Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency of back pain occurrence among high school youth including their physical activity.

INTRODUCTION Back pain diseases are among frequently reported health problems. Unfortunately, more often this problem also affects young people, high school students. Lifestyle that people are representing nowadays has a negative impact on their spine. Development of technology and transportation eliminates daily basic physical activity such as taking a walk to the school. Prolonged and inappropriate sitting posture during classes and using the computer at home are predisposing factors for back pain occurrence. PURPOSE The aim of this paper was to analyze the frequency of back pain occurrence among high school youth and attempt to relate their occurrence frequency with sex, time spent in sitting position, anthropometric parameters and physical activity of examined students. MATERIAL AND METHODS The examined group initially counted 272 teenagers, but ultimately 218 were taken into account for the analysis and 54 questionnaires were filled in incorrectly and rejected. All of them were high school students. Customized survey consisting of general part and closed questions regarding physical activity (Subjective Experience of Work Load - SEWL) was used as an examination tool. RESULTS Examined youth spent on average 10.04 hours daily in a sitting position. As much as 195 (89.45%) of examined teenagers declared to experience back pain at least once in their lives. The most of teenagers (117; 53.67%) indicated lumbar spine pain. Girls more often felt spine pain issues in comparison to boys. Short stature of teenagers was often related to back pain occurrence. No correlation between time spent in sitting position and frequency of skeleton axis pain occurrence was noted. Among group of boys a relation between more frequent physical activity and less often spine pain occurrence was observed. CONCLUSIONS 1. Spine pain is an important health problem of examined youth, occurring more frequent among girls than boys. 2. Body height correlate negatively with frequency of spine pain occurrence among youth. 3. Despite spending more than 10 hours daily in sitting position by examined youth, it didn't affect the frequency of back pain issues occurrence. 4. Higher physical activity of examined boys was related to less often back pains.
Biogenesis of copper nanoparticles (Cu-NPs) using leaf extract of Allium noeanum, antioxidant and in-vitro cytotoxicity

Abstract In this research, we formulated new chemotherapeutic copper nanoparticles (Cu NPs) containing Allium noeanum Reut. ex Regel leaf for treating human endometrial cancer. For investigating the antioxidant activitiy, the 2,2-diphenyl-1-picrylhydrazyl (DPPH) test was used. MTT test was used on normal (Human umbilical vein endothelial cells (HUVECs)) and human endometrial cancer (Ishikawa, HEC-1-A, HEC-1-B, and KLE) cell lines for comparing the anti-human endometrial cancer properties of Cu(NO3)2, A. noeanum leaf aqueous extract, and copper nanoparticles. Copper nanoparticles had high cell death and anti-human endometrial cancer effects against Ishikawa, HEC-1-A, HEC-1-B, and KLE cell lines. The IC50 of A. noeanum leaf aqueous extract and copper nanoparticles against HEC-1-B cell line were 548 and 331 µg/mL, respectively; against HEC-1-A cell line were 583 and 356 µg/mL, respectively; against KLE cell line were 609 and 411 µg/mL, respectively; and against Ishikawa cell line were 560 and 357 µg/mL, respectively. Among the above cell lines, the best result of anti-human endometrial cancer properties of copper nanoparticles was gained in the cell line of HEC-1-B. This study indicated excellent anti-human endometrial cancer potentials of copper nanoparticles containing A. noeanum in the in vitro condition.
Impact of YTHDF1 gene polymorphisms on Wilms tumor susceptibility: A five‐center case‐control study

Wilms tumor is the most frequent renal malignancy in children. YTHDF1 is associated with the development of several kinds of cancers, yet whether common variants of the YTHDF1 gene influence Wilms tumor risk is unknown. We present, here, a hospital‐based case‐control study specifically designed to investigate the role of YTHDF1 genetic variants on Wilms tumor.
Biotechnological modification of bioactive natural compounds for food industry

Enzymatic transglycosylation takes place between an oligosaccharide donor and a relatively large number of structurally diverse acceptor molecules such as bioactive natural compounds. The enzymes including maltogenic amylase (MAase), α-glucanotransferase (α-GTase), and cyclodextrin glucanotransferase (CG Tase) are involved in this transglycosylational bioconversion. We have investigated the biotechnological modification of natural compounds such as isoflavones by transglycosylation activity of MAase. MAases catalyze not only hydrolysis of substrate, but also transglycosylation reaction in the presence of various acceptor molecules by preferentially forming α-(1, 6) -glycosidie linkage. The mechanism of transglycosylation was elucidated on the basis of three-dimensional structure and site-directed mutagenesis. The results suggested that Glu-332, which is located in a pocket, plays an important role on the formation and accumulation of transfer products by aligning the acceptor molecule in the nucleophilic attack of the glyeosyl-enzyme intermediate. In vitro MAase is capable of transferring glucosyl moiety of donor molecules to acceptor such as genistin and puerarin to give an α- (1, 6)-gIycosidie Iinkage. We found that transglycosylation reactions have successfully increased the water solubility and the water-soluble products fully maintained the biological activities. In this presentation, some potential applications of the transglycosylated bioactive products in food industry will be discussed.
3D information is valuable for the detection of humans in video streams

In this paper, we propose a technique based on 3D information (also called depth or range) for the detection of humans. First, a background subtraction technique operates to detect the silhouettes of humans and objects moving in the scene. Then, a machine learning algorithm is used to predict if a silhouette annotated with depth matches a human silhouette or not. The complete method is designed to cope with defects introduced during the segmentation step. Results, obtained on computer generated data, show that 3D depth data is a valuable information for detecting humans in that it improves over techniques based on binary silhouettes. In our experiments, we have reached an accuracy of 99.9% thanks to the depth information.
Long non-coding RNA HCP5 functions as a sponge of miR-29b-3p and promotes cell growth and metastasis in hepatocellular carcinoma through upregulating DNMT3A

Multiple studies have revealed that long non-coding RNA (lncRNAs) served as regulatory factors in modulating tumorigenesis of hepatocellular carcinoma (HCC). In the present study, we demonstrated that lncRNA HCP5 was overexpressed in HCC tissues and cell lines, and these findings were obvious even in metastatic and recurrent cases. Knockdown of HCP5 significantly alleviated cell growth, metastasis, and invasion both in vitro and in vivo through promoting apoptosis and by inactivating the epithelial-mesenchymal transition (EMT) progress. Moreover, miR-29b-3p has been identified as a negatively regulatory target gene of HCP5, and served as a tumor suppressor of HCC to prevent cell proliferation, migration, and invasion. Subsequently, DNMT3A was identified as a downstream regulatory factor of miR-29b-3p, and acted as a participated element of HCC progression by activating AKT phosphorylation. Taken together, our study elucidated for the first time that HCP5 plays a crucial role in HCC via the HCP5/miR-29b-3p/DNMT3A/AKT axis and our findings demonstrated a novel diagnostic and therapeutic strategy with potentiality to treat HCC.
Organizational Resilience to Disruption Risks: Developing Metrics and Testing Effectiveness of Operational Strategies

This study draws from the system resilience literature to propose three different metrics for evaluating the resilience performance of organizations against disruptions: the initial loss due to the disruption, the maximum loss, and the total loss over time. In order to show the usefulness of the developed metrics in practice, we deploy these metrics to study the effectiveness of two resilience strategies: maintaining operational slack and broadening operational scope, by empirically analyzing the performance of manufacturing firms that experienced a disruption during the period from 2005 to the end of 2014. The results show that maintaining certain aspects of operational slack and broadening business scope and geographic scope can affect these different metrics in different ways. Our results help decisionmakers in risk management to gain a better understanding of the conditions under which the recommended strategies actually improve organizations’ resilience, as well as the ways in which they may do so.
Bayes ’ rule where possible

We define Perfect Bayesian Equilibrium as an assessment (a behavioral strategy profile and system of beliefs) that is consistent with a conditional probability system over the set of strategies. Consistency with a conditional probability system captures the idea of Bayes’ rule where possible. We compare this definition with almost Perfect Bayesian equilibrium (Mailath (2018)) and show that it is stronger.
Determination of Potassium Bromate by Ion Chromatography

An ion chromatographic method was established for the quantitative determination of bromate in bread and fish paste products. Samples were homogenized with water. After centrifugation, the supernatant was filtered through a 0. 45ƒÊm filter prior to injection. In ion chromatographic fractionation, bromate was eluted separately from the other anions, especially chloride, which interfered with the determination of bromate. The bromate was absorbed on a concentrator column, then analyzed quantitatively by ion chromatography. The average recoveries were 96.4% in bread and 93.9% in fish paste products. The determination limits were 1ppm and 2ppm in the former and the latter, respectively. In this method, bromate can be measured directly, in contrast to iodometry. A total of 9 food samples (bread 3, fish paste products 6) was analyzed. One sample, chikuwa, contained 10.1ppm of bromate, while the other samples contained indetectably small amounts of bromate.
Effect of fuel additive sorbents (kaolin and calcite) on aerosol particle emission and characteristics during combustion of pelletized woody biomass

The objective of the present work was to determine the effect of two fuel additive sorbents, i.e. kaolin and calcite (CaCO3) on the aerosol particle emission and characteristics during combustion of pelletized woody biomass fuels, i.e. bark and cleaning assortments in a residential pellet burner. The influence of adding 1 2 wt % of the additives to woody pellets was studied by sampling particles, size classify them and perform elemental analysis on the samples. A significant reduction of fine particles was seen by adding kaolin to both studied ash rich woody fuels. For calcite, this effect was only marginal. The formed fine particles during both bark and cleaning assortment as well as with the studied additives were in all cases dominated by K, Na, S, Cl and Zn. However, it was clearly shown that addition of kaolin significantly reduced the fraction of K and S in the particles. This implies for example that the Cl/S ratio in the fine particles was influenced by the kaolin additive. The present study shows that there is a potential of reducing fine inorganic particle emissions during combustion of woody biomass in residential pellet appliances by using clay minerals, here illustrated by kaolin. However, the potential influence of different fuel additives on the complete ash transformation processes, e.g. slag formation and alkali volatilization has to be considered for specific fuels and fuel mixtures of different biomass raw materials.
Oridonin Attenuates the Effects of Chronic Alcohol Consumption Inducing Oxidative, Glycative and Inflammatory Injury in the Mouse Liver

Background/Aim: Oridonin (Ori) is a diterpenoid naturally present in medicinal plants with a potential as an antioxidant agent. This study aimed to evaluate the hepatic anti-oxidative, anti-glycative and anti-inflammatory properties of Ori at 0.125 and 0.25% against chronic ethanol intake in mice. Materials and Methods: Mice were divided into five groups: i) normal diet group, ii) Ori group, iii) ethanol diet (Lieber-DeCarli liquid diet with ethanol) group, iv) ethanol diet plus 0.125% Ori and v) ethanol diet plus 0.25% Ori. After 8 weeks of Ori supplementation, blood and liver tissue were used for analyses. Results: Ethanol increased the production of reactive oxygen species and nitric oxide, decreased glutathione content, and lowered the activity of glutathione peroxide, glutathione reductase and catalase. Ethanol suppressed the hepatic mRNA expression of nuclear factor E2-related factor 2. Ori supplements reversed these changes. Ethanol increased hepatic Ne-(carboxyethymethyl)-lysine (CML) and pentosidine levels, and enhanced aldose reductase (AR) activity and mRNA expression. Ori supplements at only 0.25% decreased CML and pentosidine levels, and lowered the AR activity as well as its mRNA expression. Ethanol increased the hepatic release of tumor necrosis factor-alpha, transforming growth factor-beta1, interleukin (IL)-1beta and IL-6. Histological data showed that ethanol induced necrosis and inflammatory cell infiltration, while Ori supplements alleviated these inflammatory responses. Ethanol up-regulated the hepatic mRNA expression of nuclear factor kappa B, myeloperoxidase and p38. Ori supplements reversed these changes. Conclusions: These novel findings suggest that Ori could be used as a potent agent against alcohol-induced hepatotoxicity.
Encoding the certainty of boolean variables to improve the guidance for search-based test generation

Search-based test generation commonly uses fitness functions based on branch distances, i.e., estimations of how close conditional statements in a program are to evaluating to true or to false. When conditional statements depend on Boolean variables or Boolean-valued methods, the branch distance metric is unable to provide any guidance to the search, causing challenging plateaus in the fitness landscape. A commonly proposed solution is to apply testability transformations, which transform the program in a way that avoids conditional statements from depending on Boolean values. In this paper we introduce the concept of Certainty Booleans, which encode how certain a true or false Boolean value is. Using these Certainty Booleans, a basic testability transformation allows to restore gradients in the fitness landscape for Boolean branches, even when Boolean values are the result of complex interprocedural calculations. Evaluation on a set of complex Java classes and the EvoSuite test generator shows that this testability transformation substantially alters the fitness landscape for Boolean branches, and the altered fitness landscape leads to performance improvements. However, Boolean branches turn out to be much rarer than anticipated, such that the overall effects on code coverage are minimal.
Exo-SIR: An Epidemiological Model to Quantify the Exogenous Information Diffusion and its Application to Detect Events

Online social media has changed the way we function as a society. People talk about various topics like wishing someone’s birthday to political issues. We study how the information diffuse through the online social networks. We propose a model called Exo-SIR model that quantifies the extent to which the diffusion is powered by external sources of information. Also, we propose that this model could be used for detecting events by analyzing the patterns of user behavior.
Realistic utility functions prove difficult for state-of-the-art interactive multiobjective optimization algorithms

Improvements to the design of interactive Evolutionary Multiobjective Algorithms (iEMOAs) are unlikely without quantitative assessment of their behaviour in realistic settings. Experiments with human decision-makers (DMs) are of limited scope due to the difficulty of isolating individual biases and replicating the experiment with enough subjects, and enough times, to obtain confidence in the results. Simulation studies may help to overcome these issues, but they require the use of realistic simulations of decision-makers. Machine decision-makers (MDMs) provide a way to carry out such simulation studies, however, studies so far have relied on simple utility functions. In this paper, we analyse and compare two state-of-the-art iEMOAs by means of a MDM that uses a sigmoid-shaped utility function. This sigmoid utility function is based on psychologically realistic models from behavioural economics, and replicates several realistic human behaviours. Our findings are that, on a variety of well-known benchmarks with two and three objectives, the two iEMOAs do not consistently recover the most-preferred points. We hope that these findings provide an impetus for more directed design and analysis of future iEMOAs.
Importance of Surface Chemistry in Bacterial Adhesion to Metals and Biocorrosion

The aim of this work is to emphas ize the central role of surface chemistry as a de termining factor dur ing the process of bacterial adhesion and biof i lm formation on metals. Experimental results showing the influence of the presence and composi t ion of an oxide layer, the pH, and the ionic strength of the solution on bacterial adhesion are reviewed and discussed. Indeed, af ter a biofi lm has been established, modificat ion of the surface chemistry as a result of bacterial activity is considered as a key variable influencing the corrosion process.
A geometric encoding for neural network evolution

A major limitation to the optimization of artificial neural networks (ANN) with evolutionary methods lies in the high dimensionality of the search space, the number of weights growing quadratically with the size of the network. This leads to expensive training costs, especially in evolution strategies which rely on matrices whose sizes grow with the number of genes. We introduce a geometric encoding for neural network evolution (GENE) as a representation of ANN parameters in a smaller space that scales linearly with the number of neurons, allowing for efficient parameter search. Each neuron of the network is encoded as a point in a latent space and the weight of a connection between two neurons is computed as the distance between them. The coordinates of all neurons are then optimized with evolution strategies in a reduced search space while not limiting network fitness and possibly improving search.
Tackling Algorithmic Transparency in Communal Energy Accounting through Participatory Design

Algorithmic transparency presents a significant challenge to system developers and users of algorithmic systems alike. Framing the problem as a ‘wicked’ one, this study tackles the issue of transparency in the EnerCoach energy accounting tool through presenting a situated ethnography of the algorithmic system and exploring the issues and challenges of model transparency and post-hoc explainability therein. By engaging stakeholders through participatory design methodologies, both a conceptual understanding of the problem and material solutions thereof are developed and evaluated. The findings show the promising potential of participatory design methodologies to elevate users to a ‘critical audience’, and the solutions co-created by the study participants for the challenge of algorithmic transparency. The results also highlight the complexity of the problem: transparency of algorithmic systems must be understood as a multi-facetted and highly contextual, ‘wicked’ problem that requires diverse methodological interventions to reach ‘satisficing’ solutions.
In2O3 based perovskite solar cells

Hybrid organic-inorganic perovskite solar cells have attracted lots of attention in recent years. Growth and properties of perovskite layer and its relationship to photovoltaic performance have been extensively studied. Comparably less attention was devoted to the research of the influence of electron transporting layer (ETL). Conventionally, TiO2 is selected as ETL. However, photocatalytic property of this transparent conductive metal oxide reduces the stability of perovskite solar cells under illumination. To realize the commercialization, the stability of perovskite solar cell must be improved. In this study, we replace TiO2 by In2O3, which is not only transparent and conductive, but also has little photocatalytic effect and it has higher electron mobility than TiO2. Investigation on different solution process methods of In2O3 as ETL is demonstrated.
eDiaPredict: An Ensemble-based Framework for Diabetes Prediction

Medical systems incorporate modern computational intelligence in healthcare. Machine learning techniques are applied to predict the onset and reoccurrence of the disease, identify biomarkers for survivability analysis depending upon certain health conditions of the patient. Early prediction of diseases like diabetes is essential as the number of diabetic patients of all age groups is increasing rapidly. To identify underlying reasons for the onset of diabetes in its early stage has become a challenging task for medical practitioners. Continuously increasing diabetic patient data has necessitated for the applications of efficient machine learning algorithms, which learns from the trends of the underlying data and recognizes the critical conditions in patients. In this article, an ensemble-based framework named eDiaPredict is proposed. It uses ensemble modeling, which includes an ensemble of different machine learning algorithms comprising XGBoost, Random Forest, Support Vector Machine, Neural Network, and Decision tree to predict diabetes status among patients. The performance of eDiaPredict has been evaluated using various performance parameters like accuracy, sensitivity, specificity, Gini Index, precision, area under curve, area under convex hull, minimum error rate, and minimum weighted coefficient. The effectiveness of the proposed approach is shown by its application on the PIMA Indian diabetes dataset wherein an accuracy of 95% is achieved.
Psychographic Matching between a Call Center Agent and a Customer

The interpersonal compatibility of a company agent and a customer significantly affects the outcome of their communication. In existing research, however, compatibility has been studied only in terms of the similarity in personality and values. That is, agents and customers were compared only on the same dimensions that make up their personality and values, e.g., on the same trait of the Big Five (compared agent's Extraversion and customer's Extraversion) or same values as per Schwartz's Basic Values (agent's Conformity and customer's Conformity). In this paper, we studied compatibility from a broader perspective, i.e., in addition to the similarity, we investigated interactions across different dimensions (e.g., an agent's Extraversion and a customer's Conformity, or the former's Extraversion and the latter's Neuroticism). Examining 7,594 real call logs collected from telemarketing call centers, we have confirmed that such different dimensional interactions significantly affect a customer making a purchase (i.e., a customer's conversion) or not. A simulation where we matched agents and customers demonstrated that our compatibility model that incorporated the interactions across different dimensions yielded significant conversion lift, i.e., +46% on the average, compared from one that used only similarity in personality and values.