Miracle-dz/UniFAR-FTUs huggingface dataset:
Relations Between the Use of Electronic Health and the Use of General Practitioner and Somatic Specialist Visits in Patients With Type 1 Diabetes: Cross-Sectional Study

Background The prevalence of diabetes and the use of electronic health (eHealth) are increasing. People with diabetes need frequent monitoring and follow-up of health parameters, and eHealth services can be of great value. However, little is known about the association between the use of eHealth and provider-based health care services among people with diabetes. Objective The objective of this study was to investigate the use of 4 different eHealth platforms (apps, search engines, video services, and social media sites) and associations with the use of provider-based health care visits among people diagnosed with type 1 diabetes mellitus (T1DM). Methods We used email survey data collected from 1250 members of the Norwegian Diabetes Association (aged 18 to 89 years) in 2018. Eligible for analyses were the 523 respondents with T1DM. Using descriptive statistics, we estimated the use of eHealth and the use of general practitioners (GPs) and somatic specialist outpatient services. By logistic regressions, we studied the associations between the use of these provider-based health services and the use of eHealth, adjusted for gender, age, education, and self-rated health. Results Of the sample of 523 people with T1DM, 90.7% (441/486) had visited a GP once or more, and 61.0% (289/474) had visited specialist services during the previous year. Internet search engines (such as Google) were used for health purposes sometimes or often by 84.0% (431/513), apps by 55.4% (285/514), social media (such as Facebook) by 45.2% (232/513), and video services (such as YouTube) by 23.3% (118/506). Participants aged from 18 to 39 years used all forms of eHealth more than people aged 40 years and older, with the exception of social media. The use of search engines was positively associated with the use of somatic specialist services (odds ratio 2.43, 95% CI 1.33-4.45). GP visits were not associated with any kind of eHealth use. Conclusions eHealth services are now widely used for health support and health information by people with T1DM, primarily in the form of search engines but often in the form of apps and social media as well. We found a positive association between the use of search engines and specialist visits and that people with T1DM are frequent users of eHealth, GPs, and specialist services. We found no evidence that eHealth reduces the use of provider-based health care; these services seem to be additional rather than alternative. Future research should focus on how health care services can meet and adapt to the high prevalence of eHealth use. Our results also indicate that many patients with T1DM do not visit specialist clinics once a year as recommended. This raises questions about collaboration in health care services and needs to be followed up in future research.
A plant proteinase inhibitor from Enterolobium contortisiliquum attenuates airway hyperresponsiveness, inflammation and remodeling in a mouse model of asthma.

INTRODUCTION Proteinase inhibitors have been associated with anti-inflammatory and antioxidant activities and may represent a potential therapeutic treatment for asthma. PURPOSE The aim of the present study was to evaluate the effects of Enterolobium contortisiliquum trypsin inhibitor (EcTI) on pulmonary mechanical function, eosinophilic recruitment, inflammatory cytokines, remodeling and oxidative stress in an experimental model of chronic allergic pulmonary inflammation. METHODS BALB/c mice were divided into 4 groups: C (saline i.p and inhalations with saline), OVA (ovalbumin i.p and inhalations with ovalbumin); C+EC (saline i.p, inhalations with s aline and treatment with EcTI); OVA+EC (ovalbumin i.p, inhalations with ovalbumin and treatment with EcTI). On day 29, we performed the following tests: resistance (Rrs) and elastance (Ers) of the respiratory system; (b) quantify eosinophils, 8-ISO-PGF2α, collagen and elastic fiber volume fractions; (c) IFN-γ, IL-4, IL-5, IL-13, MMP-9, TIMP-1, TGF-β, iNOS and p65-NFκB-positive cells in the airway and alveolar walls. RESULTS In OVA+EC group, there was an attenuation of the Rrs and Ers, reduction of eosinophils, IL-4, IL-5, IL-13, IFN-γ, iNOS and p65-NFκB-positive cells compared to OVA group. The 8-ISO-PGF2α, elastic and collagen fibers volume fractions as well as the positive cells for MMP-9, TIMP-1 and TGF-β positive cells were decreased in OVA+EC compared to the OVA group. CONCLUSION EcTI attenuates bronchial hyperresponsiveness, inflammation, remodeling and oxidative stress activation in this experimental mouse model of asthma.
Real-Time Demonstration of a mHealth App Designed to Reduce College Students Hazardous Drinking

Heavy alcohol use is a serious health issue in the United States with consequences such as illness, injury, and death. College students are among the most vulnerable to problems associated with risky drinking. This demographic is known as the Net Generation because members have grown up with digital technologies such as smartphones and apps. Thus, mobile health (mHealth) applications, successful in the delivery of health information and interventions to tech-savvy individuals, are a promising means of reaching them. To that end, we developed a smartphone application (SmarTrek) that targets college students and aims to reduce risky alcohol use. SmarTrek features are easy to use and have interactive components including text messages that incorporate motivational interviewing and ecological momentary interventions. We conducted iterative theater testing, field testing, and focus groups to evaluate the acceptability of SmarTrek with college students. We identified salient issues that might arise from SmarTrek use and modified the app based on feedback from participants. Participants were assigned to 2 groups (Group 1 [n = 4] and Group 2 [n = 6]). At baseline, participants completed 2 standardized surveys. Following their field testing, each group of participants was then invited to a focus group session of the app in which they provided feedback. The majority of participants (90%) agreed that SmarTrek was easy to use and that the information provided was useful and had a positive effect on decreasing their drinking.
What the diabetes data in the FOURIER trial fail to show

The FOURIER trial was set up to study the clinical outcomes of evolocumab in patients with hypercholesterolemia and a history of cardiovascular disease that use statins. Diabetes mellitus was an outcome of particular interest, because it is a known serious adverse effect of statins [1]. This article is protected by copyright. All rights reserved.
Phase Averaging on Square Cylinder Wake Based on Wavelet Analysis

Phase average techniques based on wavelet multiresolution analysis and continuous wavelet transform are developed to reveal the phase-averaged features of square cylinder wake measured by high-speed PIV. The multi-scale turbulent structures are phase-sorted to give phase-averaged representations of flow field. The phase-averaged measured flow fields suggest that the wake flow rolls up and down and is conveyed downstream together with the corresponding vortices, forming a vortex pair with opposite sense of rotation. The phase-averaged vorticity contours of large-scale flow structures show good correspondence to the topology of phase-averaged measured flow field, suggesting the alternative nature of the vortex street with strong periodicity. The phase averaged intermediate-scale structures tend to be conveyed downstream along streamwise direction, with the rotation sense vary from the first half period to the last half period, implying the nature of Kelvin-Helmholtz vortex.
Low Cost Energy Efficient Smart Security System with Information Stamping for IoT Networks

A security system with CCTV and other network nodes in IoT deals with large amount of data, so the need for devices with larger storage space comes into the picture which is a bit costlier. Traditional security systems are more power consuming as it has to record for 24x7 with more throughput but less efficient. Thus need human in middle to upload into cloud is needed. In this work, we propose a low-cost energy efficient smart security system for CCTV with other network nodes. Whenever motion is detected by passive infrared (PIR) sensor, at that time only the camera and all other security sensor nodes in network are activated (Switched on), the captured video and information (sensor data) are stamped on the image using image processing techniques on Python platform. It will be stored in a local storage device, after certain threshold (based on sensitivity of the location) data will be uploaded into the cloud along with data log created during processing stage by Python programming model. In this way, we can reduce the amount of data to be stored (as no recording when idle), consumed power (as device is switched off when idle), maintenance cost(as fully programmed). No need for human in middle for uploading data into the cloud (as program will upload data in to cloud). Because of time, date and information (data from sensor nodes) stamping on each frame of video using python programming model video access becomes easier, as data log will be created.
Design and Development of a MEMS Stacked Thermoelectric Microwatt Generator

In this paper, a MEMS thermoelectric generator (TEG) has been designed using n and p doped poly-silicon. It can be used in energy scavenging applications, such as in human body worn sensors and hot spots in Integrated Circuits (ICs). The work presented is novel in terms of vertical stacking of thermocouples resulting in two power sources and lower area occupied by a single thermocouple, unlike conventional structures where they are connected in series on a single plane. This is possible by designing a structure which has three alternatively doped poly-silicon layers separated by insulating oxide. Finite Element Method (FEM) simulations have been used to simulate the performance of the MEMS device, which indicates an output voltage of 0.71 volt and power generation of 8 micro-watts for a temperature gradient of 20 Kelvin.
Evaluating Machine Learning Varieties for NBA Players' Winning Contribution

The NBA brand has been spreading all over the world in recent years. As the league concerns a lot of money and fans, several of researches have been trying to predict its results and winning teams. Through its history, a lot of data and statistics have been collected for every player and going to enrich more details because NBA is developing more than ever. Hence, a big data issue for this kind of predictions deserves to have attention and effort on it. With the availability of such enormous data, it is still complicated to analyze and predict the outcome of match. The study chooses different approaches such as support vector machine regression, polynomial regression and random forest regression for a comparative analysis to discover how individual player's performance influences the team winning rate. The comparison results show that the learning techniques we have adopted exhibit competitive capability in prediction, and give quite consistent performance regardless of complexity in input data features.
A Navigation Ranging Scheme With True Random Entangled Microwave Signals

Focusing on the problem of vulnerable anti-deception and weak anti-jamming ability in classical pseudorandom code correlation ranging, we present a navigation ranging scheme with true random entangled microwave signals. One path of the entangled microwave signals uses for transmitting signal and another path of the signals preserves at locality. Firstly, the target that possesses the function of quantum repeater receives transmitting signal and forwards it to the locality. Then, correlation detection is implemented between the forwarded signal and the local delayed signal. Finally, the target distance can be calculated by delay measurement. We analyze the ranging precision and anti-deception ability. Results indicate that the scheme not only is superior to classical code correlation ranging precision, but also has very strong anti-multipath ability and anti-multipath interference ability, which will enhance the security of the navigation ranging system. Our investigation has significant application prospects.
Future directions in meditation research: Recommendations for expanding the field of contemplative science

The science of meditation has grown tremendously in the last two decades. Most studies have focused on evaluating the clinical effectiveness of mindfulness-based interventions, neural and other physiological correlates of meditation, and individual cognitive and emotional aspects of meditation. Far less research has been conducted on more challenging domains to measure, such as group and relational, transpersonal and mystical, and difficult aspects of meditation; anomalous or extraordinary phenomena related to meditation; and post-conventional stages of development associated with meditation. However, these components of meditation may be crucial to people’s psychological and spiritual development, could represent important mediators and/or mechanisms by which meditation confers benefits, and could themselves be important outcomes of meditation practices. In addition, since large numbers of novices are being introduced to meditation, it is helpful to investigate experiences they may encounter that are not well understood. Over the last four years, a task force of meditation researchers and teachers met regularly to develop recommendations for expanding the current meditation research field to include these important yet often neglected topics. These meetings led to a cross-sectional online survey to investigate the prevalence of a wide range of experiences in 1120 meditators. Results show that the majority of respondents report having had many of these anomalous and extraordinary experiences. While some of the topics are potentially controversial, they can be subjected to rigorous scientific investigation. These arenas represent largely uncharted scientific terrain and provide excellent opportunities for both new and experienced researchers. We provide suggestions for future directions, with accompanying online materials to encourage such research.
[Data on prevalence of metabolic syndrome in Turkey: Systematic review, meta-analysis and meta-regression of epidemiological studies on cardiovascular risk factors].

OBJECTIVE The incidence of metabolic syndrome varies greatly in developed and developing countries. In the last 10 to 15 years, important studies have been performed examining the incidence of metabolic syndrome in Turkey. This article is a meta-analysis of the studies that investigated the incidence of metabolic syndrome this country. METHODS Epidemiological studies from the last 15 years that provided information about the incidence of metabolic syndrome in Turkey were screened in the Ovid Medline, the Web of Science Core Collection, and the Turkish Academic Network and Information Center (ULAKBIM) databases. In addition, web pages of the Ministry of Health, the Turkish Statistical Institute, and the associations of the related medical discipilnes were screened for appropriate studies. A bias tool that is developed by our team was used to categorize the included studies as having low- or high risk of bias. A random effects model was used in the meta-analysis. Linear and nonlinear meta-regression analyses were performed to evaluate heterogeneity and temporal variations in prevalence rates. RESULTS Four studies (n=34893) were identified that provided information about the status of metabolic syndrome in Turkey using the Adult Treatment Panel III definition. There was excellent interobserver agreement regarding the selection of studies and the scoring of the selected research with respect to bias. The metaanalysis revealed a prevalence of 32.9% (95% confidence interval [CI]: 30.2-35) overall, 38.3% (95% CI: 35.8-40.8) in women, and 26.8% (95% CI: 23.4-30.3) in men. Two studies (n=8568) provided data according to the International Diabetes Federation definition of metabolic syndrome. The random effects model used in the metaanalysis of these studies demonstrated a prevalence of metabolic syndrome of 43.3% (95% CI: 41.9-44.7) overall, 50.4% (95% CI: 49.0-51.9) in women, and 35.4% (95% CI: 32.5-38.5) in men. CONCLUSION In Turkey, the incidence of metabolic syndrome is 1 in every 4 men, and 1 in every 3 women. The rate is particularly high among women. Studies designed to prevent the development of metabolic syndrome are needed.
Simple but robust improvement in multivoxel pattern classification

Multivoxel pattern analysis (MVPA) typically begins with the estimation of single trial activation levels, and several studies have examined how different procedures for estimating single trial activity affect the ultimate classification accuracy of MVPA. Here we show that the currently preferred estimation procedures impart spurious shifts in run-level means that cause the estimated activities to be misaligned across runs. These shifts are caused by positive correlations between the means of different category activity estimates within the same scanner run. In other words, if the mean of the estimates for one type of trials is high (low) in a given scanner run, then the mean of the other type of trials is also high (low) for that same scanner run, and the mean across all trials therefore shifts from run to run. Simulations show that these correlations are unavoidable whenever there is a need to deconvolve overlapping trial activities in the presence of noise. We show that subtracting the mean across all trials of a run from all the estimates within that run (i.e., run-level mean centering of estimates), by cancelling out these mean shifts, leads to robust and significant improvements in MVPA classification accuracy. These improvements are seen in both simulated and real data across a wide variety of situations and can provide significant direct benefits with no computational cost. However, we also point out that there could be cases when mean activations are expected to shift across runs and that run-level mean centering could be detrimental in some of these cases (e.g., different proportion of trial types between different runs).
Practical Hybrid Graph-Based Formation Control Architecture for Leader-Follower Trajectory Tracking Problem

This paper extends the traditional single vehicle trajectory tracking problem to develop a control approach for a group of N-vehicles which are capable of converging to a desired formation while moving on a predefined trajectory. A novel hybrid control algorithm is introduced to handle the two aforementioned problems separately. The proposed controller integrates three different techniques; graph theory, Lyapunov stability and leader-follower approach. The controller structure consists of two main subsystems. In the first subsystem, a leader virtual robot is responsible for the trajectory tracking problem, actuated via a Lyapunov-based controller. Simultaneously, a graph-based controller is used to guarantee the convergence of the follower mobile robots to the desired formation using local positioning information; thus, ensuring the stability of the formation of the follower robots with respect to the moving leader. This new hybrid approach handles the problem of impractical velocities providing control inputs within acceptable ranges that can be practically implemented. Furthermore, it eliminates the oscillations in the motor inputs. Several scenarios are implemented to verify the proposed hybrid control algorithm. The simulations show significant and promising results which prove the effectiveness of this hybrid approach.
An Active Learning Method Using Clustering and Committee-Based Sample Selection for Sound Event Classification

This paper proposes an active learning method to control a labeling process for efficient annotation of acoustic training material, which is used for training sound event classifiers. The proposed method performs K-medoids clustering over an initially unlabeled dataset, and medoids as local representatives, are presented to an annotator for manual annotation. The annotated label on a medoid propagates to other samples in its cluster for label prediction. After annotating the medoids, the annotation continues to the unexamined sounds with mismatched prediction results from two classifiers, a nearest-neighbor classifier and a model-based classifier, both trained with annotated data. The annotation on the segments with mismatched predictions are ordered by the distance to the nearest annotated sample, farthest first. The evaluation is made on a public environmental sound dataset. The labels obtained through a labeling process controlled by the proposed method are used to train a classifier, using supervised learning. Only 20% of the data needs to be manually annotated with the proposed method, to achieve the accuracy with all the data annotated. In addition, the proposed method clearly outperforms other active learning algorithms proposed for sound event classification through all the experiments, simulating varying fraction of data that is manually labeled.
Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models

Abstract In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions.
Mode-Domain Spatial Active Noise Control Using Multiple Circular Arrays

Noise control and attenuation over a sizable space requires uniformly distributed microphones and loudspeakers, which limits the system's viability in practice. In this paper, we propose a mode-domain active noise control (ANC) system using a simple microphone and loudspeaker array structure. We introduce few circular microphone and loudspeaker arrays to first transform a sound field into circular expansion mode coefficients and then combine them to calculate 3D mode coefficients, which are then processed in an adaptive algorithm to attenuate an undesired noise field in 3D space. Experimental results indicate that our proposed method gives comparable noise attenuation performance compared to a conventional method, which uses an unfeasible array structure. Furthermore, the proposed method shows better noise attenuation performance than a conventional temporal frequency domain ANC system.