Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstructive Sleep Apnea and Atrial Fibrillation

Atrial fibrillation (AF) is the most common arrhythmia, increasing with age and comorbidities. Obstructive sleep apnea (OSA) is a chronic sleep disorder more common in older men. It has been shown that OSA is linked to AF. Nonetheless, the prevalence of OSA in patients with AF remains unknown because OSA is significantly underdiagnosed. This review, including 54,271 patients, carried out a meta-analysis to investigate the association between OSA and AF. We also performed a meta-regression to explore cofactors influencing this correlation. A strong link was found between these two disorders. The incidence of AF is 88% higher in patients with OSA. Age and hypertension independently strengthened this association, indicating that OSA treatment could help reduce AF recurrence. Further research is needed to confirm these findings. Atrial Fibrillation (AF) is the most common arrhythmia, increasing with age and comorbidities. Obstructive sleep apnea (OSA) is a regulatory respiratory disorder of partial or complete collapse of the upper airways during sleep leading to recurrent pauses in breathing. OSA is more common in older men. Evidence exists that OSA is linked to AF. Nonetheless, the prevalence of OSA in patients with AF remains unknown because OSA is underdiagnosed. In order to investigate the incidence of AF in OSA patients, we carried out a meta-analysis including 20 scientific studies with a total of 54,271 subjects. AF was present in 4801 patients of whom 2203 (45.9%) had OSA and 2598 (54.1%) did not. Of a total of 21,074 patients with OSA, 2203 (10.5%) had AF and 18,871 (89.5%) did not. The incidence of AF was 88% higher in patients with OSA. We performed a meta-regression to explore interacting factors potentially influencing the occurrence of AF in OSA. Older age and hypertension independently strengthened this association. The clinical significance of our results is that patients with OSA should be referred early to the cardiologist. Further research is needed for the definition of the mechanisms of association between AF and OSA.
Pharmacological Therapies for Osteoporosis: A Bayesian Network Meta-Analysis

Background Numerous randomized controlled trials (RCTs) have evaluated pharmacological therapies for osteoporosis. The aim of this Bayesian network meta-analysis was to compare the efficacy and safety of pharmacological therapies for osteoporosis patients. Material/Methods The electronic databases of PubMed, Embase, and Cochrane Library were systematically searched for eligible RCTs from their inception up to January 2021. The primary endpoints were all fractures, vertebral fractures, and non-vertebral fractures, while the secondary endpoints were fractures at hip or peripheral locations, bone mineral density (BMD) at various sites, and potential adverse events. Results We included 79 RCTs reporting a total of 108 797 individuals in the final quantitative analysis. The results of network analysis indicated that romosozumab (92.1%) was the most effective in reducing the risk for all fractures, with the best therapeutic effects on vertebral fracture (97.2%) and non-vertebral fracture (88.0%). Romosozumab (92.5%) provided better therapeutic effects for the reduction of hip fracture. The best treatment agents for improving whole-body BMD (100.0%), spine BMD (95.7%), hip BMD (92.4%), femoral neck BMD (86.7%), and trochanter BMD (95.5%) were alendronate, strontium ranelate, ibandronate, risedronate, and ibandronate, respectively. Finally, the use of bazedoxifene was associated with the highest incidence of any upper-gastrointestinal event, nasopharyngitis, and back pain, while risedronate was associated with higher incidence of abdominal pain and dyspepsia. Conclusions This study found that romosozumab yielded the best effects for preventing fracture risk, while abaloparatide was the most effective in reducing the risk of vertebral fracture and non-vertebral fracture.
Multistep and continuous physics-informed neural network methods for learning governing equations and constitutive relations

We investigate the applicability and relative merit of discrete and continuous versions of physics-informed neural network (PINN) methods for learning unknown governing equations or constitutive relations in a nonlinear dynamical system. In the case of unknown dynamics, entire right-hand-side (RHS) equations of the ordinary differential equations are unknown. In the case of unknown constitutive relations, however, the RHS equations are known up to the speciﬁcation of constitutive relations (that may depend on the state of the system). We use a deep neural network to model unknown governing equations or constitutive relations. The discrete PINN approach combines classical multistep discretization methods for dynamical systems with neural-network-based machine learning methods. On the other hand, the continuous versions utilize deep neural networks to minimize the residual function for the continuous governing equations. We use the case of a fedbatch bioreactor system to study the effectiveness of these approaches and discuss conditions for their applicability. Our results indicate that the accuracy of the trained neural network models is much higher for the cases where we only have to learn a constitutive relation instead of all dynamics. This ﬁnding corroborates the well-known fact from scientiﬁc computing that building as much structural information as is available into an algorithm can enhance its efﬁciency and/or accuracy.
OpiumUse and Cancer Risk: A Comprehensive Systematic Review and Meta-Analysis of Observational Studies

Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University ofMedical Sciences, Shariati Hospital, Tehran, Iran Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran Department of Biostatistics, Tehran University of Medical Sciences (TUMS), Tehran, Iran Food Security Research Center, Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran
High Temperature and Water De cit Cause Epigenetic Changes in Somatic Plants of Pinus Radiata D. Don

ANTONIA MAIARA MARQUES DO NASCIMENTO (  mmarques@neiker.eus ) NEIKER Instituto Vasco de Investigacion y Desarrollo Agrario SA https://orcid.org/0000-0002-98782084 Itziar Aurora Montalbán NEIKER Instituto Vasco de Investigacion y Desarrollo Agrario SA https://orcid.org/0000-0002-18685058 Diego Llamazares de Miguel NEIKER-Tecnalia: NEIKER Instituto Vasco de Investigacion y Desarrollo Agrario SA Tomás Goicoa Universidad Publica de Navarra Campus de Arrosadia: Universidad Publica de Navarra María Dolores Ugarte Universidad Publica de Navarra Campus de Arrosadia: Universidad Publica de Navarra Paloma Moncaleán NEIKER Instituto Vasco de Investigacion y Desarrollo Agrario SA
The Re-Envisioned Educational Technology Course

Abstract This paper presents a new model for the required preservice technology integration course. We situate our model within the literature on the dominant stand-alone model, as well as the alternative models that have been explored. We then detail our restructured model of three one-hour courses that include focus on Introduction and Development, Integration and Evaluation, and Implementation and Assessment. We will highlight the challenges we have faced as well as our plans for continued course development. We argue that not only is our technology integration class sequence a feasible transition from stand-alone educational technology courses to a fully integrated model, but it also has unique merit for the cognitive development of our students in and of itself.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

Human reinforcement learning (RL) is characterized by different challenges. Exploration has been studied extensively in multi-armed bandits, while planning has been investigated in multi-step decision tasks. More recent work has added structured rewards to study generalization. However, past studies have often focused on a single one of these aspects, making it hard to compare results. We propose a generative model for constructing correlated trees to provide a unified and scalable method for studying exploration, planning, and generalization in a single task. In an online experiment, we find that people use structure (when provided) to generalize and perform uncertainty-directed exploration, with structure helping more in larger environments. In environments without structure, exploration becomes more random and more planning is needed. All behavioral effects are captured in a single model with recoverable parameters. In conclusion, our results connect past research on human RL in one framework using correlated trees.
Reverse Engineering Gas Turbine Emission Performance: Applied to an Aircraft Auxiliary Power Unit

Environmental and future supply pressures are expected to drive aviation towards alternative fuel sources. However little is available in the literature on aircraft landing-takeoff (LTO) cycle gaseous emissions resulting from the combustion of alternative fuels. Considering the different engine configurations existing in today’s commercial aviation fleet, emission experiments of alternative fuels on all engine types are almost impossible. Modelling may provide a solution but the availability of combustor data (geometry and air split details) in the public domain is limited. A reverse engineering technique is developed to recover the air splits and combustion process in gas turbine engine by a CRN and forward predicting the emissions from the engine exhaust. The model was developed and optimised with a Genetic Algorithm against the Jet A-1 experimental emission data obtained from an APU. Results from the optimised CRN emission predictions closely matched the Jet A-1 gaseous emission data. The modelling technique also successfully demonstrated an ability to predict APU gaseous emission data obtained for Synthetic Paraffinic Kerosene (SPK) (neat and 50–50 blended with Jet A-1) and biodiesel. This technique is expected to enhance the emission databank of aircraft and airside emissions.Copyright © 2010 by ASME
Speech Recognition Using Unbiased And Normalized Adaptation Noise Reduction (Unanr) Algorithm Using Adaptive Filtering And Naive Bayes Classifier

:- In the human communicating system, speech signals are significantly vital. In speech processing functionalities like Automated Speech Recognition (ASR) system, removing background noise is critical since it reduces the system's performance. The adaptive filtering strategy is often employed as a filtering technique which will help in noise reduction and also for speech enhancement to tackle this difficulty. The Least Mean Square (LMS) approach is commonly used in adaptive filters to remove noise, although in practice, this approach has several drawbacks in voice enhancement. In this study, a robust adaptive filtering method based on the Unbiased and Normalized Adaptive Noise Reduction (UNANR) algorithm is used to de-noise voice signals. The Naive Bayes classifier is used for classification in order to improve the recognition of speech. According to the results, the UNANR approach outperformed the LMS algorithm in accuracy after classification using the Naive Bayes classifier. The UNANR accuracy was around 89 % which is higher than the baseline LMS algorithm which produces accuracy of 86%.
NyxCache: Flexible and Efficient Multi-tenant Persistent Memory Caching

We present NyxCache (Nyx), an access regulation framework for multi-tenant persistent memory (PM) caching that supports light-weight access regulation, per-cache resource usage estimation and inter-cache interference analysis. With these mechanisms and existing admission control and capacity allocation logic, we build important sharing policies such as resource-limiting, QoS-awareness, fair slowdown, and proportional sharing: Nyx resource-limiting can accurately limit PM usage of each cache, providing up to 5⇥ better performance isolation than a bandwidth-limiting method. Nyx QoS can provide QoS guarantees to latency-critical caches while providing higher throughput (up to 6⇥ vs. previous DRAM-based approaches) to best-effort caches that are not interfering. Finally, we show that Nyx is useful for realistic workloads, isolating write spikes, and ensuring that important caches are not slowed down by increased best-effort traffic.
Calculating the vertical column density of O4 during daytime from surface values of pressure, temperature, and relative humidity

We present a formalism that relates the vertical column density (VCD) of the oxygen collision complex O2– O2 (denoted as O4 below) to surface (2 m) values of temperature and pressure, based on physical laws. In addition, we propose an empirical modification which also accounts for surface relative humidity (RH). This allows for simple and quick calculation of the O4 VCD without the need for constructing full vertical profiles. The parameterization reproduces the true O4 VCD, as derived from vertically integrated profiles, within −0.7± 1.2% (mean±SD) for Weather Research and Forecasting (WRF) simulations around Germany, 0.2± 1.8% for global reanalysis data (ERA5), and −0.3± 1.4% for Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) radiosonde measurements around the world. When applied to measured surface values, uncertainties of 1 K, 1 hPa, and 16 % for temperature, pressure, and RH correspond to relative uncertainties of the O4 VCD of 0.3 %, 0.2 %, and 1 %, respectively. The proposed parameterization thus provides a simple and accurate formula for the calculation of the O4 VCD which is expected to be useful in particular for MAX-DOAS applications.
PPA_A_348152 515..560

1Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada; 2Women’s College Research Institute, Women’s College Hospital, Toronto, Ontario, Canada; 3Michael G.Degroote School Of Medicine, McMaster University, Hamilton, Ontario, Canada; 4OpenLab, University Health Network, Toronto, Ontario, Canada; 5Gerstein Science Information Centre, University of Toronto, Toronto, Ontario, Canada; 6Institute for Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; 7Sinai Health, Toronto, Ontario, Canada; 8Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada; 9Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada; 10Ted Rogers School of Management, School of Health Services Management, Ryerson University, Toronto, Ontario, Canada; 11Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; 12Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada
Effect of chitosan coatings on quality and shelf-life of chicken and quail eggs

In this study, chitosan-coatings were prepared with acetic, lactic, propionic, gallic and caffeic acids and used for coating chicken and quail eggs to understand their effect on the quality and shelf-life of chicken and quail eggs. Shelf-life study of (weight loss, Haugh unit, yolk index, albumen pH, mineral levels and shell breaking strength) the coating formulations were investigated for 4 weeks. All chitosan coated chicken and quail egg samples showed greater interior (weight loss, Haugh unit, yolk index, albumen pH) and exterior quality (shell breaking strength) than non-coated samples (p<0.05).
The “Good” Schools: Academic Performance Data, School Choice, and Segregation

We examine the effects of disseminating school-level academic performance data—achievement status, achievement growth, or both—on parents’ school choices and their implications for racial, ethnic, and economic segregation. Many researchers consider growth to be a superior (if still imperfect) measure of school effectiveness relative to status. Moreover, compared to status, growth has weaker relationships with schools’ demographic compositions. We conduct an online survey experiment featuring a nationally representative sample of parents and caretakers of children ages 0–12. Participants choose between three randomly sampled elementary schools drawn from the same school district. The provision of status information guides participants toward schools with higher achievement status and fewer Black, Hispanic, and economically disadvantaged students. The provision of growth information and the provision of both types of academic performance data guide participants toward higher growth schools. However, only growth information—alone, and not in concert with status information—tends to elicit choices with desegregating consequences.
Characterizing the Adoption of Security.txt Files and their Applications to Vulnerability Notification

Abstract —While security researchers are adept at discovering vulnerabilities and measuring their impact, disclosing vulnerabilities to affected stakeholders has traditionally been difficult. Beyond public notices such as CVEs, there have traditionally been few appropriate channels through which to directly communicate the nature and scope of a vulnerability to those directly impacted by it. Security.txt is a relatively new proposed standard that hopes to change this by defining a canonical file format and URI through which organizations can provide contact information for vulnerability disclosure. However, despite its favourable characteristics, limited studies have systematically analyzed how effective Security.txt might be for a widespread vulnerability notification c ampaign. I n t his p aper, w e p resent a large-scale study of Security.txt’s adoption over the top 1M popular domains according to the Tranco list. We measure specific f eatures of Security.txt files such as contact information, preferred language, and RFC version compliance. We then analyze these results to better understand how suitable the current Security.txt standard is for facilitating a large-scale vulnerability notification campaign, and make recommendations for improving future version of the standard.
Multiple cancer type classification by small RNA expression profiles with plasma samples from multiple facilities

Liquid biopsy is expected to be a promising cancer screening method because of its low invasiveness and the possibility of detecting multiple types in a single test. In the last decade, many studies on cancer detection using small RNAs in blood have been reported. To put small RNA tests into practical use as a multiple cancer type screening test, it is necessary to develop a method that can be applied to multiple facilities. We collected samples of eight cancer types and healthy controls from 20 facilities to evaluate the performance of cancer type classification. A total of 2,475 cancer samples and 496 healthy control samples were collected using a standardized protocol. After obtaining a small RNA expression profile, we constructed a classification model and evaluated its performance. First, we investigated the classification performance using samples from five single facilities. Each model showed areas under the receiver curve (AUC) ranging from 0.67 to 0.89. Second, we performed principal component analysis (PCA) to examine the characteristics of the facilities. The degree of hemolysis and the data acquisition period affected the expression profiles. Finally, we constructed the classification model by reducing the influence of these factors, and its performance had an AUC of 0.76. The results reveal that small RNA can be used for the classification of cancer types in samples from a single facility. However, interfacility biases will affect the classification of samples from multiple facilities. These findings will provide important insights to improve the performance of multiple cancer type classifications using small RNA expression profiles acquired from multiple facilities.
Development and realization of energy management system in process industry, Part II: Advanced functions

RAZVOJ I REALIZACIJA SISTEMA NADZORA I UPRAVLJANJA TOKOVIMA ENERGENATA U PROCESNOJ INDUSTRIJI – DEO II: NAPREDNE FUNKCIJE DEVELOPMENT AND REALIZATION OF ENERGY MANAGEMENT SYSTEM IN PROCESS INDUSTRY – PART II: ADVANCED FUNCTIONS Dr Filip KULIĆ, mr Veroslav JANKOVIĆ**,dr Đura OROS*, dr Veran VASIĆ*, * Fakultet tehničkih nauka, 21000 Novi Sad; Trg Dositeja Obradovića 6 ** PRO-ENERGO d.o. o. , 21000 Novi Sad, Bulevar Kralja Petra I 28c / IV-13
Effects of neuromuscular electrical stimulation on exercise capacity, muscle strength and quality of life in COPD patients: A Systematic Review with Meta-Analysis

Objective To determine the effects of neuromuscular electrical stimulation on disabilities and activity limitation of individuals affected by chronic obstructive pulmonary disease. Data sources MEDLINE, PEDro database, Cochrane Controlled Trials Register, and SciELO, were searched from inception until October 2021. Review methods Inclusion criteria were patients with COPD, randomized controlled trials comparing neuromuscular electrical stimulation alone or combined conventional pulmonary rehabilitation and neuromuscular electrical stimulation versus control or sham or pulmonary rehabilitation in disabilities and activity limitation in COPD. There were no mandatory language or publication date restrictions. Two reviewers selected studies independently. Weighted mean differences and 95% confidence intervals were calculated. Results 32 studies met the study criteria, including 1.269 participants. Neuromuscular electrical stimulation improved exercise capacity (MD 1.10, 95% CI: 0.33, 1.86, N = 147), and muscle strength (0.53, 95% CI: 0.20, 0.87, N = 147) compared to sham group. Combined neuromuscular electrical stimulation and conventional rehabilitation improved exercise capacity (MD 34.28 meters, 95% CI: 6.84, 61.73, N = 262) compared to conventional rehabilitation alone. No adverse events were reported. Conclusions Neuromuscular electrical stimulation resulted in small improvement in disabilities and activity limitation (below the MCID) in COPD. Thus, the inclusion of neuromuscular electrical stimulation in rehabilitation programs must consider the cost Because of inadequate methodological conduction and reporting of methods, some studies were of low quality.
An integrative phylogenetic analysis of eastern Nearctic Leuctra (Plecoptera: Leuctridae), with an emphasis on the fauna of a southern Appalachian Highlands landscape

Abstract Leuctra Stephens, 1836 is the fourth most speciose genus of Plecoptera (Leuctridae) east of the Rocky Mountains with 31 recognised species, trailing only Isoperla Banks, 1906 (58), Allocapnia Claassen, 1928 (47), and Perlesta Banks, 1906 (34). Although Leuctra females are described in taxonomic literature, they are difficult to morphologically distinguish among regional congeners, and identifications are often made through inference only (i.e., presence of males). This is particularly problematic in the Appalachian Mountains of the United States of America, which host numerous Leuctra species. We sampled the stonefly fauna of Mount Mitchell, western North Carolina, United States of America, from April to October 2019. The mitochondrial cytochrome C oxidase subunit 1 gene was sequenced for 39 adult males and 239 adult females of Leuctra. This allowed us to confidently place species names on all of the latter individuals. Phylogenetic tree- and genetic distance–based methods consistently grouped females with males for nine recognised species. Two separate L. ferruginea (Walker, 1852) operational taxonomic units were recognised, albeit with low divergence values, and an additional undetermined Leuctra was identified based solely on females. Digital stereomicroscope images were taken from females of each species unit to identify variation among and between species. This approach allowed for a more robust assessment of regional biogeographic patterns.