Intelligence artificielle et chimie numérique : pour régler son cas à la lignine

Affiche présentée dans le cadre du Colloque de l'ARC, «Pour que la formation de la relève scientifique soit sur toutes les lèvres», dans le cadre du 87e Congrès de l'Acfas, Université du Québec en Outaouais (UQO), Gatineau, le 28 mai 2019.La lignine, un coproduit de l’industrie papetière, est le deuxième biopolymère le plus abondant sur Terre après la cellulose. Actuellement, cette lignine résiduelle est majoritairement brûlée pour produire de la chaleur. On peut donc espérer une meilleure valorisation de la lignine, mais cette amélioration passe par la connaissance de sa structure moléculaire. Comme il s’agit d’un polymère désordonné, formé d’un grand nombre d’unités fondamentales assemblées de façon combinatoire, l’intelligence artificielle (IA) aiderait à élucider le problème, et ce, à l’image de l’industrie pharmaceutique, qui a déjà recours à ces techniques pour la recherche sur de nouveaux médicaments. Pour déterminer la structure moléculaire d’une lignine à partir d’un ou de plusieurs résultats expérimentaux, il est proposé de créer numériquement une multitude de lignines potentielles et de calculer théoriquement les propriétés physiques de chacune. Cette banque de lignines alimentera ensuite un procédé d’IA qui pourra, par interpolation, produire une structure moléculaire émulant des résultats expérimentaux qu'on lui aura fournis. Déjà, de nouveaux outils ont été créés pour fabriquer des lignines numériques semblables aux lignines naturelles. L’implantation d’outils de calculs de propriétés moléculaires est en cours et le matériel permettant les calculs d’intelligence artificielle est en place. Ici comme dans bien des domaines, l'IA semble être un outil prometteur pour l’avancement des connaissances

Nanomechanical testing for crystal plasticity constitutive framework identification at high strain rates

Shot-Peening (SP) is a surface mechanical treatment that consists in propelling hard particles, called shot, onto a ductile metallic surface at high velocity to induce subsurface residual compressive stresses. It is widely used in the industry to increase fatigue life and wear resistance of treated parts. Shot-peening induced macroscopic residual stresses (e.g. Type I) predictions using finite element analysis or analytical method is today already well assessed. However, recent works [1] revealed that spherical indentation in specific crystal orientations could induce subsurface intragranular tensile stresses. In the shot-peening context, such intra-granular (e.g. Type III) residual stresses could influence structure’s High Cycle Fatigue (HCF) behavior and macroscopic residual stresses stability over the load cycles It would also favor early stage plasticity and crack initiation. Shot-peening simulations at the crystal scale would therefore provide essential quantitative inputs for treated parts fatigue life prediction. Such simulations require to select relevant constitutive frameworks representing the crystal behavior at high strain rate (up to 106 s-1) and accounting for repeated impact induced cyclic effects. Also, identification of such behavior will require mechanical tests at the crystal scale under process-representative test conditions. In the present work, a new methodology for crystal plasticity inverse identification for large strain rate ranges is developed. It relies on high-strain rate micropillar compression tests performed with a recently developed nano-indenter test apparatus [2], at strain rates up to 102 s-1. Micropercussion induced residual imprints are also experimentally generated to provide material behavior inputs at higher strain rates. Both tests are combined for inverse identification of two different crystal plasticity constitutive frameworks for copper. Unicity and stability of the given coefficients are studied using cost function plots and an identifiability indicator developed by Renner et al. [3]. Further works will focus on high strain rates Berkovich indentation tests to complete the developed methodology. Experimental data will also be generated at higher strain rates and for repeated impacts, using a currently developed impact shot gun that will propel shots at shot-peening velocity with a spatial accuracy of . [1] S. Breumier, A. Villani, C. Maurice et M. &. K. G. Lévesque, «Effect of crystal orientation on indentation-induced residual stress field: simulation and experimental validation,» Materials & Design, vol. 169, 2019. [2] G. Guillonneau, M. Mieszala, J. Wehrs, J. Schwiedrzik, S. Grop, D. Frey, L. Philippe, J.-M. Breguet, J. Michler et J. Wheeler, «Nanomechanical testing at high strain rates: New instrumentation for nanoindentation and microcompression,» Materials & Design, vol. 148, pp. 39-48, 2018. [3] E. Renner, Y. Gaillard, F. Richard, F. Amiot et P. Delobelle, «Sensitivity of the residual topography to single crystal plasticity parameters in Berkovich nanoindentation on FCC nickel,» International Journal of Plasticity, vol. 77, pp. 118 - 140, 2016

Ciprofloxacin-Resistant Shigella sonnei among Men Who Have Sex with Men, Canada, 2010.

In 2010, we observed isolates with matching pulsed-field gel electrophoresis patterns from 13 cases of ciprofloxacin-resistant Shigella sonnei in Montréal. We report on the emergence of this resistance type and a study of resistance mechanisms. The investigation suggested local transmission among men who have sex with men associated with sex venues

Deep learning of chest X‑rays can predict mechanical ventilation outcome in ICU‑admitted COVID‑19 patients

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions

Barriers and opportunities for improving dog bite prevention and dog management practices in northern Indigenous communities

Globally, people living in northern Indigenous communities are at higher risk of dog bites than the rest of the population living in North America, with annual incidence ranging from 0.61 to 59.6/10,000 inhabitants. Considering that rabies is endemic in wild canid populations in certain regions of the Arctic, the prevention of dog bites and the management of dog populations are of crucial importance for public health in these contexts. Most northern communities lack access to veterinary services, mainly due to their remote geographical location and to limited financial resources. Currently, northern Indigenous communities are using different approaches and strategies to prevent dog bites and manage dog populations, but the effectiveness of these approaches sometimes lacks evidence, and their low acceptability may affect their implementation. This study aims to describe (1) the current access and uses of veterinary services, and (2) the perceived barriers and opportunities related to dog population management practices currently implemented, or that could be implemented, in a Naskapi community and an Innu community located in northern Quebec (Canada). Quantitative data were collected through a survey to inhabitants on veterinary services (n = 122). Qualitative data were collected using individual interviews to inhabitants and health professionals to describe how dog population management measures were perceived, and to identify barriers and opportunities related to their implementation (n = 37). Descriptive and inferential analysis (quantitative data) and thematic analysis (qualitative data) were performed. Results show that the two main measures implemented at the time of the study – dog culling and short-duration veterinary clinics – were not perceived as fully acceptable and sustainable. Reinforcing access to veterinary services and other dog-related services, such as shelters and training programs on dogs, was identified as a need to improve dog bites prevention and dog population management in remote Indigenous communities. The implementation of animal health measures should be decided by concerned Indigenous communities to follow decolonial practices. It includes ensuring informed consent of dog owners, improving communication before, during and after interventions, separating veterinary services from rehoming and, most importantly giving back to Indigenous communities the complete leadership over animal health in their communities

Implementation of recommendations on the use of corticosteroids in severe COVID-19

Importance Research diversity and representativeness are paramount in building trust, generating valid biomedical knowledge, and possibly in implementing clinical guidelines. Objectives To compare variations over time and across World Health Organization (WHO) geographic regions of corticosteroid use for treatment of severe COVID-19; secondary objectives were to evaluate the association between the timing of publication of the RECOVERY (Randomised Evaluation of COVID-19 Therapy) trial (June 2020) and the WHO guidelines for corticosteroids (September 2020) and the temporal trends observed in corticosteroid use by region and to describe the geographic distribution of the recruitment in clinical trials that informed the WHO recommendation. Design, Setting, and Participants This prospective cohort study of 434 851 patients was conducted between January 31, 2020, and September 2, 2022, in 63 countries worldwide. The data were collected under the auspices of the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC)–WHO Clinical Characterisation Protocol for Severe Emerging Infections. Analyses were restricted to patients hospitalized for severe COVID-19 (a subset of the ISARIC data set). Exposure Corticosteroid use as reported to the ISARIC-WHO Clinical Characterisation Protocol for Severe Emerging Infections. Main Outcomes and Measures Number and percentage of patients hospitalized with severe COVID-19 who received corticosteroids by time period and by WHO geographic region. Results Among 434 851 patients with confirmed severe or critical COVID-19 for whom receipt of corticosteroids could be ascertained (median [IQR] age, 61.0 [48.0-74.0] years; 53.0% male), 174 307 (40.1%) received corticosteroids during the study period. Of the participants in clinical trials that informed the guideline, 91.6% were recruited from the United Kingdom. In all regions, corticosteroid use for severe COVID-19 increased, but this increase corresponded to the timing of the RECOVERY trial (time-interruption coefficient 1.0 [95% CI, 0.9-1.2]) and WHO guideline (time-interruption coefficient 1.9 [95% CI, 1.7-2.0]) publications only in Europe. At the end of the study period, corticosteroid use for treatment of severe COVID-19 was highest in the Americas (5421 of 6095 [88.9%]; 95% CI, 87.7-90.2) and lowest in Africa (31 588 of 185 191 [17.1%]; 95% CI, 16.8-17.3). Conclusions and Relevance The results of this cohort study showed that implementation of the guidelines for use of corticosteroids in the treatment of severe COVID-19 varied geographically. Uptake of corticosteroid treatment was lower in regions with limited clinical trial involvement. Improving research diversity and representativeness may facilitate timely knowledge uptake and guideline implementation