Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Engineering Support for Intelligent Software Test Optimization

Software testing is one of the most important processes in Software Development Life Cycle (SDLC) to ensure quality of software product. A large number of approaches for test data generation and optimization in software testing have been reported with varying degrees of success. In recent years, the application of artificial intelligence (AI) techniques in software testing to achieve quality software is an emerging area of research. It combines the concepts of the two domains AI and Software Engineering and yields innovative mechanisms that have the advantages of both of them. This chapter demonstrates the development of a novel software test sequence optimization framework using intelligent agents based graph searching technique. To ensure the quality, a set of test adequacy criteria such as path coverage, state coverage, and branch coverage have been applied in the approach discussed in this chapter, while achieving optimization.
The gut in later life

From cradle to grave, the gastrointestinal (GI) system is a common source of symptoms. So, what does it matter that someone is aged 88 years rather than 18 years? The answer is ‘a lot’. Differences range from changes in physiology to a growing impact of other illnesses on GI function. For example, deteriorating dentition and appetite can interfere with input at the top end of the tract, and a myriad of factors may disrupt transit, absorption and sphincter control at the other end. A multitude of challenges can face people in later life. This article offers 10 essential points about GI function in later life for GPs to consider.
Current Development and Technology in the Information Extraction for Clinical Narrative Text

Most of the clinical narratives are free-text forms. The information extractions from clinical narrative text are more complicated than those from other biomedical texts, such as books, articles, literature abstracts, and so on. In this paper, we review recent published researches on the implication and technology of information extraction from free-text clinical narratives. We mainly introduce the Hidden Markov Model, Support Vector Machine, Ontology and other combined method used in information extraction for clinical narratives. The objectives, typical systems, applications, technologies and future challenge of information extraction for clinical narrative are introduced in this paper.
Relationship of Cancer to Brain Aging Markers of Alzheimer's Disease: The Framingham Heart Study.

Background Previous studies have demonstrated a strong inverse association between cancer and risk of Alzheimer's disease (AD). This study aimed to further investigate this association by examining measures of cognitive performance and neuroimaging. Methods Neuropsychological (NP) test batteries consisting of quantitative measures of memory and executive function and volumetric brain magnetic resonance imaging (MRI) scans measuring brain and white-matter hyperintensity volumes were administered to 2,043 dementia-free participants (54% women) in the Framingham Heart Study (FHS) Offspring cohort from 1999-2005. History of cancer was assessed at examination visits and through hospital records. Linear regression was used to examine the association between cancer history and NP/MRI variables. Results There were 252 and 1,791 participants with and without a previous history of cancer, respectively. Cancer survivors had an average time between diagnosis and NP/MRI exam of 9.8 years. History of any invasive cancer was associated with better executive function (Beta=0.16, p=0.04) but not memory function. Non-invasive cancer was not associated with any change in cognitive performance. Patients with prostate cancer had larger frontal brain volumes (Beta=4.13, p=0.03). Cancer history was not associated with any other MRI measure. Conclusions We did not find any strong evidence linking cancer to cognitive or neuroimaging biomarkers that would explain a lower risk of subsequent AD, although a previous FHS study demonstrated a strong inverse association between cancer and risk of AD. Future work should examine the association between cancer and other biomarkers of AD as well as more sensitive metrics of AD-related brain aging markers.
Recognition of Chinese place names based on ontology

This paper first analyzes the shortcomings of recognition Chinese place names from texts in domestic natural language processing field,then proposes a method of recognition Chinese place names based on ontology.This method recognizes Chinese administrative division names using ontology under GATE environment,makes them correspond to specific geographic locations on the earth's surface,then gives them geographic coordinates and geographical semantics.It focuses on building place name ontology,geoparsing and geo/non-geo,geo/geo disambiguation during geocoding.The results of experiment shows the method have a more satisfactory precision and recall,and geo/non-geo and geo/geo disambiguation algorithms is feasible and effective.
Determining Energy Expenditure from Heart Rate Monitoring during Training Sessions at Bulgarian Wrestlers

Aim of the study was to determine physical activity’s part of daily energy expenditure by heart rate monitoring and using data to control athletes’ nutritional intake. Group of 10 male and 4 female wrestlers (age = 21 ± 1.8) at national level, who train 15 hours per week served as subjects in this investigation. The 72-hour HR recording was performed with a TEMEO cardiotelemetric system (made in Bulgaria). The energy expenditure during physical activity is determined by Method 1 of Hiilloskorpi et al. (2003). The determined daily energy expenditure is compared to the theoretically calculated. The deference is less than 100 kcal, but if subjects change the intensity, volume or duration of the workouts or increase their number, the difference will be much more evident.
Polysaccharides of Fructus corni Improve Ovarian Function in Mice with Aging-Associated Perimenopause Symptoms

Objective Perimenopause symptoms have an extremely high incidence in aging women. Development of new strategies to improve perimenopause symptoms is important topic in clinical context. Increasing studies have shown that the polysaccharides of Fructus corni (PFC) have many pharmacological activities including antiaging effects. Here, we evaluated the effects of PFC on the ovarian function in natural aging-associated perimenopause symptoms in mice. Methods Natural aging mice (16-month old) were orally administrated with PFC at 1.11 g/kg daily for 24 days with none-treated young mice (3-month old) as control. Blood samples were collected for measurements of serum levels of estradiol, progesterone, luteinizing hormone (LH), and follicle stimulating hormone (FSH). Ovaries were isolated for histopathological and molecular exanimations. Results We found that the aging mice had decreased number of growing follicles and corpus luteum in ovary, but treatment with PFC restored their amounts. Measurement of hormones showed that there were low serum levels of estradiol and progesterone but high levels of LH and FSH in aging mice; however PFC restored estradiol and progesterone levels but reduced LH and FSH levels. Immunohistochemical analysis with ovarian tissues also revealed that the expression of inhibin and insulin-like growth factor 1 was reduced in the ovary of aging mice but was restored by PFC. These data indicated that PFC regulated ovarian function-associated hormone levels in aging mice. Furthermore, there was reduced expression of antiapoptotic protein Bcl-2 and increased expression of proapoptotic molecules Bax and cleaved-caspase-3 in the ovary of aging mice. However, treatment with PFC upregulated Bcl-2 and downregulated Bax and cleaved-caspase-3, suggesting that PFC inhibited apoptosis of granulosa cells in the ovary of aging mice. Conclusion PFC improved the ovarian function in mice, which had high potential to be developed as a safe and effective therapeutic remedy for aging-associated perimenopause symptoms.
Psychosocial Distress Scores and Needs among Newly Diagnosed Sarcoma Patients: A Provincial Experience

Introduction Information on the psychosocial distress and needs of sarcoma patients at diagnosis is sparse. The Canadian Problem Checklist (CPC) and Psychosocial Screen for Cancer-Revised (PSSCAN-R) are validated tools to identify cancer patients' distress and are administered to all new patients referred to BC Cancer prior to their consultation. We used the CPC and PSSCAN-R to understand sarcoma patients' needs at the initial oncology consultation in British Columbia, Canada. Materials and Methods All sarcoma patients who completed the CPC and PSSCAN-R within 6 months of diagnosis between 2011 and 2016 were included. The retrospective chart review identified baseline demographics: age, performance status, disease location, resectability, and histology. Analysis was conducted using descriptive statistics, chi-squared test, Fisher's exact test, and Kaplan–Meier method. Results 413 sarcoma patients were identified. The majority of patients were over the age of 40 (83.3%) with ECOG performance status 0-1 (82.6%) and lower extremity tumors (55.4%). The most common diagnoses were liposarcoma 21.3%, undifferentiated pleomorphic sarcoma 12.1%, and myxofibrosarcoma 11.1%. At the initial consultation, 42.6% of patients were deemed resectable, 8.5% unresectable/metastatic, and 48.9% required further staging investigations. The top three patient-reported distress symptoms were feeling tense and unable to relax (50%), feeling nervous and shaky (48%), and experiencing repetitive and scary thoughts (42%). 38% of patients had subclinical/clinical anxiety symptoms, and 21% of patients had subclinical/clinical depression symptoms. 5% of patients expressed suicidal ideation. The top three concerns/needs were understanding of illness/treatment (45.5%), fear/worries (45.3%), and worry about family (23%). No differences in overall survival were identified for patients displaying symptoms of depression or anxiety versus no symptoms. Discussion Up to 45% of sarcoma patients experience some form of psychological distress at disease presentation. Patients desire information about their diagnosis and treatment. Tailored interventions to individual psychological comorbidity and improved patient education resources would be beneficial.
Döviz kuru oynaklığının Türkiye’nin tarımsal dış ticaretine etkisi

In this study, impact of the real effective exchange rate (REER) and its volatility (REERV) on Turkey's agricultural foreign trade was investigated. 25importantcountries in agricultural trade of Turkey and 1990-2012 periods were examined, and panel data analysis was used in this research. IGARCH model was applied to obtain for the REERV. Influence of the REER and REERV on Turkey's agricultural trade was analyzed whit FMOLS model both individual country and groups panel. According to the results of FMOLS model; It was determined that Turkey agricultural import (7.61%) and export (2.24%) were increased when the REER was risen about 1%. The agricultural import was decreased 18.83% in case the REERV was increased 1%. On the other hand, when the REERV was increased around 1%, there was no significantly relationship between agricultural export and REERV. As a result, it can be stated that REER and REERV were caused an imbalance on Turkey's agricultural foreign trade. Agricultural producers and industrialists are often adversely affected by the REERV. For these reasons, it is important to take protective measures for them.
Active learning query selection with historical information

This work describes novel methods and techniques to decrease the cost of employing active learning in text categorisation problems. The cost of performing active learning is a combination of labelling effort and computational overhead. Reducing the cost of active learning allows for accurate classifiers to be constructed inexpensively, increasing the number of realworld problems where machine learning solutions can be successfully applied. In this thesis we investigate strategies and techniques to reduce both computational expense and labelling effort in active learning. Critical to the success of active learning is the query selection strategy, which is responsible for identifying informative unlabelled examples. Selecting only the most informative examples will reduce labelling effort as redundant and uninformative examples are ignored. The majority of query selection strategies select queries based on the labelling predictions of the current classifier. This thesis suggests that information from prior iterations of active learning can help select more informative queries in the current iteration. We propose History-based query selection strategies, which incorporate predictions from prior iterations of active learning into the selection of the current query. These strategies have been shown to increase the accuracy of classifiers produced using active learning, thereby reducing labelling effort. In addition, History-based query selection strategies are very efficient since information is reused from previous iterations of active learning. Another contributing factor to the cost of active learning is computational expense. Query selection strategies can require considerable computation to identify the most informative examples. We investigate pre-filtering optimisation for the computationally inefficient error reduction sampling (ERS) query selection strategy. Pre-filtering restricts the number of unlabelled examples considered to a small subset of the pool, constructed using query selection strategy. Optimising ERS using pre-filtering was found to simultaneously reduce computational overhead and the labelling effort.
High-sensitivity Cardiac Troponin and Natriuretic Peptide in Persons Without Cardiovascular Disease and Subsequent Risk of Infection: Atherosclerosis Risk in Communities Study.

Whether persons without prevalent cardiovascular disease (CVD) but elevated levels of high-sensitivity cardiac troponin T (hs-cTnT) or N-terminal pro-B-type natriuretic peptide (NTproBNP) are at high risk of infection is unknown. In the Atherosclerosis Risk in Communities Study, we estimated the hazard ratios (HRs) of incident hospitalization with infection in relation to plasma hs-cTnT and NTproBNP among participants without prevalent CVD, and contrasted with those with prevalent CVD (coronary heart disease, heart failure, stroke) from 1996 through 2013. In multivariable Cox model, prevalent CVD was significantly associated with risk of infection (HR, 1.31 [95%CI,1.19,1.45]). Among participants without prevalent CVD, hs-cTnT and NTproBNP were independently associated with infection risk in a graded fashion (e.g., HR 1.44 [1.24,1.69] for hs-cTnT ≥14ng/L and 1.28 [1.14,1.44] for 9-13ng/L vs. <3ng/L; and 1.57 [1.35,1.81] for NTproBNP ≥248.1pg/mL and 1.19 [1.06,1.34] for 137.2-248.0pg/mL vs. <48.1pg/mL). The 15-year cumulative incidence of hospitalization with infection was similar between participants with prevalent CVD and those without prevalent CVD but had hs-cTnT ≥14ng/L or NTproBNP ≥248.1pg/mL. Thus, hs-cTnT and NTproBNP were independently associated with infection risk. Persons without CVD but with elevated levels of hs-cTnT or NTproBNP should be recognized to have a similar infection risk as those with prevalent CVD.
Resampling-based tests of functional categories in gene expression studies

William T. Barry: Resampling-based tests of functional categories in gene expression studies (Under the direction of Dr. Fred A. Wright and Dr. Andrew B. Nobel) DNA microarrays allow researchers to measure the coexpression of thousands of genes, and are commonly used to identify changes in expression either across experimental conditions or in association with some clinical outcome. With increasing availability of gene annotation, researchers have begun to ask global questions of functional genomics that explore the interactions of genes in cellular processes and signaling pathways. A common hypothesis test for gene categories is constructed as a post hoc analysis performed once a list of significant genes is identified, using classically derived tests for 2x2 contingency tables. We note several drawbacks to this approach including the violation of an independence assumption by the correlation in expression that exists among genes. To test gene categories in a more appropriate manner, we propose a flexible, permutation-based framework, termed SAFE (for Significance Analysis of Function and Expression). SAFE is a two-stage approach, whereby gene-specific statistics are calculated for the association between expression and the response of interest and then a global statistic is used to detect a shift within a gene category to more extreme associations. Significance is assessed by repeatedly permuting whole arrays whereby the correlation between all genes is held constant and accounted for. This permutation scheme also preserves the relatedness of categories containing overlapping genes, such that error rate estimates can iii be readily obtained for multiple dependent tests. Through a detailed survey of gene category tests and simulations based on real microarray, we demonstrate how SAFE generates appropriate Type I error rates as compared to other methods. Under a more rigorously defined null hypothesis, permutation-based tests of gene categories are shown to be conservative by inducing a special case with a maximum variance for the test statistic. A bootstrap-based approach to hypothesis testing is incorporated into the SAFE framework providing better coverage and improved power under a defined class of alternatives. Lastly, we extend the SAFE framework to consider gene categories in a probabilistic manner. This allows for a hypothesis test of co-regulation, using models of transcription factor binding sites to score for the presence of motifs in the upstream regions of genes.
What do students think about SNS in education? University students’ perceptions, expectations and concerns regarding educational uses of SNS

Social network sites (SNS) have become a popular technology topic in recent years and are currently used in educational environments. Although SNS have attracted much scholarly attention, students’ experiences with and expectations about SNS uses in education have not been deeply explored. This study investigates educational uses of SNS by analyzing university students’ perceptions, experiences, expectations, and concerns. A mixed method approach was used. The participants were 206 Turkish university students aged 18-28. As a quantitative data collection tool, “the social networking use survey” was employed. To better understand the students’ perceptions, expectations, and concerns about using SNS for educational purposes, face-to-face semi-structured interviews were conducted with 58 participants. The results of the study reveal that the students actively used SNS for different purposes. The participants also exhibited positive perceptions of SNS use for educational purposes. Among the biggest expectations is that instructors would share materials using SNS. When the students’ general expectations were examined, notably different results were obtained. The greatest concern of the students was that their posts could be seen by their instructors. All of the research findings are discussed in a detailed manner.
Improved DV-Hop Localization Algorithm Based on Anchor Weight and Distance Compensation in Wireless Sensor Network

Position information is the foundation of massive applications in Wireless Sensor Network(WSN). Three improved positioning algorithms based on DV-Hop are proposed in order to enhance the positioning accuracy of wireless sensor nodes. First improved algorithm is distance compensation algorithm (DCA) that creates a triangle model to compensate the estimated distance. The second improved algorithm creates a new chain table for all anchor nodes to record and compute the average hop distance. The third improved algorithm is weighting different anchor nodes with anchor nodes’ nearest unknown nodes. The second and third improved algorithms are based on the DCA. The simulation results show that the three improved algorithms are better than the original DV-Hop in localization accuracy. Compared to the original DV-Hop algorithm, the simulation results shows that the three improved algorithms proposed in the paper increase the positioning accuracy of the unknown nodes.
Factors Associated with Cardiorespiratory Fitness in a Swiss Working Population

Background Good cardiorespiratory fitness (high VO2max) has beneficial effects on morbidity and mortality. Therefore, a tool to estimate VO2max in daily clinical practice is of great value for preventing chronic diseases in healthy adults. This study aimed at exploring the cardiometabolic profile in a representative Swiss working population. Based on these insights, a regression model was derived revealing factors associated with VO2max. Methods Cross-sectional data of 337 healthy and full-time employed adults recruited in the Basel region, Switzerland, were collected. Anthropometric measurements to compute body mass index (BMI) and waist circumference (WC) were performed. A 20-meter shuttle run test was conducted to determine individual VO2max. Heart rate (HR) was measured at rest, during maximal exertion, and two minutes after exercise. Systolic (SBP) and diastolic blood pressure (DBP) were assessed at rest and after exercise. A multiple linear regression model was built to identify a set of nonexercise predictor variables of VO2max. Results Complete data of 303 individuals (63% male) aged 18 to 61 years (mean 33 ± 12 years) were considered for analysis. The regression model (adjusted R2 = 0.647, SE = 5.3) identified sex (β = -0.699, p < 0.001), WC (β = -0.403, p < 0.001), difference of maximal to resting HR (β = 0.234, p < 0.001), smoking (β = -0.171, p < 0.001), and age (β = -0.131, p < 0.01) as the most important factors associated with VO2max, while BMI, SBP, and DBP did not contribute to the regression model. Conclusions This study introduced a simple model to evaluate VO2max based on nonexercise parameters as part of daily clinical routine without needing a time-consuming, cost-intense, and physically demanding direct assessment of VO2max. Knowledge about VO2max may help identifying individuals at increased cardiovascular risk and may provide the basis for health counselling and tailoring preventive measures.
Recognition of Sarcasm in Microblogging Based on Sentiment Analysis and Coherence Identification

Recognition of sarcasm in microblogging is important in a range of NLP applications, such as opinion mining. However, this is a challenging task, as the real meaning of a sarcastic sentence is the opposite of the literal meaning. Furthermore, microblogging messages are short and usually written in a free style that may include misspellings, grammatical errors, and complex sentence structures. This paper proposes a novel method for identifying sarcasm in tweets. It combines two supervised classifiers, a Support Vector Machine (SVM) using N-gram features and an SVM using our proposed features. Our features represent the intensity and contradictions of sentiment in a tweet, derived by sentiment analysis. The sentiment contradiction feature also considers coherence among multiple sentences in the tweet, and this is automatically identified by our proposed method using unsupervised clustering and an adaptive genetic algorithm. Furthermore, a method for identifying the concepts of unknown sentiment words is used to compensate for gaps in the sentiment lexicon. Our method also considers punctuation and the special symbols that are frequently used in Twitter messaging. Experiments using two datasets demonstrated that our proposed system outperformed baseline systems on one dataset, while producing comparable results on the other. Accuracy of 82% and 76% was achieved in sarcasm identification on the two datasets.