Miracle-dz/UniFAR-FTUs huggingface dataset:
Interplay between Oxidative Stress and Metabolic Derangements in Non-Alcoholic Fatty Liver Disease: The Role of Selenoprotein P

Abstract Background: Pathogenetic mechanisms involved in the progression of non-alcoholic fatty liver disease (NAFLD) are complex and multifactorial. We investigated oxidative stress through the measurement of selenoprotein P (SeP) in serum and we explored its relation to metabolic derangements and liver damage in a group of non-diabetic NAFLD subjects. Methods: 57 NAFLD patients underwent a double-tracer oral glucose tolerance test (OGTT). Insulin resistance (IR) components were calculated at baseline as follows: hepatic-IR = (endogenous glucose production*insulin); peripheral-IR = (glucose rate of disappearance(Rd)); adipose-tissue(AT)-IR as Lipo-IR = (glycerol rate of appearance (Ra)*insulin) or AT-IR = (free fatty acids (FFAs)*insulin). The lipid and amino acid (AA) profiles were assessed by gas chromatography–mass spectrometry. SeP levels were measured by enzyme immunosorbent assay. Results: Circulating SeP correlated with insulin (rS = 0.28), FFAs (rS = 0.42), glucose Rd (rS = −0.33) and glycerol Ra (rS = −0.34); consistently, SeP levels correlated with Lipo-IR and AT-IR (rS > 0.4). Among the AA and lipid profiles, SeP inversely correlated with serine (rS = −0.31), glycine (rS = −0.44) and branched chain AA (rS = −0.32), and directly correlated with saturated (rS = 0.41) and monounsaturated FFAs (rS = 0.40). Hepatic steatosis and fibrosis increased in subjects with higher levels of SeP. In multivariable regression analysis, SeP was associated with the degree of hepatic fibrosis (t = 2.4, p = 0.022). Conclusions: SeP levels were associated with an altered metabolic profile and to the degree of hepatic fibrosis, suggesting a role in the pathogenesis of NAFLD.
Bayesian Assessments of Aeroengine Performance

Aeroengine performance is determined by temperature and pressure profiles along various axial stations within an engine. Given limited sensor measurements along an axial station, we require a statistically principled approach to inferring these profiles. In this paper, we detail a Bayesian methodology for interpolating the spatial temperature or pressure profile at a single axial station within an aeroengine. The profile is represented as a spatial Gaussian random field on an annulus, with circumferential variations modelled using a Fourier basis and a square exponential kernel respectively. In the scenario where precise frequencies comprising the temperature field are unknown, we utilise a sparsity-promoting prior on the frequencies to encourage sparse representations. The main quantity of interest, the spatial area average is readily obtained in closed form, and we demonstrate how to naturally decompose the posterior uncertainty into terms characterising insufficient sampling and sensor measurement error respectively. Finally, we demonstrate how this framework can be employed to enable more tailored design of experiments.
Understanding Resilience Factors Among Caregivers of People with Alzheimer’s Disease in Spain

Introduction Alzheimer’s disease (AD) caregivers resilience involves the interaction between different risk and protective factors. Context of care, objective stressors, perceived stressors caregiver assessment, mediators factors and consequences of care were associated with resilience. We have developed a more integrated and operational conceptual model of resilience and care than previous models in our sociocultural environment. Purpose To assess the resilience of caregivers of people with AD and the related factors grouped according to an established operational conceptual model of Alzheimer´s caregivers stress. Patients and Methods A total of 120 primary informal caregivers of AD persons in Badajoz (Spain) were included in a cross-sectional design. The following variables have been measured on AD persons and caregivers: socio-demographic data, dependency level, cognitive decline, neuropsychiatric and behavioral symptoms, anxiety, depression, severity of somatic symptoms, level of burden, self-esteem, coping, social support, health-related quality of life (HRQOL) and resilience. Results Most of the caregivers reported symptoms of anxiety (63.3%) and depression (62.5%). We found out higher levels of resilience in caregivers with lower dependence caring (p=0.004). Higher resilience levels of caregivers were related to minor depressive (p=0.006) and anxiety symptoms (p=0.000), and higher HRQOL (p=0.000). Coping dimension mostly used was problem-based strategies such as active coping, positive reinterpretation and acceptance (p= 0.000). Conclusion Those caregivers reporting higher levels of resilience exhibited moderate to intense indicators of burden, fewer symptoms of depression and anxiety and fewer somatic symptoms. They also used adequate problem-focused coping strategies, showed higher levels of HRQOL and demonstrated an appropriate perception of social support. Despite the fact that the characteristics relating to the care context and to social support exert an undeniable influence on caregiver resilience, it would appear that the caregiver’s own intra-psychic resources reveal stronger correlations. Relevance for Clinical Practice The early and accurate identification of caregivers with lower levels of resilience could enable the implementation of vital psychological and educative support interventions to help caregivers to improve their well-being.
Efficient Attention Network: Accelerate Attention by Searching Where to Plug

Recently, many plug-and-play self-attention modules are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs). Previous works lay an emphasis on the design of attention module for specific functionality, e.g., light-weighted or task-oriented attention. However, they ignore the importance of where to plug in the attention module since they connect the modules individually with each block of the entire CNN backbone for granted, leading to incremental computational cost and number of parameters with the growth of network depth. Thus, we propose a framework called Efficient Attention Network (EAN) to improve the efficiency for the existing attention modules. In EAN, we leverage the sharing mechanism (Huang et al. 2020) to share the attention module within the backbone and search where to connect the shared attention module via reinforcement learning. Finally, we obtain the attention network with sparse connections between the backbone and modules, while (1) maintaining accuracy (2) reducing extra parameter increment and (3) accelerating inference. Extensive experiments on widely-used benchmarks and popular attention networks show the effectiveness of EAN. Furthermore, we empirically illustrate that our EAN has the capacity of transferring to other tasks and capturing the informative features. The code is available at https://github.com/gbup-group/EAN-efficient-attention-network
RegFlow: Probabilistic Flow-based Regression for Future Prediction

Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans. Existing approaches provide results under strong assumptions concerning unimodality of future states, or, at best, assuming specific probability distributions that often poorly fit to real-life conditions. In this work we introduce a robust and flexible probabilistic framework that allows to model future predictions with virtually no constrains regarding the modality or underlying probability distribution. To achieve this goal, we leverage a hypernetwork architecture and train a continuous normalizing flow model. The resulting method dubbed RegFlow achieves state-of-the-art results on several benchmark datasets, outperforming competing approaches by a significant margin.
Dataflow-Architecture Co-Design for 2.5D DNN Accelerators using Wireless Network-on-Package

Deep neural network (DNN) models continue to grow in size and complexity, demanding higher computational power to enable real-time inference. To efficiently deliver such computational demands, hardware accelerators are being developed and deployed across scales. This naturally requires an efficient scale-out mechanism for increasing compute density as required by the application. 2.5D integration over interposer has emerged as a promising solution, but as we show in this work, the limited interposer bandwidth and multiple hops in the Network-on-Package (NoP) can diminish the benefits of the approach. To cope with this challenge, we propose WIENNA, a wireless NoP-based 2.5D DNN accelerator. In WIENNA, the wireless NoP connects an array of DNN accelerator chiplets to the global buffer chiplet, providing high-bandwidth multicasting capabilities. Here, we also identify the dataflow style that most efficiency exploits the wireless NoP’s high-bandwidth multicasting capability on each layer. With modest area and power overheads, WIENNA achieves 2.2X-5.1X higher throughput and 38.2% lower energy than an interposer-based NoP design.
DEVELOPMENT AND VALIDATION OF RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF TAPENTADOL HCL, ACECLOFENAC AND PARACETAMOL IN TERNARY MIXTURE

Objective: To develop a accurate, precise and specific RP-HPLC method for simultaneous estimation of Tapentadol HCl (TAP), Aceclofenac (ACE) and Paracetamol (PCM) in its laboratory synthetic mixture. Methods: The optimized method uses C8 Thermo quest, hypersil division, 250*4.60 mm; 5 μ column, mobile phase consisting of phosphate buffer (pH 3) and acetonitrile in the ratio of 40:60, flow rate 0.8 ml/min. The detection wavelength was set at 217 nm. Results: The developed method resulted Paracetamol, Tapentadol HCl and Aceclofenac in eluting at 4.008 min 4.595 min and 6.073 min respectively. Linearity was observed over the concentration range of 52-130 µg/ml for PCM, 16-40 µg/ml forACE and 12-30 µg/ml forTAP. The percentage recovery was found to be in the range of 98-102% at three different levels of a standard addition. The precision (intra-day, inter-day) of the method was within the limit (RSD<2%). Conclusion: The proposed method was found to be accurate, precise, reproducible and specific and it can be used for qualitative and quantitative determination of Tapentadol HCl, Aceclofenac and Paracetamol in their combined dosage form in the pharmaceutical industry.
Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management

Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, machine learning (ML) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without transmitting raw sensitive data to UAV servers. The device selection strategy is also introduced into the AFL framework to keep the low-quality devices from affecting the learning efficiency and accuracy. Moreover, we propose an asynchronous advantage actor-critic (A3C) based joint device selection, UAVs placement, and resource management algorithm to enhance the federated convergence speed and accuracy. Simulation results demonstrate that our proposed framework and algorithm achieve higher learning accuracy and faster federated execution time compared to other existing solutions.
Energy and exergy analyses of an integrated solar‐based desalination quadruple effect absorption system for freshwater and cooling production

In this paper, a novel integrated solar photovoltaic thermal absorption desalination system for freshwater and cooling production is proposed and analyzed thermodynamically. Ammonia–water pair is considered as a working fluid for the absorption system. Effect of average solar radiation for different months, time period of solar radiation availability in Abu Dhabi, salinity of seawater, and temperature of the seawater on energetic and exergetic COPs, production rate of freshwater, and overall performance of the system are investigated under different operating conditions. It is found that energetic and exergetic COPs, production rate of freshwater, energetic and exergetic utilization factors, and performance ratios vary greatly from one month to another because of the dynamic variation in solar radiation and its time of availability. The highest amount of freshwater is produced in the month of July as calculated to be 152 kg/h for a collector area of 100 m2 and solar power of 4.8 kW. The highest energetic and exergetic COPs and utilization factors are also obtained for the month of July. Moreover, the highest performance ratio is found to be 0.056 as obtained in the month of July when solar radiation intensity is highest as available for more than half of a day. Copyright © 2012 John Wiley & Sons, Ltd.
Risk and Resilience Factors Impacting Treatment Compliance and Functional Impairment among Adolescents Participating in an Outpatient Interdisciplinary Pediatric Chronic Pain Management Program

Recurrent pain is a common experience in childhood and adolescence and can result in significant disability in youth, including poor quality of life, school absences, and reduced social activities. Evidence has linked adolescent risk and resilience factors with treatment outcomes. However, less research has focused on examining risk and resilience factors that may influence or predict adolescents’ compliance to treatment within an interdisciplinary pediatric chronic pain management program. Participants included 64 adolescents (M = 15.00 ± 1.69 years); 85.9% female, 84.4% Caucasian who presented to an initial evaluation in an interdisciplinary pediatric pain management program with their caregiver. Youth completed a series of questionnaires at the initial evaluation targeting pain acceptance, self-efficacy, pain catastrophizing, parental responses, pain intensity, and functional disability. Treatment compliance was measured at 3 and 6 months post-intake. Findings indicated that higher levels of adolescent-reported self-efficacy predict decreased treatment session attendance, whereas lower levels of acceptance and parental encouragement/monitoring of symptoms predict increased treatment compliance overall. Several adolescent-reported risk factors were associated with increased functional impairment among this sample. Results highlight the unique importance of risk and resilience factors within the developmental context of adolescence, while also emphasizing the need for further investigation of other relevant influences towards treatment compliance and functional impairment.
Weaning strategies for the withdrawal of non-invasive respiratory support applying continuous positive airway pressure in preterm infants: a systematic review and meta-analysis

Background The optimal method to wean preterm infants from non-invasive respiratory support (NIVRS) with nasal continuous positive airway pressure (CPAP) or high-flow nasal cannula is still unclear, and methods used vary considerably between neonatal units. Objective Perform a systematic review and meta-analysis to determine the most effective strategy for weaning preterm infants born before 37 weeks’ gestation from NIVRS. Method EMBASE, MEDLINE, CINAHL, Google and Cochrane Central Register of Controlled Trials were searched for randomised controlled trials comparing different weaning strategies of NIVRS in infants born before 37 weeks’ gestation. Results Fifteen trials (1.547 infants) were included. With gradual pressure wean, the relative risk of successful weaning at the first attempt was 1.30 (95% CI 0.93 to 1.83), as compared with sudden discontinuation. Infants were weaned at a later postmenstrual age (PMA) (median difference (MD) 0.93 weeks (95% CI 0.19 to 1.67)). A stepdown strategy to nasal cannula resulted in an almost 3-week reduction in the PMA at successful weaning (MD −2.70 (95% CI −3.87 to −1.52)) but was associated with a significantly longer duration of oxygen supplementation (MD 7.80 days (95% CI 5.31 to 10.28)). A strategy using interval training had no clinical benefits. None of the strategies had any effect on the risk of chronic lung disease or the duration of hospital stay. Conclusion A strategy of gradual weaning of airway pressure might increase the chances of successful weaning. Stepdown strategy from CPAP to nasal cannula is a useful alternative resulting in an earlier weaning, but the focus should remain on continued weaning in order to avoid prolonged oxygen supplementation. Interval training should probably not be used.
Informed Consent for Surgery at Resumption of Elective Activity After the First Wave of COVID-19

Background The coronavirus disease 2019 (COVID-19) pandemic has changed the dynamics of healthcare, and the elective surgical consent process has also evolved. The Royal College of Surgeons of England published guidance on consent during COVID-19. Through this study, we aimed to assess our local consent adherence to these guidelines on the resumption of elective activity after the first wave of COVID-19. Methods This prospective review of consecutive elective surgical consent forms was conducted from 20 July 2020 to 16 August 2020 at the Princess Alexandra Hospital NHS Trust, England. The primary outcome was evidence of COVID-19 risk documentation on the consent forms. Results A total of 116 patients’ consent forms were reviewed. Most patients were American Society of Anaesthesiologists (ASA) grade 2 (n=70; 60.34%). Only 25 consent forms (21.55%) had COVID -19 and its associated risks documented, with registrars being the most compliant (19/46; 41.3%) followed by consultants (6/51; 11.7%). With regards to the surgical sub-specialities, general surgery, orthopaedics and ENT had the highest compliance with the guidance. Conclusions As the elective activity resumes, peri-operative risks of COVID-19 should be weighted in during the informed consent process, as mentioned in the latest international guidelines on consent to avoid litigation and negligence claims.
Manuscript Title: Translation, adaptation, and validation of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) for use in Japan: A multicenter cross-sectional study Authors:

Corresponding author: Takuya Aoki Division of Clinical Epidemiology, The Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo, 105-8461, Japan. Tel: +81-3-3433-1111; E-mail: taoki@jikei.ac. jp . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 18, 2020. ; https://doi.org/10.1101/2020.05. 14.20102442 doi: medRxiv preprint
Experimental studies of the early effects of inhaled beta-emitting radionuclides for nuclear accident risk assessment: Phase 2 report

This report summarizes a series of experiments concerning the effect of linear energy transfer and temporal radiation dose pattern to the lung from inhaled beta-emitting radionuclides. The results were used to test the validity of a hazard-function mathematical model for predicting death from radiation pneumonitis. Both morbidity and mortality within 18 months after exposure were examined in rats exposed to beta-emitting radionuclides, giving brief or protracted irradiation of the lung or having weak or strong beta emissions. Protraction of the radiation dose to the lung from a half-time in the lung of less than three days to a half-time with a long-term component of about 150 days has a sparing effect. The median lethal dose for the protracted irradiation is about 1.7 times the median lethal dose for the brief irradiation. Low energy beta emissions from /sup 147/Pm have a similar effectiveness in producing lethal injury as high energy beta emissions from /sup 90/Sr. Changes in three parameters of morbidity were measured: body weight, hematology and pulmonary function; only changes in pulmonary function correlated well with pulmonary radiation injury. The doses of radiation required to produce impaired function, however, were not significantly different from those that produced death. The hazard-functionmore » model for predicting death from radiation pneumonitis, which was developed from previously obtained data for inhalation exposures of dogs to beta-emitting radionuclides, adequately predicted the median lethal doses for rats receiving one of several different beta dose rate patterns to the lung, thus strengthening the validity of the mathematical model. 23 refs., 41 figs., 12 tabs. « less