Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon dioxide versus air insufflation enteroscopy: a systematic review and meta-analysis based on randomized controlled trials

Abstract Objectives To compare the insufflation of CO2 and ambient air in enteroscopy. Search sources The investigators researched the electronic databases MedLine, Cochrane Library, Central, LILACS, BVS, Scopus and Cinahl. The grey search was conducted in the base of theses of the University of São Paulo, books of digestive endoscopy and references of selected articles and in previous systematic revisions. Study eligibility criteria The evaluation of eligibility was performed independently, in a non-blind manner, by two reviewers, firstly by title and abstract, followed by complete text. Disagreements between the reviewers were resolved by consensus. Data collection and analysis method Through the spreadsheet of data extraction, where one author extracted the data and a second author checked the extraction. Disagreements were resolved by debate between the two reviewers. The quality analysis of the studies was performed using the Jadad score. The software RevMan 5 version 5.3 was used for the meta-analysis. Results Four randomized clinical trials were identified, totaling 473 patients submitted to enteroscopy and comparing insufflation of CO2 and ambient air. There was no statistical difference in the intubation depth between the two groups. When CO2 insufflation was reduced, there was a significant difference in pain levels 1 hour after the procedure (95 % IC, –2.49 [–4.72, –0.26], P: 0.03, I2: 20%) and 3 hours after the procedure (95% IC, –3.05 [–5.92, –0.18], P: 0.04, I2: 0 %). There was a usage of lower propofol dosage in the CO2 insufflation group, with significant difference (95 % IC, –67.68 [–115.53, –19.84], P: 0.006, I2: 0 %). There was no significant difference between the groups in relation to the use of pethidine and to the oxygen saturation. Limitations Restricted number of randomized clinical trials and nonuniformity of data were limitations to the analysis of the outcomes. Conclusion The use of CO2 as insufflation gas in enteroscopy reduces the pain levels 1 hour and 3 hours after the procedure, in addition to the reduction of the sedation (propofol) dosage used.
Exogenous Plant-Based Nutraceutical Supplementation and Peripheral Cell Mononuclear DNA Damage Following High Intensity Exercise

Plant-based nutraceuticals are categorised as nutritional supplements which contain a high concentration of antioxidants with the intention of minimising the deleterious effect of an oxidative insult. The primary aim of this novel study was to determine the effect of exogenous barley-wheat grass juice (BWJ) on indices of exercise-induced oxidative stress. Ten (n = 10) apparently healthy, recreationally trained (V̇O2max 55.9 ± 6 mL·kg−1·min−1), males (age 22 ± 2 years, height 181 ± 6 cm, weight 87 ± 8 kg, body mass index (BMI) 27 ± 1) volunteered to participant in the study. In a randomised, double-blinded, placebo-controlled crossover design, participants consumed either a placebo, a low dose (70 mL per day) of BWJ, or a high dose (140 mL per day) of BWJ for 7-days. Experimental exercise consisted of a standard maximal oxygen uptake test until volitional fatigue. DNA damage, as assessed by the single cell gel electrophoresis comet assay, increased following high intensity exercise across all groups (time × group; p < 0.05, Effect Size (ES) = 0.7), although there was no selective difference for intervention (p > 0.05). There was a main effect for time in lipid hydroperoxide concentration (pooled-group data, pre- vs. post-exercise, p < 0.05, ES = 0.2) demonstrating that exercise increased lipid peroxidation. Superoxide dismutase activity (SOD) increased by 44.7% following BWJ supplementation (pooled group data, pre- vs. post). The ascorbyl free radical (p < 0.05, ES = 0.26), α-tocopherol (p = 0.007, ES = 0.2), and xanthophyll (p = 0.000, ES = 0.5), increased between the pre- and post-exercise time points indicating a main effect of time. This study illustrates that a 7-day supplementation period of a novel plant-derived nutraceutical product is insufficient at attenuating exercise-induced oxidative damage. It is possible that with a larger sample size, and longer supplementation period, this novel plant-based nutraceutical could potentially offer effective prophylaxis against exercise-induced oxidative stress; as such, this justifies the need for further research.
A new multilevel inverter with genetic algorithm optimization for hybrid power station application

Multilevel inverters are one of the most brilliant technology evolution in power conversion. It provides a stair wave output signal out of multi DC sources used as input. This paper presents a new multilevel inverter topology. The 7 levels architecture principle used in this study is first presented. In order to achieve a better signal output quality an adaptive genetic algorithm is used to reduce the harmonic content. The THD minimization is realized by calculating the appropriate switching angle in real time. However this study is focused on the 7 levels case, it also refers to other levels. The analysis is based on THD evaluation in different cases. All simulations and programming were done using Matlab.
Expression analysis of vitellogenins in the workers of the red imported fire ant (Solenopsis invicta)

Vitellogenin has been proposed to regulate division of labor and social organization in social insects. The red imported fire ant (Solenopsis invicta) harbors four distinct, adjacent vitellogenin genes (Vg1, Vg2, Vg3, and Vg4). Contrary to honey bees that have a single Vg ortholog as well as potentially fertile nurses, and to other ant species that lay trophic eggs, S. invicta workers completely lack ovaries or the ability to lay eggs. This provides a unique model to investigate whether Vg duplication in S. invicta was followed by subfunctionalization to acquire non-reproductive functions and whether Vg was co-opted to regulate behavior within the worker caste. To investigate these questions, we compared the expression patterns of S. invicta Vg genes among workers from different morphological subcastes or performing different tasks. RT-qPCRs revealed higher relative expression of Vg1 in major workers compared to both medium and minor workers, and of Vg2 in major workers when compared to minor workers. Relative expression of Vg1 was also higher in carbohydrate foragers when compared to nurses and protein foragers. By contrast, the level of expression of Vg2, Vg3, and Vg4 were not significantly different among the workers performing the specific tasks. Additionally, we analyzed the relationship between the expression of the Vg genes and S-hydroprene, a juvenile hormone analog. No changes in Vg expression were recorded in workers 12 h after application of the analog. Our results suggest that in S. invicta the Vg gene underwent subfunctionalization after duplication to new functions based on the expression bias observed in these data. This may suggest an alternative and still unknown function for Vg in the workers that needs to be investigated further.
Egg Consumption in Infants is Associated with Longer Recumbent Length and Greater Intake of Several Nutrients Essential in Growth and Development

Nutrient intake during infancy is critical for healthy growth and development. The present study examined egg consumption and associations with nutrient intakes, markers of growth and weight-related measures in infants 6–24 months of age (N = 561) compared to infant egg non-consumers (N = 2129). Egg consumers were defined as those infants consuming eggs (i.e., with the exclusion of mixed dishes) during a 24-h dietary recall. Associations with nutrient intakes and markers of growth variables were evaluated using data from What We Eat in America, the dietary component of the National Health and Nutrition Examination Survey, 2001–2012. Mean energy and nutrient intakes were adjusted for the sample design using appropriate survey parameters and sample weights. Egg consumption was associated with greater energy intake compared to infants not consuming eggs (1265 ± 27 vs. 1190 ± 14 kcal/day; p = 0.01). Infant consumers of eggs also had greater protein (48 ± 0.7 vs. 41 ± 0.4 g/day), total choline (281 ± 6 vs. 163 ± 2 mg/day), lutein + zeaxanthin (788 ± 64 vs. 533 ± 23 mcg/day), α-linolenic acid (0.87 ± 0.02 vs. 0.82 ± 0.01 g/day), docosahexaenoic acid (DHA) (0.04 ± 0.02 vs. 0.02 ± 0.001 g/day), vitamin B12 (4.2 ± 0.1 vs. 3.7 ± 0.1 mcg/day), phosphorus (977 ± 15 vs. 903 ± 8 mg/day), and selenium (67 ± 1 vs. 52 ± 0.6 mcg/day; all p-values < 0.05). Egg consumers also had greater consumption of total fat (50 ± 0.7 vs. 45 ± 0.3 g/day), monounsaturated fat (17 ± 0.3 vs. 15 ± 0.1 g/day), saturated fat (20 ± 0.4 vs. 18 ± 0.2 g/day), and sodium (1663 ± 36 vs. 1418 ± 19 mg/day), with lower added sugar (4.7 ± 0.3 vs. 6.1 ± 0.2 tsp eq/day), and total sugar (87 ± 2 vs. 99 ± 1 g/day; all p-values < 0.05) vs. non-consumers of eggs. Egg consumption was also associated with lower intake of dietary folate, iron, magnesium and niacin relative to non-consumers of eggs. Egg consumption in infants was associated with longer recumbent length when compared to non-consumers of eggs (79.2 ± 0.2 vs. 78.7 ± 0.1 cm; p = 0.03). No associations were observed when comparing body weight. When compared to non-consumers of eggs and regardless of food security, poverty-income-ratio and Women, Infants and Children (WIC) supplemental nutrition status, egg consumption was associated with greater lutein + zeaxanthin intake per day. The current analyzes show that consumption of eggs in infant 6–24 months of age is linked with several nutrient intakes, including higher protein, lutein + zeaxanthin, choline, B12, selenium and phosphorus; and lower added and total sugars relative to non-consumers. Egg consumers also have less of several nutrients to be encouraged and a higher intake of nutrients to limit, thus presenting opportunities for educational strategies to potentially increase consumption of nutrient-dense foods in combination with eggs.
When less is more: mindfulness predicts adaptive affective responding to rejection via reduced prefrontal recruitment

Abstract Social rejection is a distressing and painful event that many people must cope with on a frequent basis. Mindfulness—defined here as a mental state of receptive attentiveness to internal and external stimuli as they arise, moment-to-moment—may buffer such social distress. However, little research indicates whether mindful individuals adaptively regulate the distress of rejection—or the neural mechanisms underlying this potential capacity. To fill these gaps in the literature, participants reported their trait mindfulness and then completed a social rejection paradigm (Cyberball) while undergoing functional magnetic resonance imaging. Approximately 1 hour after the rejection incident, participants reported their level of distress during rejection (i.e. social distress). Mindfulness was associated with less distress during rejection. This relation was mediated by lower activation in the left ventrolateral prefrontal cortex during the rejection incident, a brain region reliably associated with the inhibition of negative affect. Mindfulness was also correlated with less functional connectivity between the left ventrolateral prefrontal cortex and the bilateral amygdala and the dorsal anterior cingulate cortex, which play a critical role in the generation of social distress. Mindfulness may relate to effective coping with rejection by not over-activating top-down regulatory mechanisms, potentially resulting in more effective long-term emotion-regulation.
PILARS : Parallel Imaging with Large ARrays and Sinc-interpolation

Introduction: Large arrays make it possible to use parallel imaging to significantly accelerate MR imaging speed. K-domain reconstruction methods for parallel imaging such as GRAPPA [1] and MCMLI [2] are able to provide robust reconstructions. However, actual achieved acceleration factor is limited as the number of auto-calibration signals (ACS) is proportional to the acceleration factor and kernel size. This limitation is undesirable for large arrays with potentially high acceleration factors. This paper represents a novel reconstruction method for large array parallel imaging that uses Sinc kernels to interpolate k-space data. Interpolation coefficients are predetermined based on Sinc function with a phase parameter which can be obtained in an automated manner from a single ACS. Phantom experiments using a 64-channel system show that it provides higher acceleration factors, comparable reconstruction quality, and fast reconstruction speed. Theory and Methods: In the proposed method, the k-space data corresponding to the full FOV is recovered by
Learning Models of Sequential Decision-Making without Complete State Specification using Bayesian Nonparametric Inference and Active Querying

Learning models of decision-making behavior during sequential tasks is useful across a variety of applications, including human-machine interaction. In this paper, we present an approach to learning such models within Markovian domains based on observing and querying a decision-making agent. In contrast to classical approaches to behavior learning, we do not assume complete knowledge of the state features that impact an agent’s decisions. Using tools from Bayesian nonparametric inference and time series of agents’ decisions, we first provide an inference algorithm to identify the presence of any unmodeled state features that impact decision making, as well as likely candidate models. In order to identify the best model among these candidates, we next provide an active querying approach that resolves model ambiguity by querying the decision maker. Results from our evaluations demonstrate that, using the proposed algorithms, an observer can identify the presence of latent state features, recover their dynamics, and estimate their impact on decisions during sequential tasks.
Supply chain sustainability performance indicators - a content analysis based on published standards and guidelines

The goal of this paper is to provide researchers and practitioners with the required guidance to achieve a better assessment of the sustainability-related performance of an organization and its supply chain (SC). In order to achieve this, standards and guidelines covering three sustainability dimensions (economic, social, and environmental) were analyzed for the identification of sustainability performance indicators (SPIs). A content analysis approach was applied for the data collection and analysis. Twelve international standards and guidelines were identified and selected for the purpose of conducting an analysis to integrate the latest findings on SPIs in sustainable supply chain management (SSCM). The findings of the content analysis revealed 232 original set of instances of sustainability-related information, of which 46% were related to the environmental dimension, 43% were related to the social dimension and 11% were related to the economic dimension of sustainability. In order to provide a better understanding and evaluation, each sustainability dimension was characterized using attribute categories. A descriptive evaluation of the academic literature, standards, and guidelines resulted in 18 attribute categories. Collected indicators were classified according to these attribute categories. In-depth analysis, restructuring, standardizing of attribute categories and indicators have led to 70 unique and coherent SPIs. Out of which 49% indicators were identified as environmental SPIs, 37% as social SPIs and 14% as economic SPIs. The results revealed a complete lack of agreement among the current standards on how to measure the sustainability-related performance of an organization and its SC.
Deep Gaussian Processes with Convolutional Kernels

Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alternative to standard parametric deep learning models. A DGP is formed by stacking multiple GPs resulting in a well-regularized composition of functions. The Bayesian framework that equips the model with attractive properties, such as implicit capacity control and predictive uncertainty, makes it at the same time challenging to combine with a convolutional structure. This has hindered the application of DGPs in computer vision tasks, an area where deep parametric models (i.e. CNNs) have made breakthroughs. Standard kernels used in DGPs such as radial basis functions (RBFs) are insufficient for handling pixel variability in raw images. In this paper, we build on the recent convolutional GP to develop Convolutional DGP (CDGP) models which effectively capture image level features through the use of convolution kernels, therefore opening up the way for applying DGPs to computer vision tasks. Our model learns local spatial influence and outperforms strong GP based baselines on multi-class image classification. We also consider various constructions of convolution kernel over the image patches, analyze the computational trade-offs and provide an efficient framework for convolutional DGP models. The experimental results on image data such as MNIST, rectangles-image, CIFAR10 and Caltech101 demonstrate the effectiveness of the proposed approaches.
Adapting a System with Noisy Outputs with Statistical Guarantees

Many complex systems are intrinsically stochastic in their behavior which complicates their control and optimization. Current self-adaptation and self-optimization approaches are not tailored to systems that have (i) complex internal behavior that is unrealistic to model explicitly, (ii) noisy outputs, (iii) high cost of bad adaptation decisions, i.e. systems that are both hard and risky to adapt at runtime. In response, we propose to model the system to be adapted as black box and apply state-of-the-art optimization techniques combined with statistical guarantees. Our main contribution is a framework that combines runtime optimization with guarantees obtained from statistical testing and with a method for handling cost of bad adaptation decisions. We evaluate the feasibility of our approach by applying it on an existing traffic navigation self-adaptation exemplar.
BOCK : Bayesian Optimization with Cylindrical Kernels

A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball geometry of the search space using a cylindrical transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary, while concentrating its efforts relatively more near the center of the search region, where we expect the solution to be located. We evaluate BOCK extensively, showing that it is not only more accurate and efficient, but it also scales successfully to problems with a dimensionality as high as 500. We show that the better accuracy and scalability of BOCK even allows optimizing modestly sized neural network layers, as well as neural network hyperparameters.
Towards Webcam-based Eye Tracking in the Eclipse IDE

Eye tracking is used in program comprehension research and is potentially useful for enhancing integrated development environments (IDEs). While eye tracking plug-ins exist for the Eclipse IDE, eye trackers do not typically work on operating systems other than Windows. To make empirical studies accessible to programmers working on Linux or MacOS, a recently presented webcam-based eye tracker is being adapted to the Java platform and provided as a plug-in for the Eclipse IDE. The eye tracker shall demonstrate eye tracking in the IDE without external devices to stimulate ideas of how to improve webcam-based eye tracking and how eye tracking could be used to improve program comprehension research and IDEs.
Ranking Influential and Influenced Shares Based on the Transfer Entropy Network

Influence is a concept found in nature and society and is related to the interdependency among a set of objects. In the context of a stock market, the variation in price of shares can influence the variation in price of other shares, leading to influential and influenced shares. In this work we leverage the notion of transfer entropy to build a network of shares and pairwise directed influence that is used to rank the most influential and influenced shares. Classical network centrality metrics such as PageRank and HITS are leveraged to rank the nodes. We apply our methodology to the shares in the greater stock market in Brazil, we rank nodes to find source and destination of influence in that market, while also comparing the different rankings and their correlation with traded volume.
Automated Content Analysis: A Case Study of Computer Science Student Summaries

Technology is transforming Higher Education learning and teaching. This paper reports on a project to examine how and why automated content analysis could be used to assess precis writing by university students. We examine the case of one hundred and twenty-two summaries written by computer science freshmen. The texts, which had been hand scored using a teacher-designed rubric, were autoscored using the Natural Language Processing software, PyrEval. Pearson’s correlation coefficient and Spearman rank correlation were used to analyze the relationship between the teacher score and the PyrEval score for each summary. Three content models automatically constructed by PyrEval from different sets of human reference summaries led to consistent correlations, showing that the approach is reliable. Also observed was that, in cases where the focus of student assessment centers on formative feedback, categorizing the PyrEval scores by examining the average and standard deviations could lead to novel interpretations of their relationships. It is suggested that this project has implications for the ways in which automated content analysis could be used to help university students improve their summarization skills.
DNA Phase Transition in Charge Neutralization and Comformation Induced by Trivalent-Hydrolysed Metal Ions

It is well known that common trivalent counter ions can induce DNA compaction or condensation but are unable to invert DNA surface charge in a normal aqueous solution. In the present study, we found that trivalent-hydrolysed metal ions (Fe3+, Al3+) are not only capable of inducing DNA condensation, but they also invert the electrophoretic mobility of DNA by electrophoretic light scattering and single molecular techniques. In comparison with neutral trivalent cations, hydrolysed metal ions such as Fe3+ can induce DNA condensation at a much lower concentration of cations, and its corresponding morphology of condensed DNA was directly observed by atomic force microscopy (AFM). The condensing and unravelling forces of DNA condensates were measured by tethering DNA by magnetic tweezers (MT) measurements at various concentration of Fe3+ and Al3+. We found that a coil–globule transition of DNA by hydrolysed metal ions not only was observed in DNA-complex sizes, but also in the curve of electrophoretic mobility of DNA in solution. In contrast, the transition was not observed in the case of neutral trivalent cations such as La3+ and Co3+. We attribute the transition and charge inversion to the ion-specific interaction between hydrolysed metal ions and phosphates of DNA backbone.