Publications

PD-0586 Design and assembly of a non-invasive radiation detector to measure the AIF in dynamic PET.
Liam Carroll
Y. Daoud
Shirin A. Enger
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PO-1632 deep learning-based automatic segmentation of rectal tumors in endoscopy images
A. Thibodeau-Antonacci
L. Weishaupt
Aurélie Garant
C. Miller
T. Vuong
P. Nicolaï
Shirin A. Enger
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PO-2166 Commissioning of new rectal applicator using electronic brachytherapy source
Nada Tomic
L. Liang
A. Esmaelbeigi
Jonathan Kalinowski
Shirin A. Enger
T. Vuong
S. Devic
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PO-2225 Characterization of the RBE of various photon radiation qualities on human cancer cell lines
N. Chabaytah
J. Babik
J. Li
B. Behmand
T. Connell
M. Evans
R. Ruo
H. Bekerat
Shirin A. Enger
T. Vuong
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
Protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against the omicron variant and severe disease: a systematic review and meta-regression
Niklas Bobrovitz
Harriet Ware
Xiaomeng Ma
Zihan Li
Reza Hosseini
Christian Cao
Anabel Selemon
Mairead Whelan
Zahra Premji
Hanane Issa
Brianna Cheng
Laith J Abu Raddad
David Buckeridge
Maria D Van Kerkhove
Vanessa Piechotta
Melissa M Higdon
Annelies Wilder-Smith
Isabel Bergeri
Daniel R Feikin
Rahul K. Arora … (voir 2 de plus)
Minal K Patel
Lorenzo Subissi
2023-05-01
The Lancet Infectious Diseases (publié)
doi.org
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization
Aishwarya Agrawal
Ivana Kaji'c
Emanuele Bugliarello
Elnaz Davoodi
Anita Gergely
Phil Blunsom
Aida Nematzadeh
2023-05-01
Findings of the Association for Computational Linguistics: EACL 2023 (publié)
doi.org
arxiv.org
RIGAA at the SBFT 2023 Tool Competition - Cyber-Physical Systems Track
Dmytro Humeniuk
Foutse Khomh
Giuliano Antoniol
Testing and verification of autonomous systems is critically important. In the context of SBFT 2023 CPS testing tool competition, we present… (voir plus) our tool RIGAA for generating virtual roads to test an autonomous vehicle lane keeping assist system. RIGAA combines reinforcement learning as well as evolutionary search to generate test scenarios. It has achieved the second highest final score among 5 other submitted tools.
2023-05-01
2023 IEEE/ACM International Workshop on Search-Based and Fuzz Testing (SBFT) (publié)
doi.org
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