Miracle-dz/UniFAR-FTUs huggingface dataset:
Perspectives on Barriers to Eating Healthy Among Food Pantry Clients

Abstract Objective: To explore perspectives on barriers of eating healthy among food pantry clients. Methods: Food pantry clients participated in focus groups/interviews. Qualitative data were coded and analyzed using content analyses and grounded theory approach. Themes were then identified. Quantitative data were analyzed for frequencies and descriptives. Results: Fifty-four clients from 10 pantries participated in interviews/focus groups and completed questionnaires. Two major themes emerged: concern over obesity and other chronic diseases, and barriers to healthy eating. Several subthemes for barriers to healthy eating were identified: financial uncertainty, cost of healthy foods, lack of time, rationing food within family, lack of transportation, lack of adequate kitchen equipment, lack of nutrition knowledge and skills, and social support network. Conclusions: Issues identified above and those identified by others working with low-income populations need to be systematically addressed and incorporated into programs and nutrition education interventions for this group.
Polymeric Nanocarriers for the Delivery of Antimalarials

Malaria is an infectious disease caused by a protozoan parasite which is transmitted by female Anopheles mosquitoes around tropical and sub-tropical regions. Half of the world’s population is at risk of being infected by malaria. This mainly includes children, pregnant women and people living with chronic diseases. The main factor that has contributed to the spread of this disease is the increase in the number of drug-resistant parasites. To overcome drug resistance, researchers have developed drug delivery systems from biodegradable polymers for the loading of antimalarials. The drug delivery systems were characterized by distinct features such as good biocompatibility, high percentage drug encapsulation, reduced drug toxicity and targeted drug delivery. In this review article, we highlight the various types of drug delivery systems developed from polymeric nanocarriers used for the delivery of antimalarials.
Prevalence and Characteristics of Atypical Periprosthetic Femoral Fractures

Bisphosphonate use has been associated with atypical femoral fractures (AFFs), defined by the American Society of Bone and Mineral Research (ASBMR) Task Force criteria, which currently exclude periprosthetic fractures. The objectives of this study were to establish the prevalence of atypical periprosthetic femoral fractures (APFFs) in patients with hip and knee arthroplasties and to determine the clinical and radiological risk factors associated with these fractures. We performed a retrospective radiological review of all femoral fractures between January 1, 2006, and March 31, 2015, in Quebec City, Canada. Patients who sustained a periprosthetic femoral fracture (PFF) were identified and included in this study. We used the ASBMR Task Force criteria to identify atypical fractures and establish their prevalence. Data from medical records and radiological assessments of the femoral anatomy, the characteristics of the fracture, and the positioning of the prosthesis were collected. The prevalence of APFFs among PFFs was 8.3% (11/133). A strong association with bisphosphonates (p = 0.007) was observed, as well as an increased risk of APFFs among alendronate users compared to risedronate users (p = 0.04). A transverse fracture (p < 0.0001), a periosteal thickening of the lateral cortex at the fracture (p < 0.0001), a unicortical fracture (p = 0.02), and prodromal symptoms (p = 0.03) were associated with APFFs. The type of implant, its positioning, and the femoral geometry did not appear to be risk factors for APFFs compared to PFFs. © 2018 American Society for Bone and Mineral Research.
Improved robustness to adversarial examples using Lipschitz regularization of the loss

We augment adversarial training (AT) with worst case adversarial training (WCAT) which improves adversarial robustness by 11% over the current state-of-the-art result in the $\ell_2$ norm on CIFAR-10. We obtain verifiable average case and worst case robustness guarantees, based on the expected and maximum values of the norm of the gradient of the loss. We interpret adversarial training as Total Variation Regularization, which is a fundamental tool in mathematical image processing, and WCAT as Lipschitz regularization.
Paternal race/ethnicity and risk of adverse birth outcomes in the United States, 1989–2013

Objectives Investigate adverse birth outcomes in the United States (US) from 1989–2013 in relation to paternal and maternal race/ethnicity. Design We used US natality data for singleton births to women 15–44 with information on birthweight, gestational age, and covariates (n = 90,771,339). We calculated unadjusted and adjusted probabilities of preterm birth (PTB, < 37 weeks gestation) and small for gestational age (SGA, < 10th percentile) among all combinations of maternal and paternal race/ethnicity: non-Hispanic black (NHB), non-Hispanic white (NHW), Hispanic, and Asian, and where paternal race/ethnicity was missing. Results Missing, followed by NHB, paternal race/ethnicity had the two highest risks of PTB within each maternal racial/ethnic group. Asian, followed by NHW, paternal race/ethnicity had the two lowest risks of PTB. For SGA, however, Asian, followed by missing, paternal race/ethnicity had the two highest risks, and NHW race/ethnicity had the lowest risk. Our findings also demonstrate effect modification on the additive scale, with missing and NHB paternal race/ethnicity conferring a larger increase in risk of PTB for NHB women compared to women of other race/ethnicity groups. Conclusions These data confirm US disparities in adverse birth outcomes by maternal and paternal race/ethnicity and argue for increased resources and interventions in response.
Haemolysin Sph2 of Leptospira interrogans induces cell apoptosis via intracellular reactive oxygen species elevation and mitochondrial membrane injury

Leptospira interrogans causes widespread leptospirosis in humans and animals, with major symptoms of jaundice and haemorrhage. Sph2, a member of the sphingomyelinase haemolysins, is an important virulence factor for leptospire. In this study, the function and mechanism of Sph2 in the pathogenesis of leptospirosis were investigated to further understand the pathogenesis of leptospire. Real‐time PCR analysis of expression levels during cell invasion showed that sph2 gene expression was transiently induced in human umbilical vein endothelial cells (HUVECs), human embryo liver cells (L02), and human epithelial lung cells (L132), with expression levels reaching a peak after 45 min of infection. Further functional analysis of recombinant Sph2 (rSph2) by LDH assays and confocal microscopy showed that rSph2 can be internalised by cells both by causing cell membrane damage and by a damage‐independent clathrin‐mediated endocytosis pathway. Subsequently, rSph2 is able to translocate to mitochondria, which led to an increase in the levels of reactive oxygen species (ROS) and a decrease of the mitochondrial membrane potential (ΔΨm). Further flowcytometry analyses after rSph2 exposure showed that 28.7%, 31%, and 27.3% of the HUVEC, L02, and L132 cells, respectively, became apoptotic. Because apoptosis could be decreased with the ROS inhibitor N‐acetyl cysteine, these experiments suggested that rSph2 triggers apoptosis through mitochondrial membrane damage and ROS elevation. The ability of leptospiral haemolysin rSph2 to cause apoptosis likely contributes to the pathogenesis of leptospirosis.
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

Random Matrix Theory (RMT) is applied to analyze weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly indicate that the DNN training process itself implicitly implements a form of Self-Regularization. The empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of explicit regularization. Building on relatively recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization. These phases can be observed during the training process as well as in the final learned DNNs. For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a "size scale" separating signal from noise. For state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems. This results from correlations arising at all size scales, which arises implicitly due to the training process itself. This implicit Self-Regularization can depend strongly on the many knobs of the training process. By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size. This demonstrates that---all else being equal---DNN optimization with larger batch sizes leads to less-well implicitly-regularized models, and it provides an explanation for the generalization gap phenomena.
Multi-Scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets

Recently, Convolution Neural Networks (CNNs) obtained huge success in numerous vision tasks. In particular, DenseNets have demonstrated that feature reuse via dense skip connections can effectively alleviate the difficulty of training very deep networks and that reusing features generated by the initial layers in all subsequent layers has strong impact on performance. To feed even richer information into the network, a novel adaptive Multi-scale Convolution Aggregation module is presented in this paper. Composed of layers for multi-scale convolutions, trainable cross-scale aggregation, maxout, and concatenation, this module is highly non-linear and can boost the accuracy of DenseNet while using much fewer parameters. In addition, due to high model complexity, the network with extremely dense feature reuse is prone to overfitting. To address this problem, a regularization method named Stochastic Feature Reuse is also presented. Through randomly dropping a set of feature maps to be reused for each mini-batch during the training phase, this regularization method reduces training costs and prevents co-adaptation. Experimental results on CIFAR-10, CIFAR-100 and SVHN benchmarks demonstrated the effectiveness of the proposed methods.
Down-regulation of miR-1236-3p is correlated with clinical progression and unfavorable prognosis in gastric cancer.

OBJECTIVE MiRNAs have been considered to participate in many processes of various cancers, including gastric cancer (GC). However, the significance of miRNAs in the progression prognosis of GC is largely unknown. The goal of this study was to determine the expression level of miR-1236-3p in GC and its clinical association. PATIENTS AND METHODS Clinical specimens from GC patients were obtained to quantify the expression level of miR-1236-3p using quantitative Real-time PCR. The correlation between the miR-1236-3p levels and the clinicopathological factors of the GC patients was analyzed. The association between miR-1236-3p expression and overall survival was estimated by the Kaplan-Meier method. The significance of different variables with respect to survival was analyzed using the univariate and multivariate Cox proportional hazards model. RESULTS We found that miR-1236-3p was significantly downregulated in GC tissues compared to non-tumor gastric tissues (p < 0.01). The levels of miR-1236-3p expression were associated significantly with clinical stage (p = 0.002), lymph node metastasis (p = 0.000) and distant metastasis (p = 0.017). Kaplan-Meier survival analysis showed that patients in the high miR-1236-3p expression group had better overall survival than those in the low miR-1236-3p expression group (p = 0.0039). Moreover, we confirmed that miR-1236-3p was an independent poor prognostic factor for GC patients through univariate and multivariate analysis. CONCLUSIONS Our findings provide evidence that miR-1236-3p expression is frequently decreased in GC tissues, and its low expression may be a significant prognostic factor for poor survival in GC patients.
Effect of changes in the fractal structure of a littoral zone in the course of lake succession on the abundance, body size sequence and biomass of beetles

Dystrophic lakes undergo natural disharmonic succession, in the course of which an increasingly complex and diverse, mosaic-like pattern of habitats evolves. In the final seral stage, the most important role is played by a spreading Sphagnum mat, which gradually reduces the lake’s open water surface area. Long-term transformations in the primary structure of lakes cause changes in the structure of lake-dwelling fauna assemblages. Knowledge of the succession mechanisms in lake fauna is essential for proper lake management. The use of fractal concepts helps to explain the character of fauna in relation to other aspects of the changing complexity of habitats. Our 12-year-long study into the succession of water beetles has covered habitats of 40 selected lakes which are diverse in terms of the fractal dimension. The taxonomic diversity and density of lake beetles increase parallel to an increase in the fractal dimension. An in-depth analysis of the fractal structure proved to be helpful in explaining the directional changes in fauna induced by the natural succession of lakes. Negative correlations appear between the body size and abundance. An increase in the density of beetles within the higher dimension fractals is counterbalanced by a change in the size of individual organisms. As a result, the biomass is constant, regardless of the fractal dimension.
Differences in Speed and Power Capacities Between Female National College Team and National Olympic Team Handball Athletes

Abstract The aim of this study was to compare and examine differences in several neuromuscular assessments between female national Olympic team (Rio-2016) and national college team handball players (2015-Gwangju Summer Universiade). Twenty-eight elite female handball players of the national Brazilian Olympic (n = 12) and college (n = 16) teams participated in this study. The Olympic and college athletes performed the following speed-power tests assessing mean propulsive power (MPP) in loaded jump squat (JS) and bench press (BP) exercises, unloaded squat and countermovement jumps (SJ and CMJ), sprint performance over 5-, 10-, and 20-m, and change of direction ability in a standard Zig-zag test and a T-Test. The differences between Olympic and college team performances in all variables were analyzed using the magnitude-based inference. The Olympic group presented likely higher performances in the SJ, CMJ, and MPP JS and very likely higher performances in the MPP BP and T-Test than the college group. The differences in the linear sprint velocity in 5-, 10-, and 20-m tests as well as in the Zig-zag test were all rated as unclear. These findings may have substantial implications for the development of effective strength-power training and testing strategies in elite handball. In addition, coaches and researchers can use these data to create efficient talent identification programs for youth handball players.
Deterministic Policy Gradients With General State Transitions

We study a reinforcement learning setting, where the state transition function is a convex combination of a stochastic continuous function and a deterministic function. Such a setting generalizes the widely-studied stochastic state transition setting, namely the setting of deterministic policy gradient (DPG). We firstly give a simple example to illustrate that the deterministic policy gradient may be infinite under deterministic state transitions, and introduce a theoretical technique to prove the existence of the policy gradient in this generalized setting. Using this technique, we prove that the deterministic policy gradient indeed exists for a certain set of discount factors, and further prove two conditions that guarantee the existence for all discount factors. We then derive a closed form of the policy gradient whenever exists. Furthermore, to overcome the challenge of high sample complexity of DPG in this setting, we propose the Generalized Deterministic Policy Gradient (GDPG) algorithm. The main innovation of the algorithm is a new method of applying model-based techniques to the model-free algorithm, the deep deterministic policy gradient algorithm (DDPG). GDPG optimize the long-term rewards of the model-based augmented MDP subject to a constraint that the long-rewards of the MDP is less than the original one. We finally conduct extensive experiments comparing GDPG with state-of-the-art methods and the direct model-based extension method of DDPG on several standard continuous control benchmarks. Results demonstrate that GDPG substantially outperforms DDPG, the model-based extension of DDPG and other baselines in terms of both convergence and long-term rewards in most environments.
Modeling three-dimensional forest structures to drive canopy radiative transfer simulations of bidirectional reflectance factor

ABSTRACT Three-dimensional (3-D) Monte Carlo-based radiative transfer (MCRT) models are usually used for benchmarking in intercomparisons of the canopy radiative transfer (RT) simulations. However, the 3-D MCRT models are rarely applied to develop remote sensing algorithms to estimate essential climate variables of forests, due mainly to the difficulties in obtaining realistic stand structures for different forest biomes over regional to global scales. Fortunately, some of important tree structure parameters such as canopy height and tree density distribution have been available globally. This enables to run the intermediate complexities of the 3-D MCRT models. We consequently developed a statistical approach to generate forest structures with intermediate complexities depending on the inputs of canopy height and tree density. It aims at facilitating applications of the 3-D MCRT models to develop remote sensing retrieval algorithms. The proposed approach was evaluated using field measurements of two boreal forest stands at Estonia and USA, respectively. Results demonstrated that the simulations of bidirectional reflectance factor (BRF) based on the measured forest structures agreed well with the BRF based on the generated structures from the proposed approach with the root mean square error (RMSE) and relative RMSE (rRMSE) ranging from 0.002 to 0.006 and from 0.7% to 19.8%, respectively. Comparison of the computed BRF with corresponding MODIS reflectance data yielded RMSE and rRMSE lower than 0.03 and 20%, respectively. Although the results from the current study are limited in two boreal forest stands, our approach has the potential to generate stand structures for different forest biomes.
Factors Associated with Human Papillomavirus Vaccination Among Diverse Adolescents in a Region with Low Human Papillomavirus Vaccination Rates

Abstract Introduction: This study assesses the sociodemographic facilitators and barriers to human papillomavirus (HPV) vaccination for diverse teens in a region with low HPV vaccination rates. Materials and Methods: In this community-based participatory research study, we surveyed adult family members of teens aged 11–17 years from African American, African refugee, American Indian/Alaskan Native, Hispanic/Latino, and Native Hawaiian/Pacific Islander community groups in Salt Lake City, Utah. Bivariate analyses assessed associations between sociodemographic characteristics and, respectively, HPV vaccine receipt and intentions for vaccination. Barriers to vaccination were also investigated. Results: Only 20% of participants had vaccinated at least one of their children with at least one dose of the HPV vaccine. HPV vaccination was significantly related to caregiver age (p=0.035), race/ethnicity (p=0.001), educational attainment (p=0.006), annual household income (p=0.0454), years in the United States (p=0.023), and caregiver parent birthplace (p=0.008). Among caregivers with unvaccinated children, intention to vaccinate was significantly related to race/ethnicity (p=0.048 for daughters; p=0.003 for sons), caregiver parent birthplace (p=0.023 for sons), health insurance coverage (p=0.028 for daughters; p=0.047 for sons), and type of health insurance coverage (p=0.008 for sons). The most frequently cited barriers to HPV vaccination were lack of knowledge about the HPV vaccine, costs, side effects, and child not being sexually active. Conclusions: Our results show substantially lower HPV vaccine coverage than both national and state rates, signaling the urgent need for multipronged HPV vaccination interventions within these communities; strategies are discussed.
Meat, Meat Products and Seafood as Sources of Energy and Nutrients in the Average Polish Diet

The aim of this study was to identify the share of meat, meat products and seafood in the contribution of energy and 22 nutrients to the average Polish diet. Data from the nationally representative sample of Polish population (2016 Household Budget Survey) on meat and seafood product consumption from 38,886 households (n = 99,230) were calculated into one person per month. The analyses were conducted for seven food groups (e.g., red meat, poultry) and 16 products (e.g., beef, chicken). Approximately 18.5% of energy is delivered from the sources such as meat, meat products and seafood, providing a higher percentage of 18 nutrients to the diet (e.g., 56.0% of vitamin B12, 52.3% of niacin, 44.9% of cholesterol, 41.5% of protein, 41.4%of vitamin D, 37.6% of monounsaturated fatty acids (MUFA), 37.4% of thiamin, 33.8% of zinc, 32.0% of total fats, 30.3% of saturated fatty acids (SFA), 29.6% of vitamin B6, 25.3% of riboflavin, 24.9% of phosphorus, 24.8% of iron, 22.5% of vitamin A, 21.6% of polyunsaturated fatty acids (PUFA) and 20.3% of sodium). For the contribution of 18 nutrients and energy, processed meat products were ranked first. These results should be taken into consideration in order to compose diets with adequate energy and nutrient contribution and also to analyze benefits and risk resulting from the current level of consumption of red and processed meat, fish and other seafood.