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Sustainable AAL technology for supporting seniors in independent living shared homes
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Phospholipid Signaling in Crop Plants: A Field to Explore
No full textInternational audienceIn plant models such as Arabidopsis thaliana, phosphatidic acid (PA), a key molecule of lipid signaling, was shown not only to be involved in stress responses, but also in plant development and nutrition. In this article, we highlight lipid signaling existing in crop species. Based on open access databases, we update the list of sequences encoding phospholipases D, phosphoinositide-dependent phospholipases C, and diacylglycerol-kinases, enzymes that lead to the production of PA. We show that structural features of these enzymes from model plants are conserved in equivalent proteins from selected crop species. We then present an in-depth discussion of the structural characteristics of these proteins before focusing on PA binding proteins. For the purpose of this article, we consider RESPIRATORY BURST OXIDASE HOMOLOGUEs (RBOHs), the most documented PA target proteins. Finally, we discuss pioneering experiments that show, by different approaches such as monitoring of gene expression, use of pharmacological agents, ectopic over-expression of genes, and the creation of silenced mutants, that lipid signaling plays major roles in crop species. Finally, we present major open questions that require attention since we have only a perception of the peak of the iceberg when it comes to the exciting field of phospholipid signaling in plants
Mechanosynthesis and Polymerization of Biosourced Styrene Derivatives Based on Building Blocks of Lignin
No full textInternational audienceFinding and developing new biosourced materials that can compete with the current petro-sourced ones are a top priority in order to transition to a more sustainable society. Although the preparation of biosourced polyesters or polyamides is quite well described, finding biosourced and sustainable alternatives to petro-sourced polystyrene is still challenging. Herein, we report the synthesis of polystyrenes prepared in three steps from vanillin, 4-hydrobenzaldehyde, and syringaldehyde, compounds that can be obtained through lignin depolymerization under oxidative conditions. The synthesis involves the conversion of these biosourced platforms into polymerizable styrene derivatives through a methylation of the hydroxyl group followed by an olefination of the aldehyde function. The monomers were first synthesized under conventional conditions using solvents. Then, the synthesis was improved from the sustainability point of view by using a ball mill under solventless conditions, generating much less waste in the process. The three monomers were then converted into biosourced homopolymers through free radical polymerization in bulk, providing functional polystyrene derivatives with thermal properties comparable to those of common petro-sourced polystyrene
Review and analysis of recent advances in intelligent network softwarization for the Internet of Things
No full textInternational audienceThe Internet of Things (IoT) is an emerging technology that aims to connect heterogeneous and constrained objects to each other and to the Internet. It has grown significantly in a wide variety of applications such as smart homes, smart cities, smart vehicles, etc. The huge number of connected devices increases the challenges, as IoT provides diverse and complex network services with different requirements on a common infrastructure. Network Softwarization is the latest network paradigm that transforms traditional network processes to the separation of hardware and software by using some enabling network technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). Machine Learning (ML) plays an essential role in creating smarter IoT networks, as it has shown remarkable results in various domains. Given that the network softwarization allows it to be easily integrated, ML can play a crucial role in efficient and self-adaptive IoT networks. In this paper, we provide a detailed overview of the concepts of IoT, network softwarization, and ML, and we study and discuss the state of the art of intelligent ML-enabled network softwarization for IoT. We also identify the most prominent future research directions to be considered
Multiscale Mechanical Modeling of Skeletal Muscle: A Systemic Review of the Literature
No full textInternational audienceAbstract Purpose From the myofibrils to the whole muscle scale, muscle micro-constituents exhibit passive and active mechanical properties, potentially coupled to electrical, chemical, and thermal properties. Experimental characterization of some of these properties is currently not available for all muscle constituents. Multiscale multiphysics models have recently gained interest as a numerical alternative to investigate the healthy and diseased physiological behavior of the skeletal muscle. Methods This paper refers to the multiscale mechanical models proposed in the literature to investigate the mechanical properties and behavior of skeletal muscles. More specifically, we focus on the scale transition methods, constitutive laws and experimental data implemented in these models. Results Using scale transition methods such as homogenization, coupled to appropriate constitutive behavior of the constituents, these models explore the mechanisms of ageing, myopathies, sportive injuries, and muscle contraction. Conclusion Emerging trends include the development of multiphysics simulations and the coupling of modeling with the acquisition of experimental data at different scales, with increasing focus to little known constituents such as the extracellular matrix and the protein titin
A microscopic model of the dose distribution in hepatocellular carcinoma after selective internal radiation therapy
No full textInternational audienceUnlabelled - The dosimetry evaluation for the selective internal radiation therapy is currently performed assuming a uniform activity distribution, which is in contrast with literature findings. A 2D microscopic model of the perfused liver was developed to evaluate the effect of two different Y microspheres distributions: i) homogeneous partitioning with the microspheres equally distributed in the perfused liver, and ii) tumor-clustered partitioning where the microspheres distribution is inferred from the patient specific images. Methods - Two subjects diagnosed with liver cancer were included in this study. For each subject, abdominal CT scans acquired prior to the SIRT and post-treatment Y positron emission tomography were considered. Two microspheres partitionings were simulated namely homogeneous and tumor-clustered partitioning. The homogeneous and tumor-clustered partitionings were derived starting from CT images. The microspheres radiation is simulated by means of Russell's law. Results - In homogenous simulations, the dose delivery is uniform in the whole liver while in the tumor-clustered simulations a heterogeneous distribution of the delivered dose is visible with higher values in the tumor regions. In addition, in the tumor-clustered simulation, the delivered dose is higher in the viable tumor than in the necrotic tumor, for all patients. In the tumor-clustered case, the dose delivered in the non-tumoral tissue (NTT) was considerably lower than in the perfused liver. Conclusions - The model proposed here represents a proof-of-concept for personalized dosimetry assessment based on preoperative CT images
Weak Convergence of the Conditional Set-Indexed Empirical Process for Missing at Random Functional Ergodic Data
No full textInternational audienceThis work examines the asymptotic characteristics of a conditional set-indexed empirical process composed of functional ergodic random variables with missing at random (MAR). This paper’s findings enlarge the previous advancements in functional data analysis through the use of empirical process methodologies. These results are shown under specific structural hypotheses regarding entropy and under appealing situations regarding the model. The regression operator’s asymptotic (1−α)-confidence interval is provided for 0<α<1 as an application. Additionally, we offer a classification example to demonstrate the practical importance of the methodology
Journey into Gait Biometrics: Integrating Deep Learning for Enhanced Pattern Recognition
No full textInternational audienceExploring Gait Biometrics within the domain of deep learning offers a potent fusion that significantly enhances pattern recognition capabilities. Over the past decade, the evolution of deep learning (DL) pipelines has showcased their effectiveness in overcoming complex challenges within image and signal processing applications. Constructing these pipelines requires a deep understanding of the diverse intermediate layers and their implications. The iterative refinement process involves careful selection and rigorous performance validation of each configuration, demanding significant time and contemplation. Consequently, the task of selecting a robust DL pipeline that excels across various datasets remains challenging. The central objective of this review is to provide guidance to researchers, fostering a comprehensive grasp of distinct gait sensing technologies, while establishing a solid foundation in deep learning concepts. Although gait recognition is a relatively recent development and is yet to find widespread application in real-world scenarios, this article offers a thorough examination of gait biometrics tailored specifically for real-time surveillance applications. Delving into the complexities, it elucidates the crucial parameters governing deep learning pipelines and their nuanced selection to address specific challenges. Through an analysis of recent research articles on deep learning models and their performance across diverse datasets, the review outlines the merits and demerits of various approaches. The ultimate aim is to facilitate the development of an optimized pipeline that seamlessly integrates existing methodologies, enabling the attainment of swift yet precise results for a given problem
Postharvest Caffeoylquinic Acid Accumulation in Forced Chicory Roots: Insights into the Role of Temperature, Water Loss, and Biological Defense Mechanisms
No full textInternational audienceForced chicory roots (FCR), a by-product of Belgian endive cultivation, are a valuable source of bioactive phenolic compounds such as caffeoylquinic acids (CQAs). This study investigated how post-harvest treatments, including wounding and drying, could be used to increase CQAs concent in FCR. Results showed that wounding and water loss treatments significantly increased the content of CQAs in FCR, with chlorogenic acid (5-CQA) content being 3.3-fold higher after wounding and 244 h of drying at room temperature, and dicaffeoylquinic acids (diCQAs) content being 2.3-fold higher after wounding and 71 h of drying at room temperature. The mechanism behind the accumulation of CQAs in FCR was also investigated, with two potential competitive mechanisms proposed: biosynthesis of CQAs by the phenylpropanoid pathway (PP) and consumption/degradation by oxidation/oligomerization reactions (OPO). These mechanisms are temperature-and water-content dependent
Strong consistency rate in functional single index expectile model for spatial data
No full textInternational audienceAnalyzing the real impact of spatial dependency in financial time series data is crucial to financial risk management. It has been a challenging issue in the last decade. This is because most financial transactions are performed via the internet and the spatial dependency between different international stock markets is not standard. The present paper investigates functional expectile regression as a spatial financial risk model. Specifically, we construct a nonparametric estimator of this functional model for the functional single index regression (FSIR) structure. The asymptotic properties of this estimator are elaborated over general spatial settings. More precisely, we establish Borel-Cantelli consistency (BCC) of the constructed estimator. The latter is obtained with the precision of the convergence rate. A simulation investigation is performed to show the easy applicability of the constructed estimator in practice. Finally, real data analysis about the financial data (Euro Stoxx-50 index data) is used to illustrate the effectiveness of our methodology