PERFORMANCE EVALUATION OF CROSS-LAYER DESIGN WITH DISTRIBUTED AND SEQUENTIAL MAPPING SCHEME FOR VIDEO APPLICATION OVER IEEE 802.11E

The rapid development of wireless communication imposes several challenges to support QoS for real-time multimedia applications such as video stream applications. Researchers tackled these challenges from different points of view including the semantics of the video to achieve better QoS requirements. The main goal of this research is to design a UDP protocol to realize a distributed sequential mapping scheme (DSM) with a cross-layer design and evaluate its accuracy under different network conditions. In DSM, the perceived quality of a multi-layer video is addressed by mapping each video layer into channel resources represented as queues or access categories (ACs) existing in IEEE 802.11e MAC layer. This research work further investigates the efficiency of this scheme with actual implementation and thorough simulation experiments. The experiments reported the efficiency of this scheme with the presence of different composite traffic models covering most known traffic scenarios using Expected Reconstructed Video Layers (ERVL) and packet loss rate as accuracy measures. This research work also investigates the accuracy of calculating the ERVL compared to its value using actual readings of layers drop rate. The effect of changing the ACs queue size on the ERVL is studied. The use of this scheme shows zero-drop in the base layer in almost all scenarios where no ongoing traffic is presented except that the testing video sessions between nodes. In these experiments, the ERVL continuously reported high values for the number of expected reconstructed video layers. While these values dramatically vary when introducing ongoing different composite traffic models together with the testing video sessions between nodes. Finally, a 40% increase in the ACs queue size shows significant improvement on ERVL while an increase of the queue size beyond this value has very little significance on ERVL

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom

Altres ajuts: This research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Center (BRC), US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, and IQVIA. The University of Oxford received funding related to this work from the Bill & Melinda Gates Foundation (Investment ID INV016201 and INV-019257). APU has received funding from the Medical Research Council (MRC) [MR/K501256/1, MR/N013468/1] and Fundación Alfonso Martín Escudero (FAME) (APU). VINCI [VA HSR RES 13-457] (SLD, MEM, KEL). JCEL has received funding from the Medical Research Council (MR/K501256/1) and Versus Arthritis (21605). MR is funded by Wereld Kanker Onderzoek Fonds (WKOF), as part of the World Cancer Research Fund International grant program [grant number: 2017/1630]A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8−40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0−33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies

Characteristics and outcomes of over 300,000 patients with COVID-19 and history of cancer in the United States and Spain

Background: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. Methods: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. Results: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%–18% and 1%–14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin’s lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n ¼ 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. Conclusions: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. Impact: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.</p

Implementation and evaluation of cross-layer design with distributed and sequential mapping scheme for video application over IEEE 802.11e

The rapid development of wireless communication imposes several challenges to support QoS for real-time multimedia applications such as video stream applications. Researchers tackled these challenges from different points of view including the semantics of the video to achieve better QoS requirements. The main goal of this research is to extend the UDP protocol to realize a distributed sequential mapping scheme (DSM) with a cross-layer design and evaluate its accuracy under different network conditions. In DSM, the perceived quality of a multi-layer video is addressed by mapping each video layer into channel resources represented as queues or access categories (ACs) existing in IEEE 802.11e MAC layer. The experiments reported the efficiency of this scheme with the presence of different composite traffic models covering most known traffic scenarios using Expected Reconstructed Video Layers (ERVL) and packet loss rate as accuracy measures. In Experiments without ongoing traffic, ERVL continuously reported high values, while ERVL values dramatically vary when introducing ongoing different composite traffic models together with the testing video sessions between nodes. 2016 IEEE.Scopu

Clinical Course and Nutritional Management of Propionic and Methylmalonic Acidemias

Propionic and methylmalonic acidemias result in multiple health problems including increased risk for neurological and intellectual disabilities. Knowledge regarding factors that correlate to poor prognosis and long-term outcomes is still limited. In this study, we aim to provide insight concerning clinical course and long-term complications by identifying possible correlating factors to complications. Results. This is a retrospective review of 20 Egyptian patients diagnosed with PA (n = 10) and MMA (n = 10) in the years 2014–2018. PA patients had lower DQ/IQ and were more liable to hypotonia and developmental delay. The DQ/IQ had a strong negative correlation with length of hospital stay, frequency of PICU admissions, time delay until diagnosis, and the mode ammonia level. However, DQ/IQ did not correlate with age of onset of symptoms or the peak ammonia level at presentation. Both the growth percentiles and albumin levels had a positive correlation with natural protein intake and did not correlate with the total protein intake. Additionally, patients on higher amounts of medical formula did not necessarily show an improvement in the frequency of decompensation episodes. Conclusion. Our findings indicate that implementation of NBS, vigilant and proactive management of decompensation episodes, and pursuing normal ammonia levels during monitoring can help patients achieve a better neurological prognosis. Furthermore, patients can have a better outcome on mainly natural protein; medical formula should only be used in cases where patients do not meet 100–120% of their DRI from natural protein

Patient-Driven Network Selection in multi-RAT Health Systems Using Deep Reinforcement Learning

The recent pandemic along with the rapid increase in the number of patients that require continuous remote monitoring imposes several challenges to support the high quality of services (QoS) in remote health applications. Remote-health (r-health) systems typically demand intense data collection from different locations within a strict time constraint to support sustainable health services. On the contrary, the end-users with mobile devices have limited batteries that need to run for a long time, while continuously acquiring and transmitting health-related information. Thus, this paper proposes an adaptive deep reinforcement learning (DRL) framework for network selection over heteroge-neous r-health systems to enable continuous remote monitoring for patients with chronic diseases. The proposed framework allows for selecting the optimal network(s) that maximizes the accumulative reward of the patients while considering the patients' state. Moreover, it adopts an adaptive compression scheme at the patient level to further optimize the energy consumption, cost, and latency. Our results depict that the proposed framework outperforms the state-of-the-art techniques in terms of battery lifetime and reward maximization.This work was made possible by NPRP grant # NPRP12S-0305-190231 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors

Improving Regulation of Enzymatic and Non-Enzymatic Antioxidants and Stress-Related Gene Stimulation in Cucumber mosaic cucumovirus-Infected Cucumber Plants Treated with Glycine Betaine, Chitosan and Combination

Cucumber mosaic cucumovirus (CMV) is a deadly plant virus that results in crop-yield losses with serious economic consequences. In recent years, environmentally friendly components have been developed to manage crop diseases as alternatives to chemical pesticides, including the use of natural compounds such as glycine betaine (GB) and chitosan (CHT), either alone or in combination. In the present study, the leaves of the cucumber plants were foliar-sprayed with GB and CHT&mdash;either alone or in combination&mdash;to evaluate their ability to induce resistance against CMV. The results showed a significant reduction in disease severity and CMV accumulation in plants treated with GB and CHT, either alone or in combination, compared to untreated plants (challenge control). In every treatment, growth indices, leaf chlorophylls content, phytohormones (i.e., indole acetic acid, gibberellic acid, salicylic acid and jasmonic acid), endogenous osmoprotectants (i.e., proline, soluble sugars and glycine betaine), non-enzymatic antioxidants (i.e., ascorbic acid, glutathione and phenols) and enzymatic antioxidants (i.e., superoxide dismutase, peroxidase, polyphenol oxidase, catalase, lipoxygenase, ascorbate peroxidase, glutathione reductase, chitinase and &beta;-1,3 glucanase) of virus-infected plants were significantly increased. On the other hand, malondialdehyde and abscisic acid contents have been significantly reduced. Based on a gene expression study, all treated plants exhibited increased expression levels of some regulatory defense genes such as PR1 and PAL1. In conclusion, the combination of GB and CHT is the most effective treatment in alleviated virus infection. To our knowledge, this is the first report to demonstrate the induction of systemic resistance against CMV by using GB

Software project management: Theory of constraints, risk management, and performance evaluation

Constraints and risks are two critical factors that affect software project performance; more attention needs to be paid to constraints and risks in order to improve performance. In this paper, investigation will take place to determine the relation between those three factors. An enhanced model has been proposed to describe how these factors affect each other. As an application, the performance is examined for both open and closed source software projects in terms of some constraints and risk factors. Moreover, solutions for controlling both constraints and risks are provided. For constraints, project activities scheduling is enhanced using a genetic algorithm. For risks, RISKIT is briefly explained as a risk management methodology.Scopu