HAL Evry
Not a member yet
19237 research outputs found
Sort by
Natural versus artificial herd immunity: Is vaccine research investment always optimal?
No full textInternational audienceUnder the threat of a rapid expanding virus like the 2020 COVID-19, policy-makers need to decide relatively fast whether and under which conditions to invest in a vaccine, and eventually adopt other protective measures like social distancing or lockdowns, or to wait for natural herd immunity. Taking into account that vaccines take time to be fully developed and effective, this paper considers a unified framework at the crossroad between economics and epidemiology to study optimal public spending in medical research to obtain a vaccine against an infectious disease evolving according to a SIR dynamics. We prove that developed economies always invest in the search of a vaccine. The more individuals care about consumption, the more they actually reduce their current consumption and the more they invest in the vaccine research program to recover their consumption potential at the earliest. Our model would only recommend economies with very poor technology to restrain from investment and wait for herd immunity
A single changing hypernetwork to represent (social-)ecological dynamics
No full textInternational audienceTo understand and manage (social-)ecological systems, we need an intuitive and rigorous way to represent them. Recent ecological studies propose to represent interaction networks into modular graphs, multiplexes and higher-order interactions. Along these lines, we argue here that non-dyadic (non-pairwise) interactions are common in ecology and environmental sciences, necessitating fresh concepts and tools for handling them. In addition, such interaction networks often change sharply, due to appearing and disappearing species and components. We illustrate in a simple example that any ecosystem can be represented by a single hypergraph, here called the ecosystem hypernetwork. Moreover, we highlight that any ecosystem hypernetwork exhibits a changing topology summarizing its long term dynamics (e.g., species extinction/invasion, pollutant or human arrival/migration). Qualitative and discrete-event models developed in computer science appear suitable for modeling hypergraph (topological) dynamics. Hypernetworks thus also provide a conceptual foundation for theoretical as well as more applied studies in ecology (at large), as they form the qualitative backbone of ever-changing ecosystems
Audiocarnet - DJ Mix Transcription with Multi-Pass Non-Negative Matrix Factorization
No full textThis work uses music licensed under the CC-BY-NC licence.Robbero, Doxent Zsigmond, annabloom - Revolver - http://dig.ccmixter.org/files/Robbero/44106Attic Ella, CSoul, Speck, SackJo22, Doxent Zsigmond - Wind Chimes - http://dig.ccmixter.org/files/Levihica/49724DJs and DJ mixing have been a part of our cultural landscape for decades, representing the art of selecting and transforming recorded media.DJs use techniques like - time-stretching, - jumps or loops, -layering multiple tracks together, - and other effects.DJ mix reverse engineering is the process of breaking down mixes into the tracks used, and analyzing the creative choices made by the DJ.More specifically, we focus on DJ mix transcription. Given a recording of a mix and the recordings of its constituent tracks, our goal is to determine when and how each track was played.We use a matrix-based model of the mixing process. This allows us to capture all temporal transformations and all mixing gain changes in a so-called "activation matrix".We estimate this matrix’s values using Non-negative Matrix Factorization, or NMF. To handle the long durations typical of DJ mixes, we developed a multi-pass algorithm, which performs the NMF at progressively finer time resolutions.The method is promising, allowing precise extraction of mix parameters. But future work is needed to handle a wider range of transformations, such as transposition, compression, or filtering
Public discourse and socially responsible market behavior
No full textFNEGE 1*, ABS 4*International audienceWe investigate the causal impact of public discourse on socially responsible market behavior. Across three laboratory experiments, having market participants engage in public discourse generally increases market social responsibility. These positive impacts are robust to variation in several characteristics of the discourse. We provide evidence that discourse strengthens beliefs that others support socially responsible exchange. However, relaxing requirements to engage in discourse sharply reduces its effectiveness. Our findings suggest that campaigns encouraging discussion of appropriate market behavior can have sizable impacts on addressing inefficiencies due to market failures but that policies encouraging broad public engagement may be important
Modelling energy metabolism dysregulations in neuromuscular diseases: A case study of calpainopathy
No full textInternational audienceBiological modelling helps understanding complex processes, like energy metabolism, by predicting pathway compensations and equilibrium under given conditions. When deciphering metabolic adaptations, traditional experiments face challenges due to numerous enzymatic activities, needing modelling to anticipate pathway behaviours and orientate research. This paper aims to implement a constraint-based modelling method of muscular energy metabolism, adaptable to individual situations, energy demands, and complex disease-specific metabolic alterations like muscular dystrophy calpainopathy. Our calpainopathy-like model not only confirms the ATP production defect under increasing energy demands, but suggests compensatory mechanisms through anaerobic glycolysis. However, excessive glycolysis indicates a need to enhance mitochondrial respiration, preventing excess lactate production common in several diseases. Our model suggests that moderate-intensity physiotherapy, known to improve aerobic performance and anaerobic buffering, combined with increased carbohydrate and amino acid sources, could be a potent therapeutic approach for calpainopathy
Convergence Rate of the Euler-Maruyama Scheme Applied to Diffusion Processes with L Q − L ρ Drift Coefficient and Additive Noise
No full textInternational audienceWe are interested in the time discretization of stochastic differential equations with additive d-dimensional Brownian noise and L q − L ρ drift coefficient when the condition d ρ + 2 q < 1, under which Krylov and Röckner [26] proved existence of a unique strong solution, is met. We show weak convergence with order 1 2 (1 − (d ρ + 2 q)) which corresponds to half the distance to the threshold for the Euler scheme with randomized time variable and cutoffed drift coefficient so that its contribution on each time-step does not dominate the Brownian contribution. More precisely, we prove that both the diffusion and this Euler scheme admit transition densities and that the difference between these densities is bounded from above by the time-step to this order multiplied by some centered Gaussian density
Automatic proximal and distal thrombi segmentation
No full textInternational audienceAbstract N°569Background and aims: In stroke patient treatment, timely decision-making is crucial. Identifying, localizing, and measuring occlusive arterial thrombi during initial imaging is a complex but critical step. And even though some image processing methods [1,2] and deep learning models [3] have been proposed for this purpose, all of them use CT images for acute stroke patients. We present a deep-tech artificial intelligence solution that uses MRI images for hyper-acute proximal and distal thrombi.Methods: Using a database of 150 patients imaged during the hyper-acute phase of a stroke, with lesions and thrombi manually segmented by experts, we trained a model that computes a cross-modality attention between the diffusion modality (DWI) and the susceptibility-weighted modalities to relate the lesion and the thrombi. A recurrent model is then used to transfer the information in between multiple brain slices. Finally, from the distances between the thrombi and the lesion prediction performed by a classic 3D nnUnet [4], we choose the most probable object among the predicted ones.Results: We are able to detect all the proximal thrombi and more than 90% for the distal ones on the test set. Incorporating the predicted lesion (having a performance of 77% of surface detection (Dice) with the ground-truth and 95% of detection rate) reduces the false positives to zero and identifies 58% and 52% of the surface (Dice) of distal and proximal thrombi respectively in average.Conclusions: Our automatic deep learning model swiftly detects and segments thrombi in nearly all patients, providing valuable assistance to clinicians in the initial therapeutic decision
The MKK3 MAPK cascade integrates temperature and after-ripening signals to modulate seed germination
No full textInternational audienceTemperature is a major environmental cue for seed germination. The permissive temperature range for germination is narrow in dormant seeds and expands during after-ripening. Quantitative trait loci analyses of pre-harvest sprouting in cereals have revealed that MKK3, a mitogen-activated protein kinase (MAPK) cascade protein, is a negative regulator of grain dormancy. Here we show that the MAPKKK19/20-MKK3-MPK1/2/7/14 cascade modulates germination temperature range in Arabidopsis seeds by elevating germinability of the seeds at sub- and supra-optimal temperatures. The expression of MAPKKK19 and MAPKKK20 is regulated by an unidentified temperature sensing and signaling mechanism the sensitivity of which is modulated during after-ripening of the seeds, and MPK7 is activated at the permissive temperature for germination regulated by expression levels of MAPKKK19/20 . Activation of the MKK3 cascade represses abscisic acid (ABA) biosynthesis enzyme gene expression, and induces expression of ABA catabolic enzyme and gibberellic acid biosynthesis enzyme genes, resulting in expansion of the germinable temperature range. Our data demonstrate that the MKK3 cascade integrates temperature and after-ripening signals to germination processes including phytohormone metabolism
Agricultural Policy Reforms and their Effects on Smallholder Farmers: A Comprehensive Review
No full textInternational audienceAgricultural policy reforms are critical in influencing the economic, social, and environmental conditions of smallholder farmers, who represent a significant portion of the agricultural workforce in developing countries. These reforms, implemented through various strategies such as market liberalization, subsidy adjustments, and land tenure reforms, have far-reaching implications. This review article delves into the multifaceted effects of agricultural policy reforms on smallholder farmers, providing a comprehensive analysis of their historical context, economic impacts, social outcomes, and environmental consequences. By examining the evolution of these policies from the pre-structural adjustment era to the present, we aim to shed light on both the successes and challenges faced by smallholder farmers. The review also highlights case studies from different regions, illustrating the diverse experiences of smallholder farmers under varying policy frameworks. Ultimately, this article offers policy recommendations designed to enhance the resilience and productivity of smallholder farming communities, ensuring sustainable development and food security in the face of global challenges such as climate change and economic volatility