principles, and the lack of tool support to assist them. This tool demo proposes UnityLint, a tool able to detect 18 types of bad smells in Unity video games. UnityLint builds upon a previously-defined and validated catalog of bad smells for video games. The tool, developed in C# and available both as open-source and binary releases, is composed of (i) analyzers that extract facts from video game source code and metadata, and (ii) smell detectors that leverage detection rules to identify smells on top of the extracted facts.Tool: https://github.com/mdipenta/UnityCodeSmellAnalyzerTeaser Video: https://youtu.be/HooegxZ8H6g

2023-05-01

IEEE International Conference on Program Comprehension (publié)

doi.org

What did the T cell see? A deep-learning model of CD8+ T cell activation reveals sharp antigen discrimination at the single cell level

Madison Wahlsten

Amin Akhshi

Sooraj R. Achar

Anagha Yogam Krishnan

Paul François

Grégoire Altan-Bonnet

Immunotherapies such as checkpoint blockade antibodies to block T cell exhaustion have been successful in several cancers such as non-small … (voir plus)cell lung cancer and melanoma, but limited in others (e.g., pancreatic or prostate carcinomas) owing to differences in tumor antigenicity. Therefore, quantifying tumor antigenicity is critical for successful immunotherapies. Our lab has shown that antigenicity can be encoded in a single parameter derived from bulk cytokine dynamics in ex vivo co-cultures between antigen presenting cells (APCs) and T cells. Here we built a model that can capture the antigenicity seen by individual cells. Using a custom robotic platform, we generated high-throughput kinetics of T cell activation in co-culture with APCs by analyzing cells at various timepoints across a large set of activation conditions. We performed spectral flow cytometry to measure the expression of up to 30 surface markers and intracellular signals per cell. To analyze our content-rich datasets, we designed a machine learning-based model that can classify the antigen seen by an individual cell using expression values from flow cytometry. The model performs well not only at classifying T cells (ROC-AUC > 0.91), but also APCs (ROC-AUC > 0.88), suggesting that each individual leukocyte may register the quality of antigen being presented. Blocking cytokine signaling disrupted this antigen classification. Our study demonstrates that every individual lymphocyte can bridge local and global response to achieve high discriminatory power of antigens.

2023-05-01

Journal of Immunology (publié)

doi.org

Social Media as a Vector for Escort Ads:A Study on OnlyFans advertisements on Twitter

Maricarmen Arenas

Pratheeksha Nair

Reihaneh Rabbany

Golnoosh Farnadi

Online sex trafficking is on the rise and a majority of trafficking victims report being advertised online. The use of OnlyFans as a platfor… (voir plus)m for adult content is also increasing, with Twitter as its main advertising tool. Furthermore, we know that traffickers usually work within a network and control multiple victims. Consequently, we suspect that there may be networks of traffickers promoting multiple OnlyFans accounts belonging to their victims. To this end, we present the first study of OnlyFans advertisements on Twitter in the context of finding organized activities. Preliminary analysis of this space shows that most tweets related to OnlyFans contain generic text, making text-based methods less reliable. Instead, focusing on what ties the authors of these tweets together, we propose a novel method for uncovering coordinated networks of users based on their behaviour. Our method, called Multi-Level Clustering (MLC), combines two levels of clustering that considers both the network structure as well as embedded node attribute information. It focuses jointly on user connections (through mentions) and content (through shared URLs). We apply MLC to real-world data of 2 million tweets pertaining to OnlyFans and analyse the detected groups. We also evaluate our method on synthetically generated data (with injected ground truth) and show its superior performance compared to competitive baselines. Finally, we discuss examples of organized clusters as case studies and provide interesting conclusions to our study.

2023-04-30

Proceedings of the 15th ACM Web Science Conference 2023 (publié)

doi.org

Can ChatGPT Pass An Introductory Level Functional Language Programming Course?

Chuqin Geng

Yihan Zhang

Brigitte Pientka

Xujie Si

The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in sol… (voir plus)ving a diverse range of tasks, including language translation, text summarization, and computer programming. Its capability for writing, modifying, and even correcting code together with its ease of use and access is already dramatically impacting computer science education. This paper aims to explore how well ChatGPT can perform in an introductory-level functional language programming course. In our systematic evaluation, we treated ChatGPT as one of our students and demonstrated that it can achieve a grade B- and its rank in the class is 155 out of 314 students overall. Our comprehensive evaluation provides valuable insights into ChatGPT's impact from both student and instructor perspectives. Additionally, we identify several potential benefits that ChatGPT can offer to both groups. Overall, we believe that this study significantly clarifies and advances our understanding of ChatGPT's capabilities and potential impact on computer science education.

2023-04-29

ArXiv (prépublication)

doi.org

arxiv.org

Leveraging Data Mining Algorithms to Recommend Source Code Changes

AmirHossein Naghshzan

Saeed Khalilazar

Pierre Poilane

Olga Baysal

Latifa Guerrouj

Foutse Khomh

2023-04-29

ArXiv (prépublication)

doi.org

arxiv.org

Group Fairness in Reinforcement Learning

Harsh Satija

Alessandro Lazaric

Matteo Pirotta

Joelle Pineau

We pose and study the problem of satisfying fairness in the online Reinforcement Learning (RL) setting. We focus on the group notions of fai… (voir plus)rness, according to which agents belonging to different groups should have similar performance based on some given measure. We consider the setting of maximizing return in an unknown environment (unknown transition and reward function) and show that it is possible to have RL algorithms that learn the best fair policies without violating the fairness requirements at any point in time during the learning process. In the tabular finite-horizon episodic setting, we provide an algorithm that combines the principle of optimism and pessimism under uncertainty to achieve zero fairness violation with arbitrarily high probability while also maintaining sub-linear regret guarantees. For the high-dimensional Deep-RL setting, we present algorithms based on the performance-difference style approximate policy improvement update step and we report encouraging empirical results on various traditional RL-inspired benchmarks showing that our algorithms display the desired behavior of learning the optimal policy while performing a fair learning process.

2023-04-28

TMLR (accepté)

openreview.net

Overcoming Interpretability and Accuracy Trade-off in Medical Imaging

Ivaxi Sheth

Samira Ebrahimi Kahou

2023-04-28

MIDL.io/2023/Short_Paper_Track (poster)

openreview.net

The Influence of Age, Sex, and Socioeconomic Status on Glycemic Control Among People With Type 1 and Type 2 Diabetes in Canada: Patient-Led Longitudinal Retrospective Cross-sectional Study With Multiple Time Points of Measurement

Seyedmostafa Mousavi

Dana Tannenbaum Greenberg

Ruth Ndjaboué

Michelle Greiver

Olivia Drescher

Selma Chipenda Dansokho

Denis Boutin

Jean-Marc Chouinard

Sylvie Dostie

Robert Fenton

Marley Greenberg

Jonathan McGavock