Incomplete graphical model inference via latent tree aggregation

Graphical network inference is used in many fields such as genomics or ecology to infer the conditional independence structure between variables, from measurements of gene expression or species abundances for instance. In many practical cases, not all variables involved in the network have been observed, and the samples are actually drawn from a distribution where some variables have been marginalized out. This challenges the sparsity assumption commonly made in graphical model inference, since marginalization yields locally dense structures, even when the original network is sparse. We present a procedure for inferring Gaussian graphical models when some variables are unobserved, that accounts both for the influence of missing variables and the low density of the original network. Our model is based on the aggregation of spanning trees, and the estimation procedure on the Expectation-Maximization algorithm. We treat the graph structure and the unobserved nodes as missing variables and compute posterior probabilities of edge appearance. To provide a complete methodology, we also propose several model selection criteria to estimate the number of missing nodes. A simulation study and an illustration flow cytometry data reveal that our method has favorable edge detection properties compared to existing graph inference techniques. The methods are implemented in an R package

Link Prediction in the Stochastic Block Model with Outliers

The Stochastic Block Model is a popular model for network analysis in the presence of community structure. However, in numerous examples, the assumptions underlying this classical model are put in default by the behaviour of a small number of outlier nodes such as hubs, nodes with mixed membership profiles, or corrupted nodes. In addition, real-life networks are likely to be incomplete, due to non-response or machine failures. We introduce a new algorithm to estimate the connection probabilities in a network, which is robust to both outlier nodes and missing observations. Under fairly general assumptions, this method detects the outliers, and achieves the best known error for the estimation of connection probabilities with polynomial computation cost. In addition, we prove sub-linear convergence of our algorithm. We provide a simulation study which demonstrates the good behaviour of the method in terms of outliers selection and prediction of the missing links

Preferences and perceptions of pharmacy students on the sectoral development of community pharmacy in Belgium

peer reviewedIntroduction:Building the future of the pharmacist profession today must be done by listening to the actors of tomorrow. Their wishes and main motivations must be integrated into reflections. The university needs to understand how students plan for their future professions. Consistency between teaching and sectoral development is at the heart of university concerns: anticipating professional changes can help the academic body build flexible programmes to align with professional development and best prepare actors of tomorrow.Objectives:To assess the preferences and perception of Master's students in pharmaceutical sciences among various potential sectoral evolution in the field of pharmacies open to the public. This researchquestions how future pharmacists rank in order of importance and preference for the potential sectoral developments in their profession.Methods: An online questionnaire was sent to Belgian student in pharmaceutical sciences to understand their preferences concerning the various missions expected to be part of the role of pharmacists in the years to come. Some of these missions already exist in Belgium, others already exist abroad, and others still need to be the responsibility of the pharmacist at present. The questionnaire used a best-worst scaling (BWS) approach to determine a hierarchy of preferences on a set of attributes describing the potential sectoral developments in community 389pharmacists. The BWS then makes it possible to classify preferences based on choices and to compare preferences among all the attributes considered. Respondents do not only express their preferences among the proposed attributes but also provide information through their responses as to the most preferable and least preferable attributes in their eyes. The research team agreed on a list of 18 attributes to characterize the profession of community pharmacists and its potential sectoral developments. The 18 attributes were: preparation and dispensing of medication, pharmaceutical care, adjustment/substitution, continuity of treatment, care monitoring/risk prevention, medication review, self-medication, prescription, adherence support, health prevention and promotion, drug analysis, inter-professional collaboration, pharmaceutical care, vaccination, screening, withdrawal/deprescription, return home after hospitalization and home care.Results: The topics for which students showed the greatest interest were delivery of medication with advice on the proper use, prevention, identifying and resolving potential drug-related problems or even assisting the patient in a self-medication situation.The themes with the lowest interest were Greenpharmacy, the collection of used products and sustainable practices.Conclusion: Future pharmacists do not wish to replace medical doctorsand have little interest in diagnosis, prescription and laboratory analysis. Moreover, the lack of interest of future pharmacists in Greenpharmacy raises questions. Making students aware of this significant environmental challenge should be encouraged

Generative methods for sampling transition paths in molecular dynamics

Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one – a behavior known as metastability. Simulating transition paths linking one metastable state to another one is difficult by direct numerical methods. In view of the promises of machine learning techniques, we explore in this work two approaches to more efficiently generate transition paths: sampling methods based on generative models such as variational autoencoders, and importance sampling methods based on reinforcement learning

Favoriser l'apprentissage de la traversée d'un passage piéton pour des jeunes avec une déficience intellectuelle grâce à la réalité virtuelle

National audienceVirtual Reality (VR) has three main advantages: allowing safe simulations, experimenting different conditions for the same scenario, and providing the perfect replicability of the scenarios. We present a feasibility study on the use of VR and VR immersive headsets to assist young adults (10-18) with intellectual disabilities in learning new skills. We focused on the scenario of a pedestrian crossing without traffic lights, and we considered several environmental conditions (day/night, weather, kindness of drivers, etc.). Our study is not limited to young people with autism spectrum disorder but takes into account young adults with intellectual disability with an associated disorder. 15 young people participated in the study showing a very good acceptability of immersive headsets and a noticeable learning effect already after a short training session. However, a longer and more extensive study is needed to evaluate the transfer of learning to reality.La réalité virtuelle (VR) a trois avantages : simuler en toute sécurité, proposer différentes conditions pour une action identique et rejouer un scénario de manière contrôlée. Nous présentons une étude de faisabilité concernant l'utilisation de la VR (casques immersifs) pour accompagner dans l'apprentissage de nouvelles compétences des jeunes adultes (10-18) avec une déficience intellectuelle (DI). Nous nous focalisons sur le scénario d'un passage piéton, en considérant plusieurs conditions environnementales (jour/nuit, gentillesse des conducteurs, etc.). Notre étude ne se limite pas aux jeunes avec trouble du spectre de l'autisme mais prend en compte les 10-18 ans ayant une DI avec un trouble associé. 15 jeunes ont participé à l'étude en montrant une bonne acceptabilité du casque immersif et un effet d'apprentissage déjà après une courte session d'entrainement. Une étude plus étendue et de longue durée est nécessaire pour évaluer le transfert de l'apprentissage dans la réalité

Diagnostic Value of FDG PET-CT Quantitative Parameters and Deauville-Like 5 Point-Scale in Predicting Malignancy of Focal Thyroid Incidentaloma

Objective: To evaluate the diagnostic value of FDG PET-CT metabolic parameters and Deauville-like 5 point-scale to predict malignancy in a population of patients presenting focal thyroid incidentaloma (fTI).Design: This retrospective study included 41 fTI, classified according to cytological and histological data as benign (BL) or malignant lesion (ML). FDG PET-CT semi-quantitative parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG), tumor to liver SUVmean ratio (TLRmax and TLRmean), tumor to blood-pool SUVmean ratio (TBRmax and TBRmean) were calculated. Each fTI was also classified on a Deauville-like 5-point scale (DS) currently used in lymphoma. Comparison between BL and ML was performed for each parameter and a ROC analysis was conducted.Results: All quantitative PET metabolic parameters (SUV parameters, volume based parameters and SUV ratio) were higher in ML compared with BL, yet no significant difference was reported. fTI (uptake) malignancy rate according to DS grades 2, 3, 4, and 5 was, respectively, 25% (1 of 4), 28.6% (2 of 7), 8.3% (1 of 12), and 33.3% (6 of 18) with no significant difference between ML and BL groups. Results of ROC analysis showed that mean TBR had the highest AUC in our cohort (0.66 95%CI [0.41; 0.91]) with a cut-off value of 2.2. Specificity of MTV and TLG was 100% (cut-off values: MTV 9.6 ml, TLG 22.9 g) and their sensitivity was 30 and 40%, respectively.Conclusion: Our study did not highlight any FDG PET/CT parameter predictor of fTI malignancy

Generation of Priority Research Questions to Inform Conservation Policy and Management at a National Level

Integrating knowledge from across the natural and social sciences is necessary to effectively address societal tradeoffs between human use of biological diversity and its preservation. Collaborative processes can change the ways decision makers think about scientific evidence, enhance levels of mutual trust and credibility, and advance the conservation policy discourse. Canada has responsibility for a large fraction of some major ecosystems, such as boreal forests, Arctic tundra, wetlands, and temperate and Arctic oceans. Stressors to biological diversity within these ecosystems arise from activities of the country's resource-based economy, as well as external drivers of environmental change. Effective management is complicated by incongruence between ecological and political boundaries and conflicting perspectives on social and economic goals. Many knowledge gaps about stressors and their management might be reduced through targeted, timely research. We identify 40 questions that, if addressed or answered, would advance research that has a high probability of supporting development of effective policies and management strategies for species, ecosystems, and ecological processes in Canada. A total of 396 candidate questions drawn from natural and social science disciplines were contributed by individuals with diverse organizational affiliations. These were collaboratively winnowed to 40 by our team of collaborators. The questions emphasize understanding ecosystems, the effects and mitigation of climate change, coordinating governance and management efforts across multiple jurisdictions, and examining relations between conservation policy and the social and economic well-being of Aboriginal peoples. The questions we identified provide potential links between evidence from the conservation sciences and formulation of policies for conservation and resource management. Our collaborative process of communication and engagement between scientists and decision makers for generating and prioritizing research questions at a national level could be a model for similar efforts beyond Canada

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder