Miracle-dz/UniFAR-FTUs huggingface dataset:
Duration of obesity exposure between ages 10 and 40 years and its relationship with cardiometabolic disease risk factors: A cohort study

Background Individuals with obesity do not represent a homogeneous group in terms of cardiometabolic risk. Using 3 nationally representative British birth cohorts, we investigated whether the duration of obesity was related to heterogeneity in cardiometabolic risk. Methods and findings We used harmonised body mass index (BMI) and cardiometabolic disease risk factor data from 20,746 participants (49.1% male and 97.2% white British) enrolled in 3 British birth cohort studies: the 1946 National Survey of Health and Development (NSHD), the 1958 National Child Development Study (NCDS), and the 1970 British Cohort Study (BCS70). Within each cohort, individual life course BMI trajectories were created between 10 and 40 years of age, and from these, age of obesity onset, duration spent obese (range 0 to 30 years), and cumulative obesity severity were derived. Obesity duration was examined in relation to a number of cardiometabolic disease risk factors collected in mid-adulthood: systolic (SBP) and diastolic blood pressure (DBP), high-density-lipoprotein cholesterol (HDL-C), and glycated haemoglobin (HbA1c). A greater obesity duration was associated with worse values for all cardiometabolic disease risk factors. The strongest association with obesity duration was for HbA1c: HbA1c levels in those with obesity for <5 years were relatively higher by 5% (95% CI: 4, 6), compared with never obese, increasing to 20% (95% CI: 17, 23) higher in those with obesity for 20 to 30 years. When adjustment was made for obesity severity, the association with obesity duration was largely attenuated for SBP, DBP, and HDL-C. For HbA1c, however, the association with obesity duration persisted, independent of obesity severity. Due to pooling of 3 cohorts and thus the availability of only a limited number harmonised variables across cohorts, our models included adjustment for only a small number of potential confounding variables, meaning there is a possibility of residual confounding. Conclusions Given that the obesity epidemic is characterised by a much earlier onset of obesity and consequently a greater lifetime exposure, our findings suggest that health policy recommendations aimed at preventing early obesity onset, and therefore reducing lifetime exposure, may help reduce the risk of diabetes, independently of obesity severity. However, to test the robustness of our observed associations, triangulation of evidence from different epidemiological approaches (e.g., mendelian randomization and negative control studies) should be obtained.
Involvement of Workers in Closed and Semiclosed Institutions in Outbreaks of Acute Gastroenteritis Due to Norovirus

Norovirus outbreaks frequently occur in closed or semiclosed institutions. Recent studies in Catalonia and various countries indicate that, during outbreaks in these institutions, norovirus is detected in between 23% and 60% of workers, and the prevalence of infection in asymptomatic workers involved in outbreaks ranges from 17% to 40%. In this work, we carried out a prospective study to investigate the involvement of workers in closed and semiclosed institutions during outbreaks. The attack rates (ARs) and the rate ratios (RRs) were calculated according to the type of transmission and occupational category. The RRs and 95% confidence intervals (CIs) between workers and users were calculated. The mean cycle of quantification (Cq) values were compared according to the genogroup and the presence of symptoms. ARs were higher in person-to-person transmission than in common vehicle outbreaks, and 38.8% of workers were symptomatic. The RR between workers and users was 0.46 (95% CI 0.41–0.52). The ARs in workers were high, particularly in workers with closer contact with users. The mean Cq was lower in patients than in asymptomatic infected persons, although the difference was only significant for genogroup I (GI). The frequency of asymptomatic infected persons suggests that personal hygiene measures should be followed by all workers in the centers affected.
Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.
Sarcopenia screening in elderly in primary health care: nurse knowledge and practices.

OBJECTIVE Describe the knowledge and practices of the Primary Health Care nurse on sarcopenia screening in the elderly. METHODS Qualitative study conducted with 24 Primary Health Care nurses. The data was collected through semi-structured interviews, recorded and later transcribed. The speeches were grouped in thematic categories, later analyzed, supported by Paulo Freire's reference. RESULTS The findings showed that the primary care nurses' knowledge of sarcopenia screening in the elderly was incipient and fragile. This reality is reflected in a gap in practice, although some instruments already require the registration of characteristics indicative of sarcopenia, such as the evaluation of the calf circumference. FINAL CONSIDERATIONS The need to train nurses to perform sarcopenia screening and to implement a promotional and preventive care plan, which will result in improving the quality of life of the elderly assisted in Primary Care, was highlighted.
Multitask Attention Network for Lane Detection and Fitting

Many CNN-based segmentation methods have been applied in lane marking detection recently and gain excellent success for a strong ability in modeling semantic information. Although the accuracy of lane line prediction is getting better and better, lane markings’ localization ability is relatively weak, especially when the lane marking point is remote. Traditional lane detection methods usually utilize highly specialized handcrafted features and carefully designed postprocessing to detect the lanes. However, these methods are based on strong assumptions and, thus, are prone to scalability. In this work, we propose a novel multitask method that: 1) integrates the ability to model semantic information of CNN and the strong localization ability provided by handcrafted features and 2) predicts the position of vanishing line. A novel lane fitting method based on vanishing line prediction is also proposed for sharp curves and nonflat road in this article. By integrating segmentation, specialized handcrafted features, and fitting, the accuracy of location and the convergence speed of networks are improved. Extensive experimental results on four-lane marking detection data sets show that our method achieves state-of-the-art performance.
Mental Health, Physical Activity, and Quality of Life of US Adolescent Athletes During COVID-19-Related School Closures and Sport Cancellations: A Study of 13 000 Athletes.

CONTEXT In the spring of 2020, US schools closed to in-person teaching and sports were cancelled to control the transmission of coronavirus disease 2019 (COVID-19). It is critical to understand the mental and physical health of adolescent athletes during this time. OBJECTIVE To describe the health of athletes during COVID-19-related school closures and sport cancellations. DESIGN Cross-sectional study. SETTING A national sample recruited via social media. PATIENTS OR OTHER PARTICIPANTS A total of 13 002 US adolescent athletes (age = 16.3 ± 1.2 years, females = 52.9%, males = 47.0%) completed an anonymous online survey. MAIN OUTCOME MEASURE(S) Demographic information collected was sex, grade, sport(s) played, and zip code. Assessments used were the General Anxiety Disorder 7-Item for anxiety, Patient Health Questionnaire 9-Item for depression, the Pediatric Functional Activity Brief Scale for physical activity, and the Pediatric Quality of Life Inventory 4.0 for quality of life. Mental health, physical activity, and health-related quality-of-life variables were compared among sex, grade, sport(s) played, and poverty level using means and 95% confidence intervals (CIs) from the survey-weighted analysis of variance. RESULTS Females reported a higher prevalence of moderate to severe anxiety symptoms (females = 43.7% versus males = 28.2%). The Pediatric Functional Activity Brief Scale score was highest (best) for grade 9 (mean = 14.5, 95% CI = 14.0, 15.0) and lowest for grade 11 (mean = 10.9, 95% CI = 10.5, 11.3. The prevalence of depression symptoms was highest in team sport (74.1%) and lowest in individual sport (64.9%) participants. The total Pediatric Quality of Life Inventory score was lowest (worst) for athletes from counties with the highest poverty levels (high: mean = 74.5, 95% CI = 73.7, 75.3; middle: mean = 78.9, 95% CI = 78.0, 79.8; and low: mean = 78.3, 95% CI = 77.4, 79.1). CONCLUSIONS The health of US adolescents during the COVID-19-related school closures and sport cancellations varied to differing degrees depending on sex, grade level, type of sport participation, and level of poverty. Health policy experts should consider these findings in the future when creating and implementing policies to improve the health of adolescents in the United States.
Tuberculosis risk in patients with rheumatologic disease treated with biologic drugs.

Introduction The risk of tuberculosis is higher in cases who have used antiTNF treatments. However, it is not clearly known whether there is a relationship between other biologic agents and the risk of developing tuberculosis or not. We aimed to investigate the prevalence of active tuberculosis among patients with rheumatic disease treated with biologic drugs. Materials and Methods The study was performed at a tertiary referral center from January 2015 to December 2019. A total of 2000 patients with rheumatic diseases were screened and 461 patients were enrolled in the study due to regular records. They were underwent LTBI screening tests and were followedup at least 1 year after TNF inhibitor treatment initiation. Result The median age of all patients was 48 (min-max: 19-80). 283 patients (61.3%) were female and 178 (38.7%) were male. The most common diseases were ankylosing spondylitis (67.2%), rheumatoid arthritis (26%) and psoriatic arthritis (5.2%). Anti-TNF treatments were given to 85.2% of all cases and other biologic treatments were given to 14.8%. Tuberculin skin test was applied to 429 patients and 70.4% positivity was found. Quantiferon-TB test was applied to 93 patients and 20.4% positivity was found. 320 patients were treated for LTBI due to positive tuberculin skin test and/or positive quantiferon-TB test. TB was developed in only one patient out of 393 patients who were treated with anti-TNF treatments and the the prevalence of TB development was found 255/100.000. Conclusions The incidence of tuberculosis was quite low in our patients with rheumatic disease who were receiving anti-TNF treatment compared to previous studies. Also, in patients who were using other biological treatments, no TB cases were developed.
Estimation and correction of matrix effects in gas chromatographic pesticide multiresidue analytical methods with a nitrogen–phosphorus detector

The assessment of matrix effects in the quantification of organophosphorus pesticides in fruit and vegetables by GC-NPD, were studied applying ANCOVA. Calibration curves prepared in solvent were compared with calibration curves prepared in a blank matrix extract for eight different commodities, establishing whether the matrix induces systematic or proportional errors in the quantification of the pesticides. In such cases correction functions were obtained and validated by quantifying spiked samples using solvent calibrations and applying the correction functions to the data obtained. The results were compared with those obtained by quantification using matrix-matching calibrations and with those from 100% recovery experiments. It was found that the matrix effects can be avoided using the correction functions. Finally the contribution of the correction functions to the uncertainty of the results was estimated as well as their stability during a four month period.
EFFECTS OF WATER DEFICIT STRESS ON ROOT AND SHOOT PHYSIOLOGICAL RESPONSES AND BIOMASS PARTITIONING IN KOCHIA (KOCHIA

Kochia (Kochia scoparia L. Schrad) is a tolerant plant to both saline and water deficit conditions and have recently considered as forage feed for livestock in saline and arid regions. Understanding the belowground physiological responses and biomass allocation of Kochia's under water deficit conditions may help to realize more potentials of this plant. To achieve such knowledge of root and shoot, a pot experiment was carried out in a completely randomized design with three replications. Nine water deficit stress treatments imposed to plants at the vegetative and reproductive stages: control; NS-NS (no stress= 100% field capacity (FC)) during total growth period, moderate stress (MS=70% FC) during total growth period (MS-MS), severe stress (SS=30% FC) during total growth period (SS-SS), MS-NS, SS-NS, NS-MS, NS-SS, MSSS, SS-MS. The results indicated that water deficit stress decreased relative water content, membrane stability, fresh and dry weight and increased malondialdehyde, superoxide dismutase, catalase, free amino acids, proline and soluble sugars in root and shoot of Kochia. Our results indicate that under water deficit stress, the amount of osmolytes and enzymatic antioxidants increased in root and shoot of Kochia to maintain osmotic balance and membrane stability. It is also found that water deficit stress severely decreased biomass of both roots and shoots, but allocation of photosynthates to shoots or roots was similar across all irrigation regimes. Such performance may explain Kochia scoparia tolerance to saline and water deficit conditions.
A Unified q-Memorization Framework for Asynchronous Stochastic Optimization

Asynchronous stochastic algorithms with various variance reduction techniques (such as SVRG, S2GD, SAGA and q-SAGA) are popular in solving large scale learning problems. Recently, Reddi et al. (2015) proposed an unified variance reduction framework (i.e., HSAG) to analyze the asynchronous stochastic gradient optimization. However, the HSAG framework cannot incorporate the S2GD technique, the analysis of the HSAG framework is limited to the SVRG and SAGA techniques on the smooth convex optimization. They did not analyze other important various variance techniques (e.g., S2GD and q-SAGA) and other important optimization problems (e.g., convex optimization with non-smooth regularization and non-convex optimization with cardinality constraint). In this paper, we bridge this gap by using an unified q-memorization framework for various variance reduction techniques (including SVRG, S2GD, SAGA, q-SAGA) to analyze asynchronous stochastic algorithms for three important optimization problems. Specifically, based on the q-memorization framework, 1) we propose an asynchronous stochastic gradient hard thresholding algorithm with q-memorization (AsySGHT-qM) for the non-convex optimization with cardinality constraint, and prove that the convergence rate of AsySGHT-qM before reaching the inherent error induced by gradient hard thresholding methods is geometric. 2) We propose an asynchronous stochastic proximal gradient algorithm (AsySPG-qM) for the convex optimization with non-smooth regularization, and prove that AsySPG-qM can achieve a linear convergence rate. 3) We propose an asynchronous stochastic gradient descent algorithm (AsySGD-qM) for the general non-convex optimization problem, and prove that AsySGD-qM can achieve a sublinear convergence rate to stationary points. The experimental results on various large-scale datasets confirm the fast convergence of our AsySGHT-qM, AsySPG-qM and AsySGD-qM through concrete realizations of SVRG and SAGA. ©2020 Bin Gu, Wenhan Xian, Zhouyuan Huo, Cheng Deng, and Heng Huang. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v21/18-434.html. Gu, Xian, Huo, Deng, and Huang
Meta-Regularized Deep Learning for Financial Forecasting

Meta-learning has shown great promise for machine learning domains in which problems can be formulated as distributions over sets of tasks. Supervised meta-learners struggle when tasks within a task distribution are not mutually exclusive, meaning the mapping from input to label for a task T i can be at least partially inferred from the same mapping for a different task T j . This issue gives rise to meta-overﬁtting , in which rather than adapting speciﬁcally to each individual task, the meta-learner memorizes these mappings and in effect reduces the problem to one of standard supervised learning with a single task. This can be combated with meta-regularization , which seeks to regularize the meta-learner without interfering with its learned task-speciﬁc adaptations. We explore various approaches to meta-regularization, comparing and contrasting their effectiveness at tackling the memorization problem compared to non-meta-regularized models in the domain of ﬁnancial market forecasting. This domain is well-suited to the meta-learning paradigm: different assets have fundamental differences in what affects their behavior that is exogenous to any concrete features we might choose to encode, but at the same time there is signiﬁcant shared structure between tasks when we deﬁne a task T i as the regression on an asset i ’s price one quarter in the future. We can then sample any number of training examples speciﬁc to i when we sample i from T . We show that instead of Gaussian noise on the meta-parameters, a learned L2-based penalty on the meta-parameters can have a meta-regularizing effect by limiting the expressivity of the meta-model without inhibiting in any way the adaptive power of the task-speciﬁc parameters. We show that MAML over a simple feedforward DNN can outperform a similar standard supervised network, and that meta-regularization on top of this MAML implementation can further improve performance. We experiment with different kinds of hand-tuned and learned meta-regularizers, and we see this model’s output, when trained on strictly fundamental data, can lead even a very simple trading model to
Cardiac computed tomography and myocardial perfusion scintigraphy for risk stratiﬁcation in asymptomatic individuals without known cardiovascular disease: a position statement of the Working Group on Nuclear Cardiology and Cardiac CT of the European Society of Cardiology

Cardiovascular events remain one of the most frequent causes of mortality and morbidity worldwide. The majority of cardiac events occur in individuals without known coronary artery disease (CAD) and in low- to intermediate-risk subjects. Thus, the development of improved preventive strategies may substantially beneﬁt from the identiﬁcation, among apparently intermediate-risk subjects, of those who have a high probability for developing future cardiac events. Cardiac computed tomography and myocardial perfusion scintigraphy (MPS) by single photon emission computed tomography may play a role in this setting. In fact, absence of coronary calcium in cardiac computed tomography and inducible ischaemia in MPS are associated with a very low rate of major cardiac events in the next 3–5 years. Based on current evidence, the evaluation of coronary calcium in primary prevention subjects should be considered in patients classiﬁed as intermediate-risk based on traditional risk factors, since high calcium scores identify subjects at high-risk who may beneﬁt from aggressive secondary prevention strategies. In addition, calcium scoring should be considered for asymptomatic type 2 diabetic patients without known CAD to select those in whom further functional testing by MPS or other stress imaging techniques may be considered to identify patients with signiﬁcant inducible ischaemia. From available data, the use of MPS as ﬁrst line testing modality for risk stratiﬁcation is not recommended in any category of primary prevention subjects with the possible exception of ﬁrst-degree relatives of patients with premature CAD in whom MPS may be considered. However, the Working Group recognizes that neither the use of computed tomography for calcium imaging nor of MPS have been proven to signiﬁcantly improve clinical outcomes of primary prevention subjects in prospective controlled studies. This information would be crucial to adequately deﬁne the role of imaging approaches in cardiovascular preventive strategies. -
EFFECT OF THE -POLYLYSINE UTILIZATION ON THE PHYSIOCHEMICAL, MICROBIOLOGICAL AND RHEOLOGICAL PROPERTIES OF THE DRINKING YOGURT

-Poly-L-lysine is a homopolymer linked by the peptide bond between the carboxylic and the epsilon amino group of adjacent lysine molecules. Post-acidification of drinking yogurt always occurs during cold storage causing short shelf life thus consumer unacceptability. The short storage life of particular drinking yogurt affects the consumption level as well. This study concentrated on the effect of preservatives on post-acidification of drinking yogurt. The drinking yogurt mixture was industry and inoculated with 2% starter culture containing Streptococcus thermophilus and Lactobacillus bulgaricus. After the required inoculation; -polylysinewas added separately at different concentrations. Titratable acidity, pH and viscosity of drinkig yogurts during cold storage for 20 days were examined. Evaluation of sensory properties was done for the best concentrations of each preservative. The pH values and titratable acidity were significantly lower (<0.05) in drinking yogurts with added preservatives than those in blank. The results showed that å-polylysine used at 0.007% (w/v) respectively gave the best (optimum) inhibition of post-acidification during refrigerated storage for 20 days. The sensory evaluation revealed that 0.007% (w/v) -polylysine was the most preferred preservative to consumers with respect to taste, colour, mouth feel, appearance and overall acceptability.