Publications

Assessing the inclusion of children’s surgical care in National Surgical, Obstetric and Anaesthesia Plans: a policy content analysis
Sabrina Wimmer
Paul Truche
Elena Guadagno
Emmanuel Ameh
Lubna Samad
Emmanuel Mwenda Malabo Makasa
Sarah Greenberg
John G Meara
Tonnis H van Dijk
Dan Poenaru
Objective While National Surgical, Obstetric and Anaesthesia Plans (NSOAPs) have emerged as a strategy to strengthen and scale up surgical h… (voir plus)ealthcare systems in low/middle-income countries (LMICs), the degree to which children’s surgery is addressed is not well-known. This study aims to assess the inclusion of children’s surgical care among existing NSOAPs, identify practice examples and provide recommendations to guide inclusion of children’s surgical care in future policies. Design We performed two qualitative content analyses to assess the inclusion of children’s surgical care among NSOAPs. We applied a conventional (inductive) content analysis approach to identify themes and patterns, and developed a framework based on the Global Initiative for Children’s Surgery’s Optimal Resources for Children’s Surgery document. We then used this framework to conduct a directed (deductive) content analysis of the NSOAPs of Ethiopia, Nigeria, Rwanda, Senegal, Tanzania and Zambia. Results Our framework for the inclusion of children’s surgical care in NSOAPs included seven domains. We evaluated six NSOAPs with all addressing at least two of the domains. All six NSOAPs addressed ‘human resources and training’ and ‘infrastructure’, four addressed ‘service delivery’, three addressed ‘governance and financing’, two included ‘research, evaluation and quality improvement’, and one NSOAP addressed ‘equipment and supplies’ and ‘advocacy and awareness’. Conclusions Additional focus must be placed on the development of surgical healthcare systems for children in LMICs. This requires a focus on children’s surgical care separate from adult surgical care in the scaling up of surgical healthcare systems, including children-focused needs assessments and the inclusion of children’s surgery providers in the process. This study proposes a framework for evaluating NSOAPs, highlights practice examples and suggests recommendations for the development of future policies.
2023-03-31
BMJ Open (publié)
doi.org
Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys
Marco Bonizzato
Rose Guay Hottin
Sandrine L. Côté
Elena Massai
Leo Choiniere
Uzay Macar
Samuel Laferrière
Parikshat Sirpal
Stephan Quessy
Guillaume Lajoie
Marina Martinez
Numa Dancause
Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged ne… (voir plus)urostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming other search strategies after testing only a fraction of the possible combinations. Through a series of real-time multi-dimensional neurostimulation experiments, we demonstrate optimization across diverse biological targets (brain, spinal cord), animal models (rats, non-human primates), in healthy subjects, and in neuroprosthetic intervention after injury, for both immediate and continual learning over multiple sessions. GP-BO can embed and improve “prior” expert/clinical knowledge to dramatically enhance its performance. These results advocate for broader establishment of learning agents as structural elements of neuroprosthetic design, enabling personalization and maximization of therapeutic effectiveness.
2023-03-31
Cell Reports Medicine (publié)
doi.org
Biomedical discovery through the integrative biomedical knowledge hub (iBKH)
Chang Su
Yu Hou
Manqi Zhou
Suraj Rajendran
Jacqueline R.M. A. Maasch
Zehra Abedi
Haotan Zhang
Zilong Bai
Anthony Cuturrufo
Winston Guo
Fayzan F. Chaudhry
Gregory Ghahramani
Jian Tang
Feixiong Cheng
Yue Li
Rui Zhang
Steven T. DeKosky
Jiang Bian
Fei Wang
Summary The massive and continuously increasing volume of biomedical knowledge derived from biological experiments or gained from healthcare… (voir plus) practices has become an invaluable treasure for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In the present study, we harmonized and integrated data from diverse biomedical resources to curate a comprehensive BKG, named the integrative Biomedical Knowledge Hub (iBKH). To facilitate the usage of iBKH in biomedical research, we developed a web-based, easy-to-use, publicly available graphical portal that allows fast, interactive, and visualized knowledge retrieval in iBKH. Furthermore, an efficient and scalable graph learning pipeline was developed for novel knowledge discovery in iBKH. As a proof of concept, we performed our iBKH-based method for computational in silico drug repurposing for Alzheimer’s disease. The iBKH is publicly available at: http://ibkh.ai/ .
2023-03-31
iScience (publié)
doi.org
Deep learning-enabled anomaly detection for IoT systems
Adel Abusitta 0001
Adel Abusitta
Glaucio H.S. Carvalho
Omar Abdel Wahab
Talal Halabi
Benjamin C. M. Fung
Saja Al-Mamoori
2023-03-31
Internet of Things (publié)
doi.org
Facing AI extinction
David M. Krueger
2023-03-31
New Scientist (publié)
doi.org
Genesis, modelling and methodological remedies to autism heterogeneity
Juliette Rabot
Eya‐mist Rødgaard
Ridha Joober
Guillaume Dumas
Danilo Bzdok
Boris C Bernhardt
Sébastien Jacquemont
Laurent Mottron
2023-03-31
Neuroscience and Biobehavioral Reviews (publié)
doi.org
Machine-learning-based arc selection for constrained shortest path problems in column generation
Mouad Morabit
Guy Desaulniers
Andrea Lodi
Column generation is an iterative method used to solve a variety of optimization problems. It decomposes the problem into two parts: a maste… (voir plus)r problem and one or more pricing problems (PP). The total computing time taken by the method is divided between these two parts. In routing or scheduling applications, the problems are mostly defined on a network, and the PP is usually an NP-hard shortest path problem with resource constraints. In this work, we propose a new heuristic pricing algorithm based on machine learning. By taking advantage of the data collected during previous executions, the objective is to reduce the size of the network and accelerate the PP, keeping only the arcs that have a high chance to be part of the linear relaxation solution. The method has been applied to two specific problems: the vehicle and crew scheduling problem in public transit and the vehicle routing problem with time windows. Reductions in computational time of up to 40% can be obtained.
2023-03-31
INFORMS Journal on Optimization (publié)
doi.org
arxiv.org
Picture Cards Versus Physical Examination: A Proof-of-Concept Study to Improve the SOSAS Survey Tool.
Adesoji Ademuyiwa
Benedict C. Nwomeh
Dan Poenaru
Justina O. Seyi-Olajide
Iyabo Y. Ademuyiwa
Tinuola O. Odugbemi
Ogechi Abazie
Oluwaseun A. Ladipo-Ajayi
Olufemi Bankole
Olumide A. Elebute
Babasola Okusanya
Felix M. Alakaloko
Eyitayo O. Alabi
Ayomide Makanjuola
Shailvi Gupta
Tu Tran
Amanda Onwuka A
Emily R. Smith
Riinu Pius
Ewen Harrison … (voir 1 de plus)
Christopher O. Bode
2023-03-31
Journal of Surgical Research (publié)
doi.org
A Survey of Self-Supervised and Few-Shot Object Detection
Gabriel Huang
Issam Hadj Laradji
David Vázquez
Simon Lacoste-Julien
Pau Rodríguez
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require … (voir plus)dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.
2023-03-31
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
arxiv.org
Bayesian stroke modeling details sex biases in the white matter substrates of aphasia
Julius M. Kernbach
Gesa Hartwigsen
Jae-Sung Lim
Hee-Joon Bae
Kyung-Ho Yu
Gottfried Schlaug
Anna Bonkhoff
Natalia S. Rost
Danilo Bzdok
Ischemic cerebrovascular events often lead to aphasia. Previous work provided hints that such strokes may affect women and men in distinct w… (voir plus)ays. Women tend to suffer strokes with more disabling language impairment, even if the lesion size is comparable to men. In 1401 patients, we isolate data-led representations of anatomical lesion patterns and hand-tailor a Bayesian analytical solution to carefully model the degree of sex divergence in predicting language outcomes ~3 months after stroke. We locate lesion-outcome effects in the left-dominant language network that highlight the ventral pathway as a core lesion focus across different tests of language performance. We provide detailed evidence for sex-specific brain-behavior associations in the domain-general networks associated with cortico-subcortical pathways, with unique contributions of the fornix in women and cingular fiber bundles in men. Our collective findings suggest diverging white matter substrates in how stroke causes language deficits in women and men. Clinically acknowledging such sex disparities has the potential to improve personalized treatment for stroke patients worldwide.
2023-03-30
Communications Biology (publié)
doi.org
MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
Mohammad Khodadad
Morteza Rezanejad
Ali Shiraee Kasmaee
Kaleem Siddiqi
Dirk Bernhardt-Walther
Hamidreza Mahyar
2023-03-30
ArXiv (prépublication)
doi.org
arxiv.org
Varepsilon kú mask: Integrating Yorùbá cultural greetings into machine translation
Idris Akinade
Jesujoba Oluwadara Alabi
David Ifeoluwa Adelani
Clement Odoje
Dietrich Klakow
This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yorùbá greetings… (voir plus) (kú mask), which are a big part of Yorùbá language and culture, into English. To evaluate these models, we present IkiniYorùbá, a Yorùbá-English translation dataset containing some Yorùbá greetings, and sample use cases. We analysed the performance of different multilingual NMT systems including Google and NLLB and show that these models struggle to accurately translate Yorùbá greetings into English. In addition, we trained a Yorùbá-English model by fine-tuning an existing NMT model on the training split of IkiniYorùbá and this achieved better performance when compared to the pre-trained multilingual NMT models, although they were trained on a large volume of data.
2023-03-30
ArXiv (prépublication)
doi.org
arxiv.org