Effects of extractable protein hydrolysates, lipids, and polyphenolic compounds from pearl millet (Pennisetum glaucum (L.) R. BR.) whole grain flours on starch digestibility

Pearl millet and other minor cereal production is marginalized in the Sahara of Algeria (Tidikelt and Hoggar regions). Their productions in these areas depend on traditional harvesting and processing. Pearl millet seeds are used as animal feed and rarely for human consumption. This work was to assess the starch digestion of pearl millet cultivated in the arid areas of Algeria. The seeds from this cereal could provide broad potential benefits to human health. However, their digestion properties have not been reported. Therefore, in this study, the in-vitro starch digestibility of pearl millet flour and the effect of processing on the expected glycemic index (eGI) were investigated. Grains from six pearl millet samples were chosen from two regions: Tidikelt and Hoggar. Five flours were prepared by dry milling (MF) and different treatments after dry milling such as extraction of phenolic compounds (MF-PP), lipid extraction (MF-L), protein hydrolysate extraction (MF-P) or lipid plus protein hydrolysate extraction (MF-L-P). The flours were then subjected to digestion, and the effects of grain treatments on the in vitro starch digestion were investigated. For all pearl millet samples, the kinetics of in vitro starch digestion displayed first-order model as substrates were digested to different extents; k (kinetic constant), C∞ (percentage of starch hydrolyzed at infinite time), HI (hydrolysis index) and eGI (expected glycemic index) of the samples were also calculated. Significant increases in C∞, HI and eGI (P<0.05) of the samples were observed after extraction of proteins or proteins plus lipids from flour. Four flours obtained after lipid extraction and five flours from extraction of phenolic compounds had low glycemic index (<55), with values ranging between 31.36 and 44.97. In contrast, flours obtained from protein hydrolysate extraction or lipids plus protein hydrolysates had the highest glycemic index (>69), with values ranging between 77.50 and 121.44. This study confirmed that some of the processed pearl millet seed flours have acceptable nutritional values suitable for human health and nutrition due to the low glycemic index values

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutionspublishedVersio

Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges

acceptedVersio

Endpoints for randomized controlled clinical trials for COVID-19 treatments

Background: Endpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between “cure” and “death” represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of “recovered” versus “not recovered.” Methods: We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. Results: Power for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time. Discussion: Time-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses. © The Author(s) 2020

SUBARU prime focus spectrograph: integration, testing and performance for the first spectrograph

The Prime Focus Spectrograph (PFS) of the Subaru Measurement of Images and Redshifts (SuMIRe) project for Subaru telescope consists in four identical spectrographs fed by 600 fibers each. Each spectrograph is composed by an optical entrance unit that creates a collimated beam and distributes the light to three channels, two visibles and one near infrared. This paper presents the on-going effort for the tests & integration process for the first spectrograph channel: we have developed a detailed Assembly Integration and Test (AIT) plan, as well as the methods, detailed processes and I&T tools. We describe the tools we designed to assemble the parts and to test the performance of the spectrograph. We also report on the thermal acceptance tests we performed on the first visible camera unit. We also report on and discuss the technical difficulties that did appear during this integration phase. Finally, we detail the important logistic process that is require to transport the components from other country to Marseille

Prime Focus Spectrograph (PFS) for the Subaru telescope: Ongoing integration and future plans

PFS (Prime Focus Spectrograph), a next generation facility instrument on the 8.2-meter Subaru Telescope, is a very wide-field, massively multiplexed, optical and near-infrared spectrograph. Exploiting the Subaru prime focus, 2394 reconfigurable fibers will be distributed over the 1.3 deg field of view. The spectrograph has been designed with 3 arms of blue, red, and near-infrared cameras to simultaneously observe spectra from 380nm to 1260nm in one exposure at a resolution of ∼ 1.6-2.7Å. An international collaboration is developing this instrument under the initiative of Kavli IPMU. The project recently started undertaking the commissioning process of a subsystem at the Subaru Telescope side, with the integration and test processes of the other subsystems ongoing in parallel. We are aiming to start engineering night-sky operations in 2019, and observations for scientific use in 2021. This article gives an overview of the instrument, current project status and future paths forward