生命与环境科学工业学院

While Charolais livestock farming is often seen as a jewel in the crown of French agriculture, it is nevertheless going through a serious crisis, linked to seventy years of agrarian trajectories focussed on maximising the number of calvings per worker. This publication sets out to show how the increase in volumes produced per unit of labour was to the detriment of creating value added. This is demonstrated by reconstructing long-term statistical series (1968–2023) from the Farm Accountancy Data Network (FADN) and through semi-structured interviews conducted in the Charolais region. Seventy years of contradictory changes in physical and economic labour productivity are therefore examined. These materials have been linked together using an analytical framework conceived for the needs of this study, including a theoretical component which combines concepts from comparative agriculture with those of regulation theory. This analytical framework makes it possible to understand, period by period, how and why the net value added created by Charolais livestock farmers fell so sharply (− 80

Recovering the random graph model from an observed collection of networks is known to present significant challenges in the setting, where the networks do not share a common node set and have different sizes. More specifically, the goal is the estimation of the graphon function that parametrizes the nonparametric exchangeable random graph model. Existing methods typically suffer from either limited accuracy or high computational complexity. We introduce a new histogram-based estimator with low algorithmic complexity that achieves high accuracy by jointly aligning the nodes of all graphs, in contrast to most conventional methods that order nodes graph by graph. Consistency results of the proposed graphon estimator are established. A numerical study shows that the proposed estimator outperforms existing methods in terms of accuracy, especially when the dataset comprises only small and variable-size networks. Moreover, the computing time of the new method is considerably shorter than that of other consistent methodologies. Additionally, when applied to a graph neural network classification task, the proposed estimator enables more effective data augmentation, yielding improved performance across diverse real-world datasets.

With the rising interest in eco-safe cosmetic products due to ongoing governmental restrictions and growing public concern regarding the effects of microplastics derived from synthetic products, research for biodegradable alternatives from renewable materials has been accelerated. Carbohydrate-based polymers can be considered suitable materials due to their excellent biodegradability, renewability, abundance, and versatility. In this study, microparticles based on starch, chitosan, and cellulose derivatives were produced using different methods to obtain micrometric particles with specific structural and rheological properties. In parallel, a formulation strategy was developed to establish a sustainable Pickering emulsion platform capable of incorporating and exploiting the functional properties of the produced biodegradable microparticles. Micrometric particles from native and chemically modified tapioca starch were found compatible with the optimized Pickering emulsion formulation. At the same time, the pseudoplastic flow behavior was maintained, and specific effects regarding droplet size distribution were achieved. The results provided evidence that the synthesized microparticles could serve effectively and safely as co-stabilizers and structuring-modifying agents for sustainable cosmetic emulsion formulation, confirming the appropriateness of the proposed biodegradable starch microparticles. In contrast, chitosan-based microparticles did not yield satisfactory texture or stabilization under the tested conditions, pointing toward additional optimization and investigation. This research confirms that chemically modified starch microparticles can be introduced effectively and safely as eco-safe, sustainable ingredients for next-generation, microplastic-free cosmetic formulations.

3D food printing with flour-based doughs remains challenging due to the complex interactions between starch, proteins, and dietary fibers in unfractionated raw materials. This study investigated the structural, rheological, and printing properties of six flour-based doughs (formulated with wheat, rye, rice, red kidney bean, lupin, and chia flours) subjected to a two-step pre-printing process at constant hydration. The doughs exhibited a wide viscosity range, from a few to several hundred Pa & sdot;s at 10 s(-1), reflecting differences in structural organization. Back-extrusion measurements showed strong agreement with shear rheometry for estimating apparent viscosity (r > 0.978), and proved useful for comparing the apparent viscosities of different products under similar flow conditions, particularly for materials that could not be reliably analyzed by conventional rheometry. Confocal microscopy revealed distinct structuring elements depending on flour type. This multi-scale approach allowed for the formulation of hypotheses regarding the rigidity of dispersed particles and their contribution to dough structure. Printability was governed by both viscosity and particle size. All extrudable doughs achieved a printing quality higher than 90%. The collapse of the red kidney bean-based printed structure was associated with lower stress values at the G ' = G '' crossover, indicating insufficient structural strength. These results provide insight into the multi-scale factors controlling structure formation, flow behavior, and printing performance of flour-based doughs for extrusion-based 3D food printing.

Corticotropin-releasing hormone (CRH) and its receptors CRHR1 and CRHR2 are major actors in the stress response and are well established as components of the hypothalamic-pituitary-adrenal (HPA) axis. Evidence also suggests they are expressed in peripheral tissues and, more interestingly, in the skin. While CRHR1 expression in keratinocytes is documented in terms of presence or absence, data on CRHR2 remain sparse. Moreover, there is no detailed description of the exact localization of CRHR1/2 receptors within the different layers of the epidermis, leaving this question fully unexplored. To better understand the link between stress and skin disorders, we aimed to investigate the differential expression of CRHR1 and CRHR2 in keratinocytes, depending on their level of differentiation. In vitro results demonstrated that CRHR1 appears to be more abundant at early stages of differentiation and CRHR2 at more advanced stages.