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8 figures.-- Supplementary information available., Physicochemical properties, kinetic pyrolysis and thermodynamic study of spent green tea, pure spent coffee grounds, spent coffee grounds blended with 50% torrefied barley and coffee husk were experimentally investigated using thermogravimetric analysis under an inert atmosphere to evaluate their thermochemical application. Five isoconversional methods were applied to determine effective activation energy (Ea) of the pyrolysis processes. All methods showed good agreement by determining fluctuating Ea values (150–500 kJ mol−1). Complex Ea profiles with conversion were divided into four stages corresponding to thermal degradation of main biomass constituents (extractives, hemicellulose, cellulose and lignin), indicating that extractives decomposition was the least demanding reaction while lignin decomposition was the most demanding. The kinetic process was verified by reconstruction according to the Friedman parameters. The thermodynamic parameters were evaluated to determine the energy demand and efficiency throughout the process. The values obtained for physicochemical properties such as volatile matter (> 68%) and higher heating value (> 17 MJ kg−1), average Ea (223–319 kJ mol−1) and significant energy efficiency implied that these types of biomass waste have significant reactivity and consequently the highest potential for the production of bioenergy and a range of high-value chemicals and materials., This work was supported and funded by the Ministry of Higher Education and Scientific Research (MESRST) in Tunisia through the Project “Thermochemical and biochemical conversion of solid waste” under a Contract between the MESRST and the Research and Technology Centre of Energy (Program contract 2019-2022). The financial support for this work also was given by the Spanish Ministry of Science and Innovation, the State Research Agency and the European Funds for Regional Development (No. RTI2018-095575-B-I00, MCI/AEI/FEDER, UE), as well as the Regional Government of Aragon (DGA) under the research groups support programme. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

This study presents a new attempt to quantify and predict changes in the ECG signal in the pre-ictal period. In the proposed approach, threshold techniques were applied to the standard deviation (STD) of two Heart rate variability features (The number of heartbeats per two minutes and approximate entropy) computed to ensure prediction and quantification of the pre-ictal state. We analyzed clinical data taken from two epileptic public databases, Siena Scalp EEG and Post-Ictal Heart Rate Oscillations in Partial Epilepsy and a local database. By testing the proposed approach on the Siena scalp EEG database, we achieved a sensitivity of 100%, specificity of 95%, and an accuracy of 96.4% whereas using acquisitions from the post-Ictal database, we achieved a sensitivity of 100%, specificity of 91% and an accuracy of 94% and using the local database we achieved a sensitivity of 100%, a specificity of 97% and an accuracy of 97.5%. Furthermore, the proposed approach predicted 58.7%, 57.2, and 40% of the seizures before the onset by more than 10 min for the data taken from post-ictal, local and Siena database, respectively. Using the automatic threshold technique, we were able to achieve a sensitivity, specificity, and accuracy of 85%, 81%, 82% using our local database respectively, whereas using acquisitions take from the Siena Scalp EEG database, we achieved a sensitivity of 75%, specificity of 85% and an accuracy of 82%. Besides, using the post-ictal database, we achieved a sensitivity of 90%, a specificity of 83% and an accuracy of 85%.

International audience; Aged macular degeneration (AMD) leads to a progressive decline in visual acuity until reaching blindness. It is considered as an irreversible pathology where an early diagnosis remains crucial. However, the lack of ophthalmologists, the permanent increase in elderly people, and their limited mobility involves a delay in AMD diagnosis. In this paper, we propose an automated method for AMD screening. The proposed processing pipeline consists in applying the well-known Radon transform to the macula region in order to model the AMD lesions even with a moderate quality of smartphone-captured fundus images. Thereby, the relevant features are carefully selected, related to the main proprieties of drusens, and then provided to an SVM classifer. The implementation of the method into a smartphone associated to a fundus image capturing device leads to a mobile CAD system that performs higher performance AMD screening. Within this framework and to achieve a real-time implementation, an optimization approach is suggested in order to reduce the processing workload. The evaluation of our method is carried out through the three public STARE, REFUGE, and RFMiD databases. A 4-fold cross-validation approach is used to evaluate the method performance where accuracies of 100%, 95.2%, and 94.3% are respectively obtained with STARE, REFUGE, and RFMiD databases. Comparisons with the state-of-the-art methods in the literature are done. Thereafter, the robustness of the proposed method was evaluated and proved. We note that 100% accuracy was preserved despite the use of degraded quality fundus images as noisy and blurred. Moreover, the propounded method was implemented in S7-Edge and S9 Smartphone devices, where the execution times of 19 and 15 milliseconds were respectively achieved, which proves the AMD real-time detection. Taking advantage of its mobility, cost-efective, detection performance, andreduced execution time, our proposed method seems a good solution for real-time AMD screening on mobile devices.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV 2) may cause damage of the cardiovascular system during the acute phase of infection. However, Recent studies described a mid and long-term subtle cardiac injuries after recovery from acute Coronavirus disease 19 (COVID-19).The aim of this study was to determine the relationship between the severity of chest computed tomography (CT) lesions and the persistence of subtle myocardial injuries at mid-term follow-up of patients recovered from COVID-19 infection.MethodsAll COVID-19 patients were enrolled prospectively in this study. Sensitive troponin T (hsTnT) and chest CT scan was performed in all patients at the acute phase of Covid-19 infection. At the mid-term follow up, conventional transthoracic echocardiograph and global longitudinal strain (GLS) of left and right ventricles (LV and RV) were determined and compared between patients with chest CT scan lesions less than 50% (Group 1) and those with severe chest CT scan greater or equal to 50% (Group 2).ResultsThe mean age was 55 more or less than 14 years. Both LV GLS and RV GLS were significantly decreased in the group 2 (p=0.013 and p=0.011, respectively). LV GLS value more than -18% was noted in 43% of all the patients and RV GLS value more than -20% was observed in 48% of them. The group with severe chest CT scan lesions included more patients with reduced LV GLS and reduced RV GLS than the group with mild chest CT scan lesions (G1:29% vs. G2:57%, p=0.002) and (G1:36% vs. G2:60 %, p=0.009) respectively).ConclusionPatients with severe chest CT scan lesions are more likely to develop subclinical myocardial damage. TTE could be recommended in patients recovering from COVID-19 to detect subtle LV and RV lesions.Trial registrationThe cohort of patients is a part of the research protocol (IORG 00093738 N°102/OMB 0990-0279) approved by the Hospital Ethics Committee.