New regime of droplet generation in a T-shape microfluidic junction

International audienceWe present an experimental study of a new regime of monodisperse micro-droplet generation that we named the balloon regime. A dispersion of oil in water in a T-junction microfluidic system was studied. Several microfluidic devices having different cross-sections of the continuous and the dispersed phases micro-channels were tested. This new regime appears only for low- dispersed phase velocity. The micro-droplet size is mainly related to the geometry of the T-junction micro-channels especially its width and depth, and independent of the continuous and dispersed phases velocities. In our experiments, the velocities of the continuous and the dispersed phases vc and vd respectively, have been varied in a wide range: vc from 0.5 to 500 mm/s, and vd from 0.01 to 30 mm/s. We show that the continuous phase only controls the micro-droplet density, while the dispersed phase linearly changes the frequency of the micro-droplet generation. Another particularity of the present regime, which differentiates it from all other known regimes, is that the micro-droplet retains its circular shape throughout its formation at the T junction, and undergoes no deformation due to the drag forces. We propose a mechanism to explain the formation of microdroplets in this new regime

Tracing commodities in indoor environments for service robotics

Daily life assistance for elderly people is one of the most promising scenarios for service robots in the the near future. In particular, the go-and-fetch task will be one of the most demanding tasks in these cases. In this paper, we present an informationally structured room that supports a service robot in the task of daily object fetching. Our environment contains different distributed sensors including a floor sensing system and several intelligent cabinets. Sensor information is send to a centralized management system which process the data and make it available to a service robot which is assisting people in the room. We additionally present the first steps of an intelligent framework used to maintain information about locations of commodities in our informationally structured room. This information will be used by the service robot to find objects under people requests. One of the main goal of our intelligent environment is to maintain a small number of sensors to avoid interfering with the daily activity of people, and to reduce as much as possible invasion of their privacy. In order to compensate this limited available sensor information, our framework aims to exploit knowledge about people's activity and interaction with objects, to infer reliable information about the location of commodities. This paper presents simulated results that demonstrate the suitability of this framework to be applied to a service robotic environment equipped with limited sensors. In addition we discuss some preliminary experiments using our real environment and robot

0052: Role of kinins in diabetic wound healing

The diabetic foot is associated with pain, decrease in patient's quality of life, considerable costs, and amputation. In this study, we determined the role of KKS, via activation of bradykinin receptors (B1R or B2R), in a mouse model of diabetic wound healing. Diabetic or nondiabetic mice are wounded with an 8-mm punch biopsy and then are treated or not with specific B1R or B2R agonists (720nmol/kg.d-1) and/or B2R antagonist (Icatibant, 500μg/kg.dg/d-1). The wound-healing surface was daily followed up. At 11 days, the scar were analysed by histology (Masson's trichrome staining) and B1R and B2R expression were assessed (RT-qPCR). Effects of the agonists on cells (fibroblasts and keratinocytes) migration and proliferation were also analysed. In diabetic condition, mRNA of B1R and B2R was increased in skin (p<0.01). B1R activation had no effect on wound closure in our model. In contrast, B2R activation dramatically delayed wound healing in diabetic (p<0.001) or nondiabetic (p<0.01) mice. Histological analysis of scar showed significant skin disorganization and epidermis thickening with B2R agonist (p<0.05). In vitro, B2R agonist induced an increase of keratinocyte proliferation (+46% after 48h, p<0.01) and a stimulation of keratinocyte migration (+30% after 24h, p<0.05). These effects was associated with ERK phosphorylation which occurs downstream of EGFR activation (p<0.05). B2R agonist had no effect on fibroblast migration but decreased fibroblast proliferation (–33% after 48h, p<0.05). Co-treatment with Icatibant abrogated in vivo and in vitro effects observed with B2R agonist. Moreover, Icatibant alone hastened wound healing and decrease the epidermis thickening induced by diabetes. In conclusion, KKS, through the B2R but not the B1R, plays a critical role in proliferation and remodelling phases of skin wound healing in mice. While more studies are needed, Icatibant could be used to correct the diabetic wound healing defect

Automatic analysis of facilitated taste-liking

This paper focuses on: (i) Automatic recognition of taste-liking from facial videos by comparatively training and evaluating models with engineered features and state-of-the-art deep learning architectures, and (ii) analysing the classification results along the aspects of facilitator type, and the gender, ethnicity, and personality of the participants. To this aim, a new beverage tasting dataset acquired under different conditions (human vs. robot facilitator and priming vs. non-priming facilitation) is utilised. The experimental results show that: (i) The deep spatiotemporal architectures provide better classification results than the engineered feature models; (ii) the classification results for all three classes of liking, neutral and disliking reach F1 scores in the range of 71%-91%; (iii) the personality-aware network that fuses participants’ personality information with that of facial reaction features provides improved classification performance; and (iv) classification results vary across participant gender, but not across facilitator type and participant ethnicity.EPSR

Functional outcomes in symptomatic versus asymptomatic patients undergoing incisional hernia repair: Replacing one problem with another? A prospective cohort study in 1312 patients

Background: Incisional hernias can be associated with pain or discomfort. Surgical repair especially mesh reinforcement, may likewise induce pain. The primary objective was to assess the incidence of pain after hernia repair in patients with and without pre-operative pain or discomfort. The secondary objectives were to determine the preferred mesh type, mesh location and surgical technique in minimizing postoperative pain or discomfort. Materials and methods: A registry-based prospective cohort study was performed, including patients undergoing incisional hernia repair between September 2011 and May 2019. Patients with a minimum follow-up of 3–6 months were included. The incidence of hernia related pain and discomfort was recorded perioperatively. Results: A total of 1312 patients were included. Pre-operatively, 1091 (83%) patients reported pain or discomfort. After hernia repair, 961 (73%) patients did not report pain or discomfort (mean follow-up = 11.1 months). Of the pre-operative asymptomatic patients (n = 221), 44 (20%, moderate or severe pain: n = 14, 32%) reported pain or discomfort after mean follow-up of 10.5 months. Of those patients initially reporting pain or discomfort (n = 1091), 307 (28%, moderate or severe pain: n = 80, 26%) still reported pain or discomfort after a mean follow-up of 11.3 months postoperatively. Conclusion: In symptomatic incisional hernia patients, hernia related complaints may be resolved in the majority of cases undergoing surgical repair. In asymptomatic incisional hernia patients, pain or discomfort may be induced in a considerable number of patients due to surgical repair and one should be aware if this postoperative complication

Deep learning with Python

DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He blogs about deep learning at blog.keras.io

Synthese de nouveaux triazoles-1, 2, 4; etude de leur systemie phloemienne et de leurs proprietes phytosanitaires

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc