1,421 research outputs found

    Effect of ultra high temperature (UHT) treatment on coffee brew stability

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    In this work, the influence of an Ultra High Temperature (UHT) treatment on chemical and sensory composition of Arabica coffee brews for a longer shelf-life has been studied. A temperature of 120 degrees C for 2 s allows to obtain a microbiologically safe coffee brew, good valued from the sensory point of view. The behavior of the UHT vs non UHT treated coffee brew was followed throughout 120 days of storage at 4 degrees C. The UHT treatment keeps the typical acidity of the brews longer, delaying and softening the pH decrease and the development of sourness, which is one of the main causes for the rejection of stored coffee brews. The UHT treatment hardly affects the concentrations of caffeine and trigonelline, and of some phenolic compounds such as 5-caffeoylquinic (5-CQA), caffeic or ferulic acids. Sixteen key odorants and staling volatiles were analyzed by HS-GC-MS and lower changes were observed in the UHT treated coffee brew throughout storage. Higher DPPH center dot scavenging activity was observed in the UHT treated coffee brew from days 60 to 120. In conclusion, the application of an UHT treatment is proposed to extend the shelf-life (up to 60 days) of stored coffee brews

    SLAM algorithm applied to robotics assistance for navigation in unknown environments

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    <p>Abstract</p> <p>Background</p> <p>The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI).</p> <p>Methods</p> <p>In this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents.</p> <p>Results</p> <p>The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface.</p> <p>Conclusions</p> <p>The integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.</p

    Association of lifestyle factors and inflammation with sarcopenic obesity: data from the PREDIMED‐Plus trial

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    Background: Sarcopenia is a progressive age-related skeletal muscle disorder associated with increased likelihood of adverse outcomes. Muscle wasting is often accompanied by an increase in body fat, leading to ‘sarcopenic obesity’. The aim of the present study was to analyse the association of lifestyle variables such as diet, dietary components, physical activity (PA), body composition, and inflammatory markers, with the risk of sarcopenic obesity. Methods: A cross-sectional analysis based on baseline data from the PREDIMED-Plus study was performed. A total of 1535 participants (48% women) with overweight/obesity (body mass index: 32.5 ± 3.3 kg/m2; age: 65.2 ± 4.9 years old) and metabolic syndrome were categorized according to sex-specific tertiles (T) of the sarcopenic index (SI) as assessed by dual-energy X-ray absorptiometry scanning. Anthropometrical measurements, biochemical markers, dietary intake, and PA information were collected. Linear regression analyses were carried out to evaluate the association between variables. Results: Subjects in the first SI tertile were older, less physically active, showed higher frequency of abdominal obesity and diabetes, and consumed higher saturated fat and less vitamin C than subjects from the other two tertiles (all P < 0.05). Multiple adjusted linear regression models evidenced significant positive associations across tertiles of SI with adherence to the Mediterranean dietary score (P-trend < 0.05), PA (P-trend < 0.0001), and the 30 s chair stand test (P-trend < 0.0001), whereas significant negative associations were found with an inadequate vitamin C consumption (P-trend < 0.05), visceral fat and leucocyte count (all P-trend < 0.0001), and some white cell subtypes (neutrophils and monocytes), neutrophil-tolymphocyte ratio, and platelet count (all P-trend < 0.05). When models were additionally adjusted by potential mediators (inflammatory markers, diabetes, and waist circumference), no relevant changes were observed, only dietary variables lost significance

    Measurement of the cross-section and charge asymmetry of WW bosons produced in proton-proton collisions at s=8\sqrt{s}=8 TeV with the ATLAS detector

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    This paper presents measurements of the W+→Ό+ÎœW^+ \rightarrow \mu^+\nu and W−→Ό−ΜW^- \rightarrow \mu^-\nu cross-sections and the associated charge asymmetry as a function of the absolute pseudorapidity of the decay muon. The data were collected in proton--proton collisions at a centre-of-mass energy of 8 TeV with the ATLAS experiment at the LHC and correspond to a total integrated luminosity of 20.2~\mbox{fb^{-1}}. The precision of the cross-section measurements varies between 0.8% to 1.5% as a function of the pseudorapidity, excluding the 1.9% uncertainty on the integrated luminosity. The charge asymmetry is measured with an uncertainty between 0.002 and 0.003. The results are compared with predictions based on next-to-next-to-leading-order calculations with various parton distribution functions and have the sensitivity to discriminate between them.Comment: 38 pages in total, author list starting page 22, 5 figures, 4 tables, submitted to EPJC. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/STDM-2017-13

    Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector