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Nasal In situ gel of zolmitriptan loaded GMO nanoparticles for effective migraine treatment: In vitro and in vivo assessment
No full textInternational audienceThe present work focused on formulating a glyceryl monooleate (GMO) based nanoparticulate system of zolmitriptan (ZMT) tailored for intranasal delivery to enhance its brain-targeting potential in migraine treatment. ZMT loaded GMO nanoparticles (ZMT-GMO-NPs) were produced via the thin-film hydration technique. A Box–Behnken statistical design, aligned with the principles of Quality by Design (QbD), was employed to investigate the influence of key formulation parameters on essential characteristics such as particle size (PS), zeta potential (ZP), and entrapment efficiency (EE%). The finalized formulation exhibited an average particle size of 150 ± 0.6 nm, a polydispersity index (PDI) of 0.302 ± 0.001, a zeta potential of −20.8 ± 0.2 mV, and a notable entrapment efficiency of 83.3 ± 0.4 %. Compatibility of the excipients and the amorphous nature of the encapsulated drug were validated through Fourier-transform infrared spectroscopy and X-ray diffraction analyses. The optimized nanoparticles were further incorporated into a thermosensitive nasal in situ gel (NIG). In vitro release experiments demonstrated a sustained ZMT release profile from the GMO-NPs@NIG system (71 ± 0.4 %) compared to the immediate release observed with the plain ZMT-Solution (91.2 ± 0.8 %). Ex vivo studies using sheep nasal mucosa demonstrated enhanced permeability of ZMT from the GMO-NPs@NIG system. In vivo brain uptake studies in mice revealed significantly higher brain accumulation of ZMT with intranasal GMO-NPs@NIG than with oral ZMT solutions. Pharmacokinetic and pharmacodynamic evaluations confirmed improved brain targeting, enhanced bioavailability, and greater therapeutic efficacy. In conclusion, this intranasal ZMT-GMO-NP@NIG formulation offers a promising strategy for effective migraine management through targeted brain delivery
Evidence for the absence of a relationship between inflammation and cognition in a cohort of 1565 individuals with bipolar spectrum disorders: a Bayesian analysis of network
No full textInternational audiencePrevious studies have reported variable associations between peripheral inflammatory markers and cognitive functioning in individuals with bipolar spectrum disorders (BSD), with some identifying significant links and others finding no relationship. Such inconsistencies raise important questions about the role of inflammation in cognitive impairment among individuals with BSD. This study aims to investigate the relationship between peripheral inflammatory markers and cognitive function in a clinical sample of individuals with BSD using a Bayesian network analysis framework. We analyzed data from a large cohort (n = 1565) focusing on hsCRP and a subsample (n = 249) that included concurrent assessments of additional cytokines including Interleukin-6 and Tumor Necrosis Factor-alpha. A Bayesian approach was utilized to quantify uncertainty regarding the presence or absence of associations between inflammation and cognitive function. Our findings revealed no significant associations between inflammatory markers and cognitive performance in both samples. Strong evidence was found supporting the absence of association, with network analysis indicating distinct clusters for cognitive and inflammatory variables, suggesting they function as independent constructs with limited interactions. In our clinical sample of individuals with BSD, our findings do not support a direct association between some inflammatory markers and cognition, aligning with studies that found minimal or no associations. Our study emphasizes the importance of utilizing Bayesian methods to assess these relationships rigorously and suggests further exploration of individual differences and subgroup effects in future research
Organizational Barriers to the Operator 4.0, Operator 5.0 and Human-Centricity Paradigms: A Mixed Methods Study
No full textInternational audienceThis study addresses the limitations associated with the adoption of Operator 4.0, Operator 5.0, and human-centricity within manufacturing environments. Specifically, it examines the organizational barriers to the implementation of Operator 4.0, Operator 5.0, and the human-centricity paradigms. The study employed a mixed method, two-step Qualitative → Quantitative design where qualitative data analysis was integrated with advanced AI-based text-mining techniques. The findings indicate that the successful implementation of human-centricity necessitates addressing some fundamental organizational barriers, particularly those related to leadership approaches, operational efficiency, and the cognitive load of the operator. The results reveal that the most significant barriers are management-related, suggesting that technological solutions alone are insufficient without corresponding human-centric cultural changes. The categorization of organizational barriers into strong, moderate, and weak provides insight into the systemic and individual challenges that hinder the transition to a human-centric organization and the transition towards Operator 5.0 with a focus on the human-centric part
Estimation of the minimum and maximum states of charge of lithium-ion battery packs: A hybrid approach
No full textInternational audienceMonitoring the minimum and maximum states of charge (SOC) in lithium-ion battery packs is key to ensuring safe and reliable long-term operation. The challenge is that these SOCs cannot be directly measured and their corresponding cells within the pack may change with time. This paper proposes a novel hybrid scheme that estimates the minimum and maximum SOCs within a battery pack given by the series interconnection of equivalent circuit models. The estimation scheme relies on a mechanism that determines online two cells, which are candidates for having the minimum and maximum SOCs. The dimension of the hybrid estimator is independent of the number of cells, which makes it particularly attractive for large battery packs. Moreover, the estimator is endowed with robust, global convergence guarantees despite disturbances and measurement noise. Furthermore, Zeno behavior is ruled out as any solution to the considered system is shown to exhibit an average dwell-time. Numerical simulations illustrate the efficiency in terms of accuracy and computational time of the proposed estimator
Temperature and associated effects on water diffusion in poly(lactic acid)
No full textInternational audienc
Dairy powders: Impact of surface composition and heterogeneity on functional properties
No full textInternational audienceThis study investigates the surface characteristics of four industrial dairy powders - Skim Milk Powder (SMP), Whole Milk Powder (WMP), Instant Filled Milk Powder (IFMP), and Cheese Powder (CP) - across multiple length scales, aiming to understand how these characteristics influence their physical and functional properties. Using X-ray Photoelectron Spectroscopy on large area (multiple particles), it was observed that surface composition remains consistent across different particle sizes within each powder type. Notably, SMP exhibited a more hydrophilic surface compared to the other powders, particularly CP. At the particle scale (single particle – 10 μm × 10 μm), Atomic Force Microscopy (AFM) analysis revealed uniform surface structures. Among the samples, CP and SMP displayed smoother surfaces whereas WMP and IFMP exhibited more textured topographies. High-resolution (2 μm × 2 μm) AFM assessments highlighted distinct differences in surface structures among the powders. Nanomechanical measurements indicated that SMP had the highest Young's modulus suggesting a stiffer surface, while CP had the lowest, indicating a softer surface. Finally, these findings underscore the significance of surface characteristics at various scales in determining the functional performance of dairy powders, here flowability and wettability
Three- to 8-year old children do not favor male power when allocating resources
No full textInternational audienceAbstract From an early age, children perceive power imbalances between genders, but their attitudes toward gendered power remain largely unexplored. We studied this issue using a resource allocation task with 653 French children aged 3–8 (50.15% girls) recruited between 2022 and 2023. Participants were exposed to a dyadic power interaction and had to distribute more resources to either the dominant or the subordinate character. We tested three hypotheses: H1 predicted a male dominance bias; H2 predicted own-gender favoritism; and H3 predicted sensitivity to hierarchical status only. Contrary to H1, no pro-male bias was found. Results supported H3: younger children favored dominant characters, while older children favored subordinates. H2 was partially supported, showing own-gender bias, stronger in girls, without overriding sensitivity to status
La fin du secret sur les salaires, un choc culturel pour les entreprises françaises
No full textthe Conversation FranceLa directive européenne qui oblige de nombreuses entreprises à faire la transparence sur les salaires sera un vrai défi pour les directions de ressources humaines et les managers. Car, si des écarts de salaire peuvent se justifier, il faut savoir les expliquer, ce qui n’est pas toujours facile quand l’argent reste tabou. Quant aux salariés, comment réagiront-ils quand ils découvriront qu’ils sont plus ou moins bien payés que leurs collègues à poste équivalent ? C’est un grand chambardement qui attend les entreprises
Diffusion-based Frameworks for Unsupervised Speech Enhancement
No full textThis paper addresses unsupervised diffusion-based single-channel speech enhancement (SE). Prior work in this direction combines a score-based diffusion model trained on clean speech with a Gaussian noise model whose covariance is structured by non-negative matrix factorization (NMF). This combination is used within an iterative expectation–maximization (EM) scheme, in which a diffusion-based posterior-sampling E-step estimates the clean speech. We first revisit this framework and propose to explicitly model both speech and acoustic noise as latent variables, jointly sampling them in the E-step instead of sampling speech alone as in previous approaches. We then introduce a new unsupervised SE framework that replaces the NMF noise prior with a diffusion-based noise model, learned jointly with the speech prior in a single conditional score model. Within this framework, we derive two variants: one that implicitly accounts for noise and one that explicitly treats noise as a latent variable. Experiments on WSJ0–QUT and VoiceBank–DEMAND show that explicit noise modeling systematically improves SE performance for both NMF-based and diffusion-based noise priors. Under matched conditions, the diffusion-based noise model attains the best overall quality and intelligibility among unsupervised methods, while under mismatched conditions the proposed NMF-based explicit-noise framework is more robust and suffers less degradation than several supervised baselines. Our code will be publicly available at https://github.com/jeaneudesAyilo/enudiffuse
Digital Casulana: Restitution et édition numérique du Primo libro de' madrigali a quattro voci de Maddalena Casulana
No full textInternational audienceCe poster présente les avancées du projet Digital Casulana. Il a été présenté aux Rencontres de musicologie médiévale à l'Université de Lorraine, le 13 janvier 2026