Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of the rs 1801282 single nucleotide polymorphism of the PPARG gene in patients with metabolic syndrome

Objective: This study aimed to get the genotypic and allelic frequencies of rs1801282 in 179 volunteer donors and 154 patients with Metabolic syndrome (MetS) in Brasilia, Brazil and also examine the association with anthropometric, biochemical and hemodynamic variables in the latter group. MetS comprises a group of diseases resulting from insulin resistance, in-creased risk of type 2 diabetes and atherosclerotic cardiovascular disease. MetS is defined by the presence of increased visceral fat, atherogenic dyslipidemia (elevated triglycerides (TGL)), with decreased high density lipoprotein (HDL) and increased low density lipoprotein (LDL) levels, hypertension (BPH) and disturbances in glucose homeostasis representing a significant burden across the world due to the alarming increase in the incidence over the last decades besides their significant morbidity and mortality. Peroxisome proliferator activated receptor-gamma (PPARg) has been mentioned as a candidate gene for determining the risk of MetS. It is a member of the nuclear receptors superfamily and a ligand-activated transcription factor, which regulates the expression of genes involved in the network lipogenesis and adipogenesis, insulin sensitivity, energy balance, inflammation, angiogenesis and atherosclerosis. Among the PPARG genetic variants, single nucleotide polymorphism rs1801282 has been the most extensively studied one since it was first described by Yen and cols. in 1997. This polymorphism is characterized by the replacement of a proline (CCC) to an alanine (GCA) at codon 12 of exon B, due to the exchange of a cytosine with a guanine. The Ala allele frequency varies in different ethnic groups. Materials and methods: DNA was extracted using Chelex-100 method and determinations of genotypes were performed by allele-specific chain reaction. Results: The distribution of genotype frequency of the MetS group was not statistically different from the frequency in the donor population at large. In the first group, genotype frequency was CC to 0.869 and 0.103 for CG, while allelic frequencies were 0.948 for C and 0.052 for G allele. In the group of donors, the genotype and allele frequencies were 0.882 for CC, 0.117 to CG; and 0.941 to 0.059 for G and C, respectively. GG genotype was not found in any of the two groups. The genotype distribution and allele frequencies were in Hardy-Weinberg equilibrium. No marker could be detected from the analysis of anthropometric, biochemical and hemodynamic variables in the MetS group. Conclusion: Our data suggest that this polymorphism is not correlated with predisposition to MetS. The results obtained on a small sample of the population of Brasilia, corroborate the data reported in the literature on the prevalence of this polymorphism in PPAR in populations of different ethnic origins. Arch Endocrinol Metab. 2015;59(4):297-302
Validity of a method for the self-screening of cardiovascular risk

Background The validity of a cardiovascular risk self-screening method was assessed. The results obtained for self-measurement of blood pressure, a point-of-care system’s assessment of lipid profile and glycated hemoglobin, and a self-administered questionnaire (sex, age, diabetes, tobacco consumption) were compared with the standard screening (gold standard) conducted by a health professional. Methods Crossover clinical trial on a population-based sample from Girona (north-eastern Spain), aged 35–74, with no cardiovascular disease at recruitment. Participants were randomized to one of the two risk assessment sequences (standard screening followed by self-screening or vice versa). Cardiovascular risk was estimated with the Framingham-REGICOR function. Concordance between methods was estimated with the intraclass correlation coefficient (ICC). Sensitivity, specificity, and positive and negative predictive values were estimated, considering 5% cardiovascular risk as the cutoff point. ClinicalTrials.gov Registration #NCT02373319. Clinical Research Ethic Committee of the Parc de Salut Mar Registration #2014/5815/I. Results The median cardiovascular risk in men was 2.56 (interquartile range: 1.42–4.35) estimated by standard methods and 2.25 (1.28–4.07) by self-screening with ICC=0.92 (95% CI: 0.90–0.93). In women, the cardiovascular risk was 1.14 (0.61–2.10) by standard methods and 1.10 (0.56–2.00) by self-screening, with ICC=0.89 (0.87–0.90). The sensitivity, specificity, and positive and negative predictive values for the self-screening method were 0.74 (0.63–0.82), 0.97 (0.95–0.99), 0.86 (0.77–0.93), and 0.94 (0.91–0.96), respectively, in men. In women, these values were 0.50 (0.30–0.70), 0.99 (0.98–1), 0.81 (0.54–0.96), and 0.97 (0.95–0.99), respectively. Conclusion The self-screening method for assessing cardiovascular risk provided similar results to the standard method. Self-screening had high clinical performance to rule out intermediate or high cardiovascular risk.
Stochastic Approximation for Risk-Aware Markov Decision Processes

We develop a stochastic approximation-type algorithm to solve finite state/action, infinite-horizon, risk-aware Markov decision processes. Our algorithm has two loops. The inner loop computes the risk by solving a stochastic saddle-point problem. The outer loop performs <inline-formula><tex-math notation="LaTeX">$Q-$</tex-math></inline-formula> learning to compute an optimal risk-aware policy. Several widely investigated risk measures (e.g., conditional value-at-risk, optimized certainty equivalent, and absolute semideviation) are covered by our algorithm. Almost sure convergence and the convergence rate of the algorithm are established. For an error tolerance <inline-formula><tex-math notation="LaTeX">$\epsilon >0$</tex-math></inline-formula> for optimal <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-value estimation gap and learning rate <inline-formula><tex-math notation="LaTeX">$k\in (1/2,\,1]$</tex-math></inline-formula>, the overall convergence rate of our algorithm is <inline-formula><tex-math notation="LaTeX">$\Omega ((\ln (1/\delta \epsilon)/\epsilon ^{2})^{1/k}+(\ln (1/\epsilon))^{1/(1-k)})$</tex-math></inline-formula> with probability at least <inline-formula><tex-math notation="LaTeX">$1-\delta$</tex-math></inline-formula>.
Characterization and Erosion Modeling of a Nozzle-Based Inflow-Control Device

In the petroleum industry, water-and-gas breakthrough in hydrocarbon reservoirs is a common issue that eventually leads to uneconomic production. To extend the economic production lifetime, inflow-control devices (ICDs) are designed to delay the water-and-gas breakthrough. Because the lifetime of a hydrocarbon reservoir commonly exceeds 20 years and it is a harsh environment, the reliability of the ICDs is vital. With computational fluid dynamics (CFD), an inclined nozzlebased ICD is characterized in terms of the Reynolds number, discharge coefficient, and geometric variations. The analysis shows that especially the nozzle edges affect the ICD flow characteristics. To apply the results, an equation for the discharge coefficient is proposed. The Lagrangian particle approach is used to further investigate the ICD. This allows for erosion modeling by injecting sand particles into the system. By altering the geometry and modeling several scenarios while analyzing the erosion in the nozzles and at the nozzle edges, an optimized design for incompressible media is found. With a filleted design and an erosion-resistant material, the mean erosion rate in the nozzles may be reduced by a factor of more than 2,500.
Understanding and Improving Deep Neural Network for Activity Recognition

Activity recognition has become a popular research branch in the field of pervasive computing in recent years. A large number of experiments can be obtained that activity sensor-based data's characteristic in activity recognition is variety, volume, and velocity. Deep learning technology, together with its various models, is one of the most effective ways of working on activity data. Nevertheless, there is no clear understanding of why it performs so well or how to make it more effective. In order to solve this problem, first, we applied convolution neural network on Human Activity Recognition Using Smart phones Data Set. Second, we realized the visualization of the sensor-based activity's data features extracted from the neural network. Then we had in-depth analysis of the visualization of features, explored the relationship between activity and features, and analyzed how Neural Networks identify activity based on these features. After that, we extracted the significant features related to the activities and sent the features to the DNN-based fusion model, which improved the classification rate to 96.1%. This is the first work to our knowledge that visualizes abstract sensor-based activity data features. Based on the results, the method proposed in the paper promises to realize the accurate classification of sensor- based activity recognition.
EARLY MODERN ENGLISH LEXIS AND SEMANTICS

Introduction Overview Despite the long life and stability of core vocabulary, the rate of language change is no doubt greatest in the lexicon. Lexical words differ from phonemes and grammatical morphemes in that they can be freely added to the existing stock. As we shall see in more detail below, the Early Modern English period is marked by an unprecedented lexical growth. It is achieved both by extensive borrowing from other languages and by exploiting native resources by means of word-formation. One of the most obvious differences between Old English and Present-Day English is the increase in borrowed lexis. According to one estimate, loan words take up a mere three per cent of the recorded vocabulary in Old English, but some seventy per cent or more in Present-Day English (Scheler 1977: 74). In Early Modern English their share varies between forty per cent and fifty per cent of the new vocabulary recorded (Wermser 1976: 40). This large-scale borrowing no doubt reflects both the various foreign contacts of the period and the growing demands made on the evolving standard language. This is the period in the history of English when for the first time the vernacular extends to practically all contexts of speech and writing. Borrowed lexis supplies new names for new concepts, but also increases synonymy in the language, thus providing alternative ways of saying the same thing in different registers. The means by which words are formed are increased by a number of new productive elements that owe their existence to borrowed lexis.
Cascaded Perforates as One-Dimensional, Bulk Absorbers

Porous cell honeycomb liners for aircraft engine nacelles offer the possibility of exploiting extended reaction effects to improve liner attenuation bandwidth as generally attributed to the performance of bulk absorbers. This paper describes an analytical procedure, starting with an impedance prediction model for a single perforated plate, to estimate the bulk-absorber parameters for a cascade of such perforates - a first step to modeling a porous wall honeycomb structure. The objective is to build confidence in a lumped element impedance model, when applied to a uniformly-spaced set of porous plates to predict its . bulk. absorber properties. The model is based upon a modified version of the two-parameter flow resistance model of the form A + BV(sub inc), where A and B are physics-based, semi-empirical parameters that are adjusted to provide an optimum fit to a composite dataset from three plate porosities of 2.5, 5 and 10%. The composite dataset is achieved by reformulating the two-parameter flow resistance model into a . reduced pressure drop coefficient. dependency on perforate hole Reynolds number. The resulting impedance model is employed to calculate surface impedance spectra for N and 2N-layer perforate cascades. The well-known two-thickness method for experimental determination of bulk-absorber parameters is then applied to these . synthesized. data sets to predict the characteristic impedance and propagation constant for the perforate cascades. These results are then compared with experimental results reported in a companion paper.
Assessment of low- and high-level task performance in people with transtibial amputation using crossover and energy-storing prosthetic feet: A pilot study

Background: Crossover feet incorporate features of energy-storing feet and running-specific feet. As such, crossover feet may be suitable for both daily ambulation and participation in physically demanding activities. Objectives: To compare crossover feet and energy-storing feet on performance-based tests including a range of low-level (e.g. sit-to-stand) and high-level (e.g. jogging) activities. Study design: Cross-sectional, repeated measures. Methods: Participants with transtibial amputation completed a battery of performance-based outcome measures, including the Five Times Sit-to-Stand, Timed-Up-and-Go, Four Square Step Test, and the Comprehensive High-level Activity Mobility Predictor. Participants wore duplicate prostheses fit with crossover feet and energy-storing feet to perform the tests; the order of foot conditions was randomized. Paired t tests were used to evaluate differences between feet and order of testing. Results: Data from seven participants showed improvements in all measures while using crossover feet. Improvements in the second foot condition were also observed, indicating a practice effect for all measures. However, differences between feet and order of conditions were not statistically significant (p > 0.05). Conclusion: Results of this study suggest that crossover feet may improve low- and high-level mobility outcomes. However, intervention effects are small and practice effects were observed in all outcomes. Future research is needed to evaluate the influence of practice effects on performance-based mobility measures. Clinical relevance Crossover feet may improve transtibial prosthesis users’ performance compared to energy-storing feet across a range of activities, but additional research is needed. Practice effects may be an influential factor in the measurement of performance-based mobility outcomes and should be considered when performing a clinical assessment.
Yeast Δ12 Fatty Acid Desaturase: Gene Cloning, Expression, and Function

In an effort to elucidate the molecular mechanisms of fatty acid desaturation in yeast, a complete gene encoding Δ12 fatty acid desaturase of Saccharomyces kluyveri was cloned. The open reading frame of this gene (named Sk-FAD2) consists of 1,251 bp, encoding 416 amino acids. The deduced Sk-FAD2 protein had 37–55% identity with those from other filamentous fungi. Unlike the genes of these other fungi, S. cerevisiae expressing Sk-FAD2 was found to be capable of synthesizing the dienoic fatty acid hexadecadienoic acid as well as linoleic acid. Moreover, the Sk-FAD2-disrupted strain of S. kluyveri was unable to produce polyunsaturated fatty acids. These results suggested that Sk-FAD2 protein is a unique Δ12 fatty acid desaturase in S. kluyveri. Analysis of transcriptional expression revealed that Sk-FAD2 was not repressed by exogenous unsaturated fatty acids but responded to low-temperature stress.
PIP Loss : A Unitary-invariant Metric for Understanding Functionality and Dimensionality of Vector Embeddings

In this paper, we present a theoretical framework for understanding vector embedding, a fundamental building block of many deep learning models, especially in NLP. We discover a natural unitary-invariance in vector embeddings, which is required by the distributional hypothesis. This unitary-invariance states the fact that two embeddings are essentially equivalent if one can be obtained from the other by performing a relative-geometry preserving transformation, for example a rotation. This idea leads to the Pairwise Inner Product (PIP) loss, a natural unitary-invariant metric for the distance between two embeddings. We demonstrate that the PIP loss captures the difference in functionality between embeddings, and that it is tightly connect to two fundamental properties, namely similarity and compositionality. By formulating the embedding training process as matrix factorization under noise, we reveal a fundamental bias-variance tradeoff in dimensionality selection. With tools from perturbation and stability theory, we provide an upper bound on the PIP loss using the signal spectrum and noise variance, both of which can be readily inferred from data. Our framework sheds light on many empirical phenomena, including the existence of an optimal dimension, and the robustness of embeddings against over-parametrization. The bias-variance tradeoff of PIP loss explicitly answers the fundamental open problem of dimensionality selection for vector embeddings. 1 ar X iv :1 80 3. 00 50 2v 3 [ st at . M L ] 1 2 A pr 2 01 8
A General Family of Robust Stochastic Operators for Reinforcement Learning

We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors. Various theoretical results are established, which include showing on a sample path basis that our family of operators preserve optimality and increase the action gap. Our empirical results illustrate the strong benefits of our family of operators, significantly outperforming the classical Bellman operator and recently proposed operators.
Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps

We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models.
Rotor Position Self-Sensing of SRM Using PSO-RVM

The motors’ flux-linkage, current and angle obtained from the system with sensors were chosen as the sample data, and the estimation model of rotor position based on relevance vector machine (RVM) was built by training the sample data. The kernel function parameter in RVM model was optimized by the particle swarm algorithm in order to increase the fitting precision and generalization ability of RVM model. It achieved higher prediction accuracy with staying at the same on-line testing time as the RVM. And because the short on-line computation, the motor can operate at 3000 r/min in sensorless control with particle swarm optimization-relevance vector machine (PSO-RVM), which is higher than support vector machine (SVM) and neural network (NN). By simulation and experiment on the test motor, it is verified that the proposed estimation model can obtain the angle of full electrical period accurately under low speed and high speed operations in current chopped control and angle position control, which has satisfactory estimation precision.
Sequential Voting with Endogenous Voter Forecasts

We investigate a sequential voting model in which voters' forecasts of outcomes on future issues are determined endogenously. Voters are assumed to make decisions in an environment in which future outcomes are uncertain. The uncertainty arises from two possible sources. Voters may be uncertain of other voters' preferences. In addition, events that occur before future issues are decided may affect voters' preferences on future votes. Information available to voters at each point in time is characterized. An equilibrium to the voting problem, if one exists, is one for which the outcome on the issue currently being decided and voters' forecasts of outcomes on future issues are determined simultaneously. We show that an equilibrium exists under a particular voting institution and characterize voters' forecasts for this equilibrium.
Group IIA-Secreted Phospholipase A2 in Human Serum Kills Commensal but Not Clinical Enterococcus faecium Isolates

Human innate immunity employs cellular and humoral mechanisms to facilitate rapid killing of invading bacteria. The direct killing of bacteria by human serum is attributed mainly to the activity of the complement system, which forms pores in Gram-negative bacteria. ABSTRACT Human innate immunity employs cellular and humoral mechanisms to facilitate rapid killing of invading bacteria. The direct killing of bacteria by human serum is attributed mainly to the activity of the complement system, which forms pores in Gram-negative bacteria. Although Gram-positive bacteria are considered resistant to killing by serum, we uncover here that normal human serum effectively kills Enterococcus faecium. Comparison of a well-characterized collection of commensal and clinical E. faecium isolates revealed that human serum specifically kills commensal E. faecium strains isolated from normal gut microbiota but not clinical isolates. Inhibitor studies show that the human group IIA secreted phospholipase A2 (hGIIA), but not complement, is responsible for killing of commensal E. faecium strains in human normal serum. This is remarkable since the hGIIA concentration in “noninflamed” serum was considered too low to be bactericidal against Gram-positive bacteria. Mechanistic studies showed that serum hGIIA specifically causes permeabilization of commensal E. faecium membranes. Altogether, we find that a normal concentration of hGIIA in serum effectively kills commensal E. faecium and that resistance of clinical E. faecium to hGIIA could have contributed to the ability of these strains to become opportunistic pathogens in hospitalized patients.
Maternal and neonatal plasma antioxidant levels in normal pregnancy, and the relationship with fatty acid unsaturation

During pregnancy, maternal plasma concentrations of the peroxidation-susceptible polyunsaturated fatty acids (polyenes) increase. In addition, the proportion of polyenes is higher in neonatal plasma than in maternal plasma. To study whether these increased amounts of polyenes affect antioxidant levels, we measured lipid-soluble antioxidants in maternal and neonatal plasmas obtained during thirty-five normal pregnancies. These values were then related to the degree of phospholipid-fatty acid unsaturation. Maternal plasma levels of tocopherols and lutein increased during pregnancy, as assessed at 14, 22, and 32 weeks of gestation. However, β-carotene levels decreased, and levels of other carotenoids remained unchanged. Retinol levels were only decreased at 32 weeks of gestation. The value for α-tocopherol: phospholipid-polyene unsaturation index (UI) also increased during pregnancy, despite the observed increase in UI. Corresponding ratios for several carotenoids and retinol, however, decreased during pregnancy. After delivery, maternal plasma levels of δ-tocopherol and β + γ-tocopherol, as well as β + γ-tocopherol: UI values, were lower than values at 32 weeks of gestation. Umbilical-cord plasma antioxidant levels and antioxidant: UI values, except retinol: UI, were significantly lower than maternal values. Significant and consistent cord ν. maternal correlations were observed for plasma levels of β + γ-tocopherol, lutein and β-carotene, but not for δ-tocopherol, α-tocopherol, lycopene, α-carotene, and retinol. In conclusion, although during pregnancy maternal plasma tocopherol levels increased concurrently with, or more than, fatty acid unsaturation in plasma phospholipids, the decrease in carotenoid: UI values during gestation, the decrease in maternal plasma levels of δ-tocopherol and β + γ-tocopherol after delivery, and the low neonatal antioxidant levels merit further investigation.