Implication du PAR-2 dans le remodelage musculaire lisse bronchique de la physiopathologie de l'asthme

La cellule musculaire lisse (CML) a un rôle pivot dans la physiopathologie de l asthme. Dans ce travail de thèse nous avons pu mettre en avant l implication du récepteur de type 2 activé par les protéases (PAR-2) dans une composante majeure du remodelage bronchique : la prolifération musculaire lisse. Dans le premier travail, nous avons montré une augmentation de l expression du PAR-2 au niveau des CML bronchiques d asthmatiques in vitro. La réponse calcique est dépendante du niveau d expression du récepteur, mais n influence pas la réponse proliférante. La stimulation répétée du PAR-2 augmente la prolifération des seules CML d asthmatiques, par un mécanisme dépendant de la voie ERK. Dans le second travail, nous avons montré que la production basale d un épithélium reconstitué entraine une prolifération plus importante des CML d asthmatiques comparée aux CML de témoins. Une augmentation supplémentaire de la prolifération des seules CML d asthmatiques a été observée, après activation par le surnageant d épithélium stimulé par des acariens de maison comparé au surnageant épithélial non stimulé. Ce mécanisme est dépendant du PAR-2 épithélial, qui induit la production de leucotriènes C4, sur des CML dont l expression du récepteur (CysLTR1) est augmentée chez l asthmatique. Ces résultats apportent de nouvelles connaissances dans le remodelage musculaire lisse bronchique de l asthmatique et met en avant le PAR-2 comme cible thérapeutique potentielle.Smooth muscle cells (SMC) play an important role in asthma pathophysiology. In this thesis, we have highlighted the involvement of protease activated receptor type-2 (PAR-2) in SMC proliferation, which is a major component of airway remodeling. In the first study, we have shown an increased expression of PAR-2 in asthmatic bronchial SMC in vitro. Calcium response is dependent on the expression level of PAR-2, which does not affect the proliferative response. Repeated stimulation of PAR-2 increases the proliferation of asthmatics SMC only, by an ERK-dependent mechanism. In the second study, we have demonstrated that the basal production of reconstituted epithelium leads to a greater proliferation of asthmatics SMC compared to controls. Increased proliferation of asthmatics SMC only was observed, after stimulation with supernatant of the epithelium stimulated by house dust mites (HDM) compared to unstimulated epithelial supernatant. This mechanism is epithelial PAR-2-dependent, which induces the production of leukotrienes C4, whose receptor expression (CysLTR1) is increased in asthmatics SMC. These results provide new insights into bronchial smooth muscle remodeling in asthma and highlights the PAR-2 as a potential therapeutic target.BORDEAUX2-Bib. électronique (335229905) / SudocBORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

A low-cost, wireless, 3-D-printed custom armband for sEMG hand gesture recognition

Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost ( 150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly (p < 0.05) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository

Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features

The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features. Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. However, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN, which significantly enhances (p=0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, the main contribution of this work is to provide the first topological data analysis of EMG-based gesture recognition for the characterisation of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. Furthermore, using convolutional network visualization techniques reveal that learned features tend to ignore the most activated channel during gesture contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.Comment: The first two authors shared first authorship. The last three authors shared senior authorship. 32 page

Alveolar Macrophages in the Resolution of Inflammation, Tissue Repair, and Tolerance to Infection

Pathogen persistence in the respiratory tract is an important preoccupation, and of particular relevance to infectious diseases such as tuberculosis. The equilibrium between elimination of pathogens and the magnitude of the host response is a sword of Damocles for susceptible patients. The alveolar macrophage is the first sentinel of the respiratory tree and constitutes the dominant immune cell in the steady state. This immune cell is a key player in the balance between defense against pathogens and tolerance toward innocuous stimuli. This review focuses on the role of alveolar macrophages in limiting lung tissue damage from potentially innocuous stimuli and from infections, processes that are relevant to appropriate tolerance of potential causes of lung disease. Notably, the different anti-inflammatory strategies employed by alveolar macrophages and lung tissue damage control are explored. These two properties, in addition to macrophage manipulation by pathogens, are discussed to explain how alveolar macrophages may drive pathogen persistence in the airways

Intuitive adaptive orientation control of assistive robots for people living with upper limb disabilities

Robotic assistive devices enhance the autonomy of individuals living with physical disabilities in their day-to-day life. Although the first priority for such devices is safety, they must also be intuitive and efficient from an engineering point of view in order to be adopted by a broad range of users. This is especially true for assistive robotic arms, as they are used for the complex control tasks of daily living. One challenge in the control of such assistive robots is the management of the end-effector orientation which is not always intuitive for the human operator, especially for neophytes. This paper presents a novel orientation control algorithm designed for robotic arms in the context of human-robot interaction. This work aims at making the control of the robot's orientation easier and more intuitive for the user, in particular, individuals living with upper limb disabilities. The performance and intuitiveness of the proposed orientation control algorithm is assessed through two experiments with 25 able-bodied subjects and shown to significantly improve on both aspects

A transferable adaptive domain adversarial neural network for virtual reality augmented EMG-Based gesture recognition

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the off line accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between off line and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different-recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p <; 0.05) outperforms using fine-tuning as the recalibration technique

A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p<0.05) outperforms using fine-tuning as the recalibration technique.Comment: 10 Pages. The last three authors shared senior authorshi