Miracle-dz/UniFAR-FTUs huggingface dataset:
Outlier detection in the concept drifting environment

Outliers are observations that lie far away from the fitting function deduced from the bulk of a set of observations. The outlier detection has become more challenging when the nature of data has involved with the “concept drifting. ” To address this challenging issue, this study explores a decision support mechanism (DSM) for coping with the outlier detection problem in the concept drifting environment. The proposed DSM has the following features: (1) implementation of the resistant learning with envelope module via the adaptive single layer feed-forward neural network, (2) implementation of the incremental learning concept via the moving window technique, and (3) effectiveness and efficiency in terms of being more accurate in identifying outliers and of having to further investigate fewer outlier candidates. An experiment is implemented to validate the proposed DSM and the results are promising.
Opportunistic Routing for Disruption Tolerant Networks

Opportunistic networks represent one of the most interesting evolution of MANET paradigm. Generally speaking, opportunistic networks enable user communication in environments where disconnection and reconnection are likely and link performance is extremely non stationary. In this paper, we propose a routing protocol, based on the opportunistic routing paradigm, able to assure connectivity in ad hoc networks characterized by high link dynamic, namely in Disruption Tolerant Networks (DTNs). By means of numerical analysis, a comparison with both traditional and collaborative routing protocols has been state showing that our proposal is able to provide end-to-end connectivity in DTNs, taking advantage by the link dynamic.
Improved Layout of Phylogenetic Networks

Split networks are increasingly being used in phylogenetic analysis. Usually, a simple equal angle algorithm is used to draw such networks, producing layouts that leave much room for improvement. Addressing the problem of producing better layouts of split networks, this paper presents an algorithm for maximizing the area covered by the network, describes an extension of the equal-daylight algorithm to networks, looks into using a spring embedder and discusses how to construct rooted split networks.
The RNA-Binding Protein Whi3 Is a Key Regulator of Developmental Signaling and Ploidy in Saccharomyces cerevisiae

In Saccharomyces cerevisiae, the RNA-binding protein Whi3 controls cell cycle progression, biofilm formation, and stress response by post-transcriptional regulation of the Cdc28-Cln3 cyclin-dependent protein kinase and the dual-specificity protein kinase Yak1. Previous work has indicated that Whi3 might govern these processes by additional, yet unknown mechanisms. In this study, we have identified additional effectors of Whi3 that include the G1 cyclins Cln1/Cln2 and two known regulators of biofilm formation, the catalytic PKA subunit Tpk1 and the transcriptional activator Tec1. We also provide evidence that Whi3 regulates production of these factors by post-transcriptional control and might exert this function by affecting translational elongation. Unexpectedly, we also discovered that Whi3 is a key regulator of cellular ploidy, because haploid whi3Δ mutant strains exhibit a significant increase-in-ploidy phenotype that depends on environmental conditions. Our data further suggest that Whi3 might control stability of ploidy by affecting the expression of many key genes involved in sister chromatid cohesion and of NIP100 that encodes a component of the yeast dynactin complex for chromosome distribution. Finally, we show that absence of Whi3 induces a transcriptional stress response in haploid cells that is relieved by whole-genome duplication. In summary, our study suggests that the RNA-binding protein Whi3 acts as a central regulator of cell division and development by post-transcriptional control of key genes involved in chromosome distribution and cell signaling.
Selectivity estimation for spatial joins

Spatial joins are important and time consuming operations in spatial database management systems. It is crucial to be able to accurately estimate the performance of these operations so that one can derive efficient query execution plans, and even develop/refine data structures to improve their performance. While estimation techniques for analyzing the performance of other operations, such as range queries, on spatial data has come under scrutiny, the problem of estimating selectivity for spatial joins has been little explored. The limited forays into this area have used parametric techniques, which are largely restrictive on the datasets that they can be used for since they tend to make simplifying assumptions about the nature of the datasets to be joined. Sampling and histogram based techniques, on the other hand, are much less restrictive. However, there has been no prior attempt at understanding the accuracy of sampling techniques, or developing histogram based techniques to estimate the selectivity of spatial joins. Apart from extensively evaluating the accuracy of sampling techniques for the very first time, this paper presents two novel histogram based solutions for spatial join estimation. Using a wide spectrum of both real and synthetic datasets, it is shown that one of our proposed schemes, called Geometric Histograms (GH), can accurately quantify the selectivity of spatial joins.
Prevalence of Anal HPV Infection Among HIV-Positive Men Who Have Sex With Men in India

Background:India has a large population of HIV-positive individuals, including men who have sex with men (MSM), and the incidence of human papillomavirus (HPV)-related cancers is high. In developed countries, HIV-positive MSM exhibit the highest prevalence of anal HPV infection and incidence of anal cancer. Little is known about anal HPV infection in HIV-positive Indian MSM. Methods:We evaluated 300 HIV-positive MSM from 2 cities in India. Men were tested for anal HPV infection using L1-HPV DNA polymerase chain reaction with probes specific for 29 types and a mixture of 10 additional types. CD4+ level and plasma HIV viral load were measured. Participants completed an interviewer-administered questionnaire including a sexual history. Results:The prevalence of anal HPV was 95% (95% confidence interval: 91% to 97%). The 3 most common types were HPV 35 (20%), HPV 16 (13%), and HPV 6/11 (13%). History of taking antiretroviral medications decreased risk of anal HPV 16 infection [relative risk (RR): 0.6 (0.4–1.0)]. Having an increased number of vaginal sex partners lowered risk of any anal HPV infection. Ever having receptive sex increased risk of any anal HPV [RR: 1.2 (1.1–1.4)] and anal HPV 16 [RR: 6.5 (1.8–107)]. Conclusions:Almost all Indian HIV-positive MSM had anal HPV infection. The prevalence of HPV 16 was lower and the prevalence of other oncogenic HPV types was higher than in similar populations in North America and Europe. Vaccine-based prevention strategies for HPV infection in India should consider potential differences in HPV type distribution among HIV-infected MSM when designing interventions.
Two methylenetetrahydrofolate reductase gene (MTHFR) polymorphisms, schizophrenia and bipolar disorder: An association study

Recent meta‐analyses of the methylenetetrahydrofolate reductase gene (MTHFR) have suggested association between two of its functional single gene polymorphisms (SNPs; C677T and A1298C) and schizophrenia. Studies have also suggested association between MTHFR C677T and A1298C variation and bipolar disorder. In a replication attempt the MTHFR C677T and A1298C SNPs were analyzed in three Scandinavian schizophrenia case‐control samples. In addition, Norwegian patients with bipolar disorder were investigated. There were no statistically significant allele or genotype case‐control differences. The present Scandinavian results do not verify previous associations between the putative functional MTHFR gene polymorphisms and schizophrenia or bipolar disorder. However, when combined with previous studies in meta‐analyses there is still evidence for association between the MTHFR C677T polymorphism and schizophrenia. Additional studies are warranted to shed further light on these relationships. © 2007 Wiley‐Liss, Inc.
Adaptive localization algorithm in distributed asynchronous wireless sensor network

This paper presents a target tracking algorithm based on the synthesized information of signal's time-of-arrival (TOA) in an asynchronous distributed wireless sensor network (WSN). The proposed algorithm combines displacement estimate (DE) and position-displacement estimate (PDE) together, and the case of target's very slow motion is mainly focused on. Taking advantage of DE's efficiency and PDE's ability of eliminating accumulated error, the effect of tracking is fairly good and the validity of the algorithm is guaranteed as well. An adaptive threshold is introduced to switch from DE to PDE so as to eliminating the error accumulated in a series of consecutive DE estimates. And the error of estimating current target's position between these two methods is used to adjust threshold for activating the next switch. Simulations show the proposed method has quite good accuracy and availability in aimed cases as well as other situations.
The effect of shear flow on nanoparticle agglomeration and deposition in in vitro dynamic flow models

Abstract Traditional in vitro toxicity experiments typically involve exposure of a mono- or co-culture of cells to nanoparticles (NPs) in static conditions with the assumption of 100% deposition (i.e. dose) of well-dispersed particles. However, cellular dose can be affected by agglomeration and the unique transport kinetics of NPs in biological media. We hypothesize that shear flow can address these issues and achieve more predictable dosage. Here, we compare the behavior of gold NPs with diameters of 5, 10 and 30 nm in static and dynamic in vitro models. We also utilize transport modeling to approximate the shear rate experienced by the cells in dynamic conditions to evaluate physiological relevance. The transport kinetics show that NP behavior is governed by both gravity and diffusion forces in static conditions and only diffusion in dynamic conditions. Our results reveal that dynamic systems are capable of producing a more predictable dose compared to static systems, which has strong implications for improving repeatability in nanotoxicity assessments.
Estimation of Modulus of Elasticity in Static Bending of Wood in Structural Dimensions as a Function of Longitudinal Vibration and Density

In order to use wood in a sustainable and rational way, it is necessary to estimate its characteristics, and in the case of civil construction, the physical and mechanical properties are the most interesting. The aim of this research was to estimate the modulus of elasticity of structural pieces of Original Research Article Cavalheiro et al.; CJAST, 26(1): 1-8, 2018; Article no.CJAST. 39531 2 Schizolobium amazonicum Herb. wood through static bending (MOEsb) and longitudinal vibration (MOElv) methods, in addition, to generate regression models to estimate the modulus of elasticity of static bending. Tests for MOEsb and MOElv estimation were carried out according to ABNT NBR 7190:1997 and FAKOPP Enterprise, respectively. Linear and quadratic regression models were used to estimate MOEsb as a function of MOElv. A multivariate regression model was also generated considering the wood density. According to the discussion of the obtained results, MOElv and MOEsb average values estimated for the studied batch of Schizolobium amazonicum Herb. wood were 9.70 and 9.07 GPa, and average density 381.21 kg/m3. Regression models proposed for estimation of MOEsb presented the coefficient of determination close to 60%.
Screening for Emotional Distress in Cancer Patients: A Systematic Review of Assessment Instruments

Screening for emotional distress is becoming increasingly common in cancer care. This systematic review examines the psychometric properties of the existing tools used to screen patients for emotional distress, with the goal of encouraging screening programs to use standardized tools that have strong psychometrics. Systematic searches of MEDLINE and PsycINFO databases for English-language studies in cancer patients were performed using a uniform set of key words (eg, depression, anxiety, screening, validation, and scale), and the retrieved studies were independently evaluated by two reviewers. Evaluation criteria included the number of validation studies, the number of participants, generalizability, reliability, the quality of the criterion measure, sensitivity, and specificity. The literature search yielded 106 validation studies that described a total of 33 screening measures. Many generic and cancer-specific scales satisfied a fairly high threshold of quality in terms of their psychometric properties and generalizability. Among the ultrashort measures (ie, those containing one to four items), the Combined Depression Questions performed best in patients receiving palliative care. Among the short measures (ie, those containing five to 20 items), the Center for Epidemiologic Studies–Depression Scale and the Hospital Anxiety and Depression Scale demonstrated adequate psychometric properties. Among the long measures (ie, those containing 21–50 items), the Beck Depression Inventory and the General Health Questionaire–28 met all evaluation criteria. The PsychoSocial Screen for Cancer, the Questionnaire on Stress in Cancer Patients–Revised, and the Rotterdam Symptom Checklist are long measures that can also be recommended for routine screening. In addition, other measures may be considered for specific indications or disease types. Some measures, particularly newly developed cancer-specific scales, require further validation against structured clinical interviews (the criterion standard for validation measures) before they can be recommended.
Empirical study of software quality evolution in open source projects using agile practices

We analyse the time evolution of two open source Java projects: Eclipse and Netbeans, both developed following agile practices, though to a different extent. Our study is centered on quality analysis of the systems, measured as defects absence, and its relation with software metrics evolution. The two projects are described through a software graph in which nodes are represented by Java files and edges describe the existing relation between nodes. We propose a metrics suite for Java files based on Chidamber and Kemerer suite, and use it to study software evolution and its relationship with bug count.
Multiple Regression Analysis on Heart Rate Signal using Hand GSR and R peak of ECG signals

This paper proposes a mathematical model for the heart rate signal using hand GSR and power samples of the R peak of the ECG through multiple regression analysis. Mathematical model helps to study the relation of the dependent function with its independent functions and to correlate with the biological processes. The paper proposed four models for heart rate. Each model had R 2 values such as 0.0594, 0.2169, 0.9296 and 0.9962 respectively. The fourth model was robust and quite acceptable because of its high goodness of fit. The study also proved a non linear relationship of the heart rate signal with the hand GSR and R peak of the ECG signal.
Year : 2010 Impaired glucose tolerance in sleep disorders

BACKGROUND: Recent epidemiological and experimental data suggest a negative influence of shortened or disturbed night sleep on glucose tolerance. Due to the high prevalence of sleep disorders this might be a major health issue. However, no comparative studies of carbohydrate metabolism have been conducted in clinical sleep disorders. METHODOLOGY/PRINCIPAL FINDINGS: We performed oral glucose tolerance tests (OGTT) and assessed additional parameters of carbohydrate metabolism in patients suffering from obstructive sleep apnea syndrome (OSAS, N = 25), restless legs syndrome (RLS, N = 18) or primary insomnia (N = 21), and in healthy controls (N = 33). Compared to controls, increased rates of impaired glucose tolerance were found in OSAS (OR: 4.9) and RLS (OR: 4.7) patients, but not in primary insomnia patients (OR: 1.6). In addition, HbA1c values were significantly increased in the same two patient groups. Significant positive correlations were found between 2-h plasma glucose values measured during the OGTT and the apnea-arousal-index in OSAS (r = 0.56; p<0.05) and the periodic leg movement-arousal-index in RLS (r = 0.56, p<0.05), respectively. Sleep duration and other quantitative aspects of sleep were similar between patient groups. CONCLUSIONS/SIGNIFICANCE: Our findings suggest that some, but not all sleep disorders considerably compromise glucose metabolism. Repeated arousals during sleep might be a pivotal causative factor deserving further experimental investigations to reveal potential novel targets for the prevention of metabolic diseases. Impaired Glucose Tolerance in Sleep Disorders Marietta Keckeis, Zuzana Lattova, Eszter Maurovich-Horvat, Pierre A. Beitinger, Steffen Birkmann, Christoph J. Lauer, Thomas C. Wetter, Johanna Wilde-Frenz, Thomas Pollmächer* 1 Max Planck Institute of Psychiatry, Munich, Germany, 2 Klinikum Ingolstadt, Centre of Mental Health, Ingolstadt, Germany
Multitaper estimation of bicoherence

The statistical properties for bicoherence estimation are shown to be strongly connected to the properties of the power- and bispectral estimator used. Data tapering will reduce spectral leakage and frequency smoothing will reduce the variance. It is shown that correct normalization is essential to ensure unbiased results. The multitaper approach is shown to be superior to other non-parametric estimators for bicoherence estimation.
DIFFER: A Propositionalization approach for Learning from Structured Data

Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. Attempts made to improve their efficiency are often associated with the risk of degrading the performance of the predictive models, creating tradeoffs between the efficiency and effectiveness of the learning. Such a situation is analogous to an optimization problem with two objectives, efficiency and effectiveness, where improving one objective without the other objective being worse off is a better solution, called a Pareto improvement. In this thesis, it is investigated how to improve the efficiency and effectiveness of learning from graph data using pattern mining methods. Two objectives are set where one concerns how to improve the efficiency of pattern mining without reducing the predictive performance of the learning models, and the other objective concerns how to improve predictive performance without increasing the complexity of pattern mining. The employed research method mainly follows a design science approach, including the development and evaluation of artifacts. The contributions of this thesis include a data representation language that can be characterized as a form in between sequences and itemsets, where the graph information is embedded within items. Several studies, each of which look for Pareto improvements in efficiency and effectiveness are conducted using sets of small graphs. Summarizing the findings, some of the proposed methods, namely maximal frequent itemset mining and constraint based itemset mining, result in a dramatically increased efficiency of learning, without decreasing the predictive performance of the resulting models. It is also shown that additional background knowledge can be used to enhance the performance of the predictive models, without increasing the complexity of the graphs.
Adoption and Diffusion in Electronic Markets: An Empirical Analysis of Attributes Influencing the Adoption of Paid Content

The rapid growth of the Internet and electronic commerce stimulates new digital innovations. Electronic markets can influence both the adoption and diffusion processes of digital innovations in significant ways. The difficulties for market transactions of information goods and the change from free to paid content on websites in recent years has led to research questions about how individuals decide whether and when to adopt paid content innovations and how this innovation diffuses throughout a population. This paper presents empirical evidence regarding the adoption and diffusion process of paid content. It focuses on the differences of the adoption and diffusion process of different paid content types dependent on: (1) the existence of established, non‐digital counterparts; (2) experience and familiarity with paid content and micropayment services; and (3) economic advantage and perceived convenience. Results pave a ground for a better understanding how paid content will support knowledge work in the future.
Cross-fertilization as a reproductive strategy in a tissue flukes Didymosulcus katsuwonicola (Platyhelmintes: Didymozoidae) inferred by genetic analysis

SUMMARY Mitochondrial DNA locus cytochrome oxidase I was used to asses intraspecific genetic diversity of a didymozoid species Didymosulcus katsuwonicola. Adult forms of this species live encapsulated in pairs in the gills of the reared Atlantic bluefin tuna (Thunnus thynnus). The life cycle of this food-borne parasites and its migration in the host tissues after releasing from the digestive tract to the definitive site in the gills are unknown. Our goal was to assess whether two encysted didymozoids share the same haplotype, indicative of a common maternal origin, as well as the extent of cross- in respect to self-fertilization strategy. Intraspecific comparison showed high haplotype diversity, while the presence of two matching haplotypes within a single cyst encompassed only 17% of sampled individuals. This infers that cross-fertilization between paired individuals within the cyst is more common mechanism than self-fertilization. Such hermaphroditic parasite's trait suggests the existence of intricate infection and reproduction mechanisms, presumably as an adaptation for successful fulfillment of their indirect life cycle through dissemination of genetically more diverse and consequently more fit offspring.
An Improved Signal Segmentation Method using Genetic Algorithm

ABSTRACT In many signal processing application, the signal of interest is often divided into epochs. In these applications, the segmented signal is preferred to have no change on the statistical characteristics of the epochs. Modified Varri is among the segmentation methods with an acceptable accuracy. There are three parameters affecting on the accuracy of this method. These parameters are set experimentally. Hence, they may not be optimal for any signal segmentation application. We have used Genetic Algorithm (GA) in this research to choose appropriate values for these parameters in any signal segmentation application. The proposed technique was applied on both synthetic signal and Electroencephalography (EEG) to evaluate its performance. The results indicate superiority of the proposed method in signal segmentation compared to the original approach. General Terms Signal segmentation, Non-stationary Signal, Adaptive Segmentation, Genetic Algorithm (GA) and EEG Signal.