Publications

Global epidemiology of SARS-CoV-2 infection: a systematic review and meta-analysis of standardized population-based seroprevalence studies, Jan 2020-Oct 2021
Isabel Bergeri
Mairead Whelan
Harriet Ware
Lorenzo Subissi
Anthony Nardone
H. Lewis
Zihan Li
Xiaomeng Ma
Marta Valenciano
Brianna Cheng
Lubna Al Ariqi
Arash Rashidian
Joseph Okeibunor
Tasnim Azim
Pushpa Wijesinghe
Linh-Vi Le
Aisling Vaughan
Richard Pebody
Andrea Vicari
Tingting Yan … (voir 8 de plus)
Mercedes Yanes-Lane
Christian Cao
Matthew P. Cheng
Jesse Papenburg
David Buckeridge
Niklas Bobrovitz
Rahul K. Arora
Maria D Van Kerkhove
Background COVID-19 case data underestimates infection and immunity, especially in low- and middle-income countries (LMICs). We meta-analyze… (voir plus)d standardized SARS-CoV-2 seroprevalence studies to estimate global seroprevalence. Objectives/Methods We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence studies aligned with the WHO UNITY protocol published between 2020-01-01 and 2021-10-29. Eligible studies were extracted and critically appraised in duplicate. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate under-ascertainment; meta-analyzed differences in seroprevalence between demographic subgroups; and identified national factors associated with seroprevalence using meta-regression. PROSPERO: CRD42020183634. Results We identified 396 full texts reporting 736 distinct seroprevalence studies (41% LMIC), including 355 low/moderate risk of bias studies with national/sub-national scope in further analysis. By April 2021, global SARS-CoV-2 seroprevalence was 26.1%, 95% CI [24.6-27.6%]. Seroprevalence rose steeply in the first half of 2021 due to infection in some regions (e.g., 18.2% to 45.9% in Africa) and vaccination and infection in others (e.g., 11.3% to 57.4% in the Americas high-income countries), but remained low in others (e.g., 0.3% to 1.6% in the Western Pacific). In 2021 Q1, median seroprevalence to case ratios were 1.9:1 in HICs and 61.9:1 in LMICs. Children 0-9 years and adults 60+ were at lower risk of seropositivity than adults 20-29. In a multivariate model using data pre-vaccination, more stringent public health and social measures were associated with lower seroprevalence. Conclusions Global seroprevalence has risen considerably over time and with regional variation, however much of the global population remains susceptible to SARS-CoV-2 infection. True infections far exceed reported COVID-19 cases. Standardized seroprevalence studies are essential to inform COVID-19 control measures, particularly in resource-limited regions.
2021-12-15
medRxiv (prépublication)
doi.org
Preference for biological motion is reduced in ASD: implications for clinical trials and the search for biomarkers
Luke Mason
F. Shic
T. Falck-Ytter
Bhismadev Chakrabarti
Tony Charman
Eva Loth
Julian Tillmann
Tobias Banaschewski
Simon Baron-Cohen
Sven Bölte
J. Buitelaar
Sarah Durston
Bob Oranje
Antonio Persico
C. Beckmann
Thomas Bougeron
Flavio Dell’Acqua
Christine Ecker
Carolin Moessnang
D. Murphy … (voir 49 de plus)
M. H. Johnson
Emily J. H. Jones
Jumana Sara Sarah Carsten Michael Daniel Claudia Yvette Chris Ineke Daisy Guillaume Jessica Vincent Pilar David Lindsay Joerg Rosemary Meng-Chuan Xavier Liogier Michael V. David J. René Andre Maarten Andreas Nico Bethany Laurence Marianne Gahan Barbara Amber Jessica Roberto Antonia San José Emily Will Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Jumana Sara Sarah Carsten Michael Daniel Claudia Yvette C Ahmad Ambrosino Baumeister Bours Brammer Brandeis
Jumana Ahmad
Sara Ambrosino
Sarah Baumeister
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Christopher H. Chatham
Ineke Cornelissen
Daisy Crawley
Guillaume Dumas
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Joerg F. Hipp
Rosemary Holt
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Nico Bast
Beth Oakley
Laurence O’Dwyer
Marianne Oldehinkel
Gahan Pandina
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
2021-12-15
Molecular Autism (publié)
doi.org
Decision Referrals in Human-Automation Teams
Kesav Kaza
Jerome Le Ny
Aditya Mahajan
We consider a model for optimal decision referrals in human-automation teams performing binary classification tasks. The automation observes… (voir plus) a batch of independent tasks, analyzes them, and has the option to refer a subset of them to a human operator. The human operator performs fresh analysis of the tasks referred to him. Our key modeling assumption is that the human performance degrades with workload (i.e., the number of tasks referred to human). We model the problem as a stochastic optimization problem. We first consider the special case when the workload of the human is pre-specified. We show that in this setting it is optimal to myopically refer tasks which lead to the largest reduction in the conditional expected cost until the desired workload target is met. We next consider the general setting where there is no constraint on the workload. We leverage the solution of the previous step and provide a search algorithm to efficiently find the optimal set of tasks to refer. Finally, we present a numerical study to compare the performance of our algorithm with some baseline allocation policies.
2021-12-14
IEEE Conference on Decision and Control (publié)
doi.org
Mean-field approximation for large-population beauty-contest games
Raihan Seraj
Jerome Le Ny
Aditya Mahajan
We study a class of Keynesian beauty contest games where a large number of heterogeneous players attempt to estimate a common parameter base… (voir plus)d on their own observations. The players are rewarded for producing an estimate close to a certain multiplicative factor of the average decision, this factor being specific to each player. This model is motivated by scenarios arising in commodity or financial markets, where investment decisions are sometimes partly based on following a trend. We provide a method to compute Nash equilibria within the class of affine strategies. We then develop a mean-field approximation, in the limit of an infinite number of players, which has the advantage that computing the best-response strategies only requires the knowledge of the parameter distribution of the players, rather than their actual parameters. We show that the mean-field strategies lead to an ε-Nash equilibrium for a system with a finite number of players. We conclude by analyzing the impact on individual behavior of changes in aggregate population behavior.
2021-12-14
IEEE Conference on Decision and Control (publié)
doi.org
Thompson sampling for linear quadratic mean-field teams
Mukul Gagrani
Sagar Sudhakara
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
We consider optimal control of an unknown multi-agent linear quadratic (LQ) system where the dynamics and the cost are coupled across the ag… (voir plus)ents through the mean-field (i.e., empirical mean) of the states and controls. Directly using single-agent LQ learning algorithms in such models results in regret which increases polynomially with the number of agents. We propose a new Thompson sampling based learning algorithm which exploits the structure of the system model and show that the expected Bayesian regret of our proposed algorithm for a system with agents of |M| different types at time horizon T is
2021-12-14
2021 60th IEEE Conference on Decision and Control (CDC) (publié)
doi.org
arxiv.org
Early Transcriptional Changes in Rabies Virus-Infected Neurons and Their Impact on Neuronal Functions
Seonhee Kim
Florence Larrous
Hugo Varet
Rachel Legendre
Lena Feige
Guillaume Dumas
Rebecca Matsas
Georgia Kouroupi
Regis Grailhe
Hervé Bourhy
2021-12-13
Frontiers in Microbiology (publié)
doi.org
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
Stephen Bonner
Ufuk Kirik
Ola Engkvist
Jian Tang
I. Barrett
Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utiliz… (voir plus)e the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.
2021-12-13
ArXiv (preprint)
doi.org
arxiv.org
A taxonomy of weight learning methods for statistical relational learning
Sriram Srinivasan
Charles Dickens
Eriq Augustine
Golnoosh Farnadi
Lise Getoor
2021-12-13
Machine-mediated learning (published)
doi.org
A taxonomy of weight learning methods for statistical relational learning
Sriram Srinivasan
Charles Dickens
Eriq Augustine
Golnoosh Farnadi
Lise Getoor
2021-12-13
Machine-mediated learning (publié)
doi.org
Artificial Intelligence in Surgical Education: Considerations for Interdisciplinary Collaborations
Elif Bilgic
A. Gorgy
Meredith Young
Samira Abbasgholizadeh-Rahimi
Jason M. Harley
Arti fi cial intelligence (AI) based devices are currently being used in the delivery of surgical care in a variety of settings. 1,2 Howeve… (voir plus)r, AI-enabled systems can trigger a variety of opinions and emotions, which reveals the different lenses that shape views on AI. Nonethless, work within surgical education may necessitate a more balanced view; with an acknowledgment of the participation of AI-enhanced devices in the delivery of surgical care and education
2021-12-10
Surgical Innovation (published)
doi.org
Artificial Intelligence in Surgical Education: Considerations for Interdisciplinary Collaborations
Elif Bilgic
Andrew Gorgy
Meredith Young
Samira Abbasgholizadeh-Rahimi
Jason M. Harley
2021-12-10
Surgical Innovation (publié)
doi.org
Few Shot Image Generation via Implicit Autoencoding of Support Sets
Andy Huang
Kuan-Chieh Wang
Guillaume Rabusseau
Alireza Makhzani
Recent generative models such as generative adversarial networks have achieved remarkable success in generating realistic images, but they r… (voir plus)equire large training datasets and computational resources. The goal of few-shot image generation is to learn the distribution of a new dataset from only a handful of examples by transferring knowledge learned from structurally similar datasets. Towards achieving this goal, we propose the “Implicit Support Set Autoencoder” (ISSA) that adversarially learns the relationship across datasets using an unsupervised dataset representation, while the distribution of each individual dataset is learned using implicit distributions. Given a few examples from a new dataset, ISSA can generate new samples by inferring the representation of the underlying distribution using a single forward pass. We showcase significant gains from our method on generating high quality and diverse images for unseen classes in the Omniglot and CelebA datasets in few-shot image generation settings.
2021-12-10
NeurIPS.cc/2021/Workshop/MetaLearn (poster)
openreview.net
openreview.net