Miracle-dz/UniFAR-FTUs huggingface dataset:
Full Characterization of Thrombotic Events in All Hospitalized COVID-19 Patients in a Spanish Tertiary Hospital during the First 18 Months of the Pandemic

The presence of a procoagulant state, COVID-19-related coagulopathy, and an increased rate of thrombotic events (TEs) is widely known about. However, descriptive studies are scarce. Here, we conducted a large retrospective study including 2894 hospitalized COVID-19 patients followed up during the first 18 months of the pandemic to completely characterize any TE. Major TEs showed a 3.45% incidence rate. TEs were associated with increased intubation/90-day mortality risk [OR = 1.71, 95% CI (1.12–2.61), p < 0.013]. Venous thrombotic events (VTEs) were more frequent than arterial thrombotic events (ATEs) (72% vs. 28%), associated with enhanced levels of D-dimer (cross-linked fibrin derivatives formed during thrombolysis), which were related to mortality but more useful for early detection of thrombosis. In this regard, D-dimer plasma levels above 2014 µg/mL at hospital admission identify TEs with 91% accuracy (AUC = 0.91, p < 0.001), rising to almost 95% (AUC = 0.94, p < 0.001) with a cut-off value of 2666 µg/mL in VTEs. Moreover, 41% of TEs occurred in patients receiving LMWH thromboprophylactic treatments in hospital or domiciliary therapies. SARS-CoV-2 infection along with a sedentary lifestyle derived from the confinement in 2020 could be more determinant than a procoagulant state in patients with risk factors for TEs. Furthermore, the normal results obtained from the thrombophilia study after the acute process are linked to this independent procoagulant state and to SARS-CoV-2-derived coagulopathy.
Resistin Associated With Cytokines and Endothelial Cell Adhesion Molecules Is Related to Worse Outcome in COVID-19

Introduction Resistin is reported to form a cytokine network and cause endothelial damage. The pathogenesis of coronavirus disease 2019 (COVID-19) remains unknown, but the association between cytokine storm and endothelial damage is crucial. This study aimed to evaluate resistin in COVID-19 pathogenesis compared with sepsis. Materials and Methods First, we evaluated the association of plasma resistin levels and disease severity and clinical outcome in two large cohorts: a publicly available cohort including 306 COVID-19 patients in the United States (MGH cohort) and our original cohort including only intubated 113 patients in Japan (Osaka cohort 1). Second, to understand pathogenesis, we evaluate resistin, cytokines and endothelial cell adhesion molecules in COVID-19 compared with sepsis. Blood samples were collected from 62 ICU-treated COVID-19 patients and 38 sepsis patients on day 1 (day of ICU admission), days 2-3, days 6-8, and from 18 healthy controls (Osaka cohort 2). The plasma resistin, inflammatory cytokines (IL-6, IL-8, MCP-1 and IL-10) and endothelial cell adhesion molecules (ICAM-1 and VCAM-1) were compared between patients and control. Correlations among resistin, inflammatory cytokines and endothelial cell adhesion molecules were evaluated in COVID-19 and sepsis. Results In the MGH cohort, the day 1 resistin levels were associated with disease severity score. The non-survivors showed significantly greater resistin levels than survivors on days 1, 4 and 8. In the Osaka cohort 1, 28-day non-survivors showed significantly higher resistin levels than 28-day survivors on days 6-8. Patients with late recovery (defined as the day of weaning off mechanical ventilation >12 or death) had significantly higher resistin levels than those with early recovery on day 1 and days 6-8. In the Osaka cohort 2, plasma resistin levels were elevated in COVID-19 and sepsis patients compared to controls at all measurement points and were associated with inflammatory cytokines and endothelial cell adhesion molecules. Conclusion Resistin was elevated in COVID-19 patients and was associated with cytokines and endothelial cell adhesion molecules. Higher resistin levels were related to worse outcome.
Residual‐error prediction based on deep learning for lossless image compression

A novel residual-error prediction method based on deep learning with application in lossless image compression is introduced. The proposed method employs machine learning tools to minimise the residual error of the employed prediction tools. Experimental results demonstrate average bitrate savings of 32% over the state-of-the-art in lossless image compression. To the best of the authors’ knowledge, this Letter is the first to propose a deep-learning based method for residual-error prediction.
Physical Activity and Its Barriers and Facilitators among University Students in Qatar: A Cross-Sectional Study

Physical inactivity is a leading risk factor for non-communicable diseases worldwide. This study investigated physical activity (PA) level among university students in Qatar and assessed other lifestyle and demographic factors associated with PA. A cross-sectional study was conducted between February 2017 and February 2018. A self-administered questionnaire, comprising questions from the International Physical Activity Questionnaire and other validated questionnaires, was used to assess PA and other lifestyle behaviors, including sedentary behavior, stress, sleep, dietary habits, and smoking habits. The results were reported according to the STROBE guidelines. A total of 370 students (response rate = 95.6%) were recruited from eight universities via quota sampling. The prevalence of physically active students—as per the World Health Organization’s recommendation for PA of 150–300 min/week—was 64.9% (75.2% in males and 58.3% in females). Females and students >20 years old were half as likely to be active compared with males and younger students. More males enjoyed getting regular exercise (83.6% vs. 67.7%, p-value = 0.002). Time spent sitting was similar during weekdays and weekends (mean time ± SD = 480.8 ± 277.7 min/week vs. 492.1 ± 265.0 min/week). Sports facilities and green spaces appear to help increase PA among university students in Qatar. Public health interventions should focus on improving PA-related perception and knowledge among students to further increase PA participation.
Performance analysis of junctionless DG‐MOSFET‐based 6T‐SRAM with gate‐stack configuration

In this work, the investigation of high- K gate-stack-based junctionless (JL) double-gate (DG) metal-oxide-semiconductor field-effect transistor (MOSFET) is carried out to study the high- K gate dielectric effect on six-transistor (6T)-static random-access memory (SRAM) built with JLDG-MOSFET. It is observed that the utilisation of the high- K gate dielectric in JLDG-MOSFETs improves the static noise margin (SNM) that is the stability of the cell as well as access time (AT) which reflects the delay performance of the SRAM cell. Furthermore, scaling down of L g degrades the stability. Moreover, it is also described that the enhancement in hold SNM (ΔHSNM = HSNM (K=40) -HSNM (K=3.9) ), read SNM (ΔRSNM = RSNM (K=40) -RSNM (K=3.9) ), and write SNM (ΔWSNM = WSNM (K=40) -WSNM (K=3.9) ) is restricted at lower L g without much enhancement in the improvement of read AT and write AT. Therefore, it is appropriate to consider higher channel length (~30 nm) while designing high- K gate-stacked-based JLDG-MOSFETs for the 6T-SRAM cell.
Efficacy and safety of olaparib in the treatment of platinum-sensitive recurrent ovarian cancer: a systematic review and meta-analysis

*Correspondence wang19621026@hotmail.com (Qiming Wang) Abstract Background: Olaparib, an orally active inhibitor, has undergone comprehensive clinical evaluation as either single or combination therapy in several solid tumors. To explore the efficacy and safety of Olaparib versus placebo in the treatment of platinumsensitive recurrent ovarian cancer, we conducted a systematic review and meta-analysis. Methods: We systemically searched through April 30, 2021 PubMed, Cochrane Library, Embase andWeb of Science for randomized control trials (RCTs) that comparedOlaparib and placebo therapies for platinum-sensitive recurrent ovarian cancer. The primary outcomes were progression-free survival (PFS) and overall survival (OS) evaluated by Hazard ratio (HR)with 95%CI and the secondary outcomewas adverse events calculated by the risk ratio (RR) with 95% CI.Results: A total of 3 RCTs, involving 592 patients in the Olaparib group and 359 patients in the placebo therapy, were included. The analysis results of RCTs showed that Olaparib had a significantly better PFS than placebo (HR 0.31; 95% CI 0.26–0.37; p ≤ 0.001), and the pooled OS of the Olaparib group was significantly higher than that of the placebo group (RR 0.73; 95% CI 0.60–0.90; p = 0.003). Regarding safety, the main adverse events included nausea, fatigue, vomiting, anemia, diarrhea, abdominal pain, constipation, headache, dysgeusia and decreased appetite. Conclusions: Olaparib has a positive effect on platinum-sensitive recurrent ovarian cancer as related to progression and overall survival with an increase in adverse events being noted. This meta-analysis demonstrated that Olaparib maintenance therapy is generally well tolerated by the patients.
Reciprocal Effects of Word of Mouse and Online Trust: A Three-Wave Longitudinal Study

Abstract This is the first study to investigate the reciprocal relationship between word of mouse and online trust. It was expected that consumers develop online trust through word of mouse information, and conversely, consumers who trust online sellers consume word of mouse (WOMS) to affirm their initial beliefs. Hypotheses were tested with a sample of 447 consumers based on a three-wave longitudinal design. The results supported that a reciprocal relationship exists between WOMS and online trust. Word of mouse predicted subsequent online trust, and online trust predicted word of mouse. Since these reciprocal effects indicate a virtuous cycle with positive effects of online trust and WOMS for both sellers and consumers, the results of this study have implications for both researchers and practitioners alike.
Intrinsic Exercise Capacity Affects Glycine and Angiotensin-Converting Enzyme 2 (ACE2) Levels in Sedentary and Exercise Trained Rats

Angiotensin-converting enzyme 2 (ACE2) has been identified as the cellular entry receptor for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). High ACE2 tissue expression and low glycine levels were suggested to increase susceptibility for SARS-CoV-2 infection and increasing circulating ACE2 has been proposed as one possible strategy to combat COVID-19. In humans, aerobic physical exercise induces an increase in plasma ACE2 in some individuals. However, it is not clear whether glycine and ACE2 levels depend on intrinsic exercise capacity or on exercise training. We used rats selectively bred for high intrinsic exercise capacity (HCR) or low exercise capacity (LCR) and tested the influence of this genetic predetermination and/or aerobic exercise on metabolites, ACE2 tissue expression and circulating ACE 2. ACE2 expression was measured in different tissues in the sedentary animals and again after 4 weeks of high-intensity aerobic exercise in both LCRs and HCRs. Sedentary HCRs exhibited significantly higher circulating ACE2 concentrations compared to LCRs, but a lower expression of ACE2 in all investigated tissues except for adipose tissue. Body weight was negatively correlated with serum ACE2 and positively correlated with ACE2 expression in the heart. Aerobic exercise caused a significant decrease in ACE2 expression in the lung, heart, muscle, and kidney both in LCRs and HCRs. Our results suggest that ACE2 expression, circulating ACE2 and glycine serum concentration are related to aerobic intrinsic exercise capacity and can be influenced with exercise. These results may support the hypothesis that physically fit individuals have a lower susceptibility for COVID-19 infection.
Multitask Sparse Bayesian Learning with Applications in Structural Health Monitoring

We focus on a Bayesian approach to learn sparse models by simultaneously utilizing multiple groups of measurements that are marked by a similar sparseness profile. Joint learning of sparse representations for multiple models has been mostly overlooked, although it is a useful tool for exploiting data redundancy by modeling informative relationships within groups of measurements. To this end, two hierarchical Bayesian models are introduced and associated algorithms are proposed for multitask sparse Bayesian learning (SBL). It is shown that the data correlations for different tasks are taken into account more effectively by using the hierarchical model with a common prediction‐error precision parameter across all related tasks, which leads to a better learning performance. Numerical experiments verify that exploiting common information among multiple related tasks leads to better performance, for both models that are highly and approximately sparse. Then, we examine two applications of multitask SBL in structural health monitoring: identifying structural stiffness losses and recovering missing data occurring during wireless transmission, which exploit information about relationships in the temporal and spatial domains, respectively. These illustrative examples demonstrate the potential of multitask SBL for solving a wide range of sparse approximation problems in science and technology.
MINERAL AND NUTRITIONAL CONTENTS OF HEN'S EGGS FROM DIFFERENT SOURCES IN ADEN GOVERNORATE-YEMEN

Many studies are needed to be done in a nutrition field and people are advised to eat eggs daily to reduce mineral deficiencies. The purpose of this study is to evaluate the concentrations of six essential elements: Na, Mg, P, S, K, Ca and determine the nutrient contents such as; protein, lipid, ash, and moisture in the edible part of hen eggs selected from four different sources in Aden (i.e. Al-Buraiqeh, Al-Hiswah, Khormaksar, and Shaikh Othman). These elements were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES). The highest concentrations of sulfur were found in hen egg samples derived from the Khormaksar (4205 ppm) and Shaik Othman (4433 ppm) regions. Moreover, the concentrations of sodium in selected raw eggs from the same regions were 3044 and 2758 ppm, respectively. On contrary, the collected egg samples from Al-Buraiqeh had in general, low levels of elements. The macro Kjeldahl method was used to determine protein in egg samples while the total lipid content was determined using the Soxhlet method and the Gerber method. The high percentage of protein content was noted in market hen eggs from Al-Hiswah (12.80%) but, the lowest percentage of protein content was derived from street hen eggs from Al-Hiswah (7.66%). For the lipid percent, the high concentration was remarked to home hen eggs from Khormaksar (12.71%) while the lowest percentage of protein content was coined for market hen eggs from Al-Buraiqeh (9.91%). This data can be attributed to hen feed specifics within different regions and the impact of environmental factors. The percentage of ash and moisture was also determined in this study. The obtained results were compared with others achieved in different countries. In all analyzed raw eggs, the concentrations of minerals and nutritional contents were safe levels.
Application of Hybrid Differential Evolution–Grey Wolf Optimization Algorithm for Automatic Generation Control of a Multi-Source Interconnected Power System Using Optimal Fuzzy–PID Controller

Abstract This paper deals with an optimal hybrid fuzzy-Proportional Integral Derivative (fuzzy-PID) controller optimized by hybrid differential evolution–Grey Wolf optimization algorithm for automatic generation control of an interconnected multi-source power system. Here a two area system is considered; each area is provided with three types of sources namely a thermal unit with reheat turbine, a hydro unit and a gas unit. The dynamic performance of the system is analyzed under two cases: with AC tie-line and with AC-DC tie-line. The efficiency and effectiveness of the proposed controller is substantiated equally in the two cases. The sturdiness of the system is proved by varying the values of the system parameters. The supremacy of the recommended work is additionally ascertained by comparison with the recently published results like differential evolution optimized PID Controller and hybrid Local Unimodal Sampling-Teaching Learning based Optimization (LUS-TLBO) optimized fuzzy-PID controller. The dynamic performance of the system is observed in terms of settling time, peak overshoot and peak undershoot. Finally the analysis is extended by applying the proposed control technique in two different models namely (i) A three area unequal thermal system considering proper generation rate constraints (GRC) and (ii) A three area hydro-thermal system with mechanical hydro governor. These test results reveal the adaptability of the proposed method in multi-area interconnected power system.
Numerical simulation of tunneling through arbitrary potential barriers applied on MIM and MIIM rectenna diodes

With the continuous miniaturization of electronic devices, quantum-mechanical effects such as tunneling become more effective in many device applications. In this paper, a numerical simulation tool is developed under a MATLAB environment to calculate the tunneling probability and current through an arbitrary potential barrier comparing three different numerical techniques: the finite difference method, transfer matrix method, and transmission line method. For benchmarking, the tool is applied to many case studies such as the rectangular single barrier, rectangular double barrier, and continuous bell-shaped potential barrier, each compared to analytical solutions and giving the dependence of the error on the number of mesh points. In addition, a thorough study of the J − V characteristics of MIM and MIIM diodes, used as rectifiers for rectenna solar cells, is presented and simulations are compared to experimental results showing satisfactory agreement. On the undergraduate level, the tool provides a deeper insight for students to compare numerical techniques used to solve various tunneling problems and helps students to choose a suitable technique for a certain application.
Characterization of Imidazole Compounds in Aqueous Secondary Organic Aerosol Generated from Evaporation of Droplets Containing Pyruvaldehyde and Inorganic Ammonium

Imidazole compounds are important constituents of atmospheric brown carbon. The imidazole components of aqueous secondary organic aerosol (aqSOA) that are generated from the evaporation of droplets containing pyruvaldehyde and inorganic ammonium are on-line characterized by an aerosol laser time-of-flight mass spectrometer (ALTOFMS) and off-line detected by optical spectrometry in this study. The results demonstrated that the laser desorption/ionization mass spectra of aqSOA particles that were detected by ALTOFMS contained the characteristic mass peaks of imidazoles at m/z = 28 (CH2N+), m/z = 41 (C2H3N+) and m/z = 67 (C3H4N2+). Meanwhile, the extraction solution of the aqSOA particles that were measured by off-line techniques showed that the characteristic absorption peaks at 217 nm and 282 nm appeared in the UV-Vis spectrum, and the stretching vibration peaks of C-N bond and C=N bond emerged in the infrared spectrum. Based on these spectral information, 4-methyl-imidazole and 4-methyl-imidazole-2-carboxaldehyde are identified as the main products of the reaction between pyruvaldehyde and ammonium ions. The water evaporation accelerates the formation of imidazoles inside the droplets, possibly owing to the highly concentrated environment. Anions, such as F−, CO32−, NO3−, SO42− and Cl− in the aqueous phase promote the reaction of pyruvaldehyde and ammonium ions to produce imidazole products, resulting in the averaged mass absorption coefficient () in the range of 200–600 nm of aqSOA increases, and the order of promotion is: F− > CO32− > SO42− ≈ NO3− ≈ Cl−. These results will help to analyze the constituents and optics of imidazoles and provide a useful basis for evaluating the formation process and radiative forcing of aqSOA particles.
On classification of road types for automotive applications

One of the challenges within autonomous driving is for the vehicle to determine what kind of surrounding environment it is operating in. This information could assist the vehicle in its decision making. In this project two methods, based on neural networks and support vector machines, for determining road types using radar and image data have been compared. The road types were divided in to three classes, highway, major road and city. The best result was achieved by a neural network using image data. Using radar data gave the worst results and that had a negative effect on the classification using radar and image data in combination.
Evolution of pretransplant cardiac risk factor burden and major adverse cardiovascular events in liver transplant recipients over time

Major adverse cardiovascular events (MACEs) are the leading cause of early (<1 y) complications after liver transplantation (LT). NASH, the leading indication for waitlisting for LT, is associated with high cardiac risk factor burden. The contemporary prevalence and temporal trends in pretransplant cardiac risk factor burden and post-LT MACE among LT recipients (LTRs) with and without NASH are unknown. The aim of this study was to evaluate (1) the evolution of post-LT cardiac risk factors in LTRs over time and (2) post-LT MACE over time, stratified by NASH status. This is a retrospective cohort of 1775 adult LTRs at a single transplant center (2003–2020). MACE was defined as death or hospitalization from myocardial infarction, revascularization, stroke, heart failure during the first post-LT year. Between 2003 and 2020, there was a significant increase in pre-LT NASH (p trend <0.05). There was also a significant increase in pre-LT obesity, atherosclerotic cardiovascular (CV) disease, and older age (≥65 y old) (p trend<0.05 for all). There was no significant change in the proportion of LTRs with diabetes, chronic kidney disease, or heart failure. Unexpectedly, there were no changes in the rate of post-LT MACE over the study period (−0.1% per year, p trend=0.44). The lack of change in MACE despite an increase in CV risk factor prevalence may reflect advancement in the identification and management of CV risk factors in LTRs. With projected continued increase in cardiac risk burden and the proportion of patients transplanted for NASH, it is critical for LT programs to develop and implement quality improvement efforts to optimize CV care in LTRs.