Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevention of Tungiasis and Tungiasis-Associated Morbidity Using the Plant-Based Repellent Zanzarin: A Randomized, Controlled Field Study in Rural Madagascar

Background Tungiasis, a parasitic skin disease caused by the female sand flea Tunga penetrans, is a prevalent condition in impoverished communities in the tropics. In this setting, the ectoparasitosis is associated with important morbidity. It causes disfigurement and mutilation of the feet. Feasible and effective treatment is not available. So far prevention is the only means to control tungiasis-associated morbidity. Methodology In two villages in Central Madagascar, we assessed the efficacy of the availability of closed shoes and the twice-daily application of a plant-based repellent active against sand fleas (Zanzarin) in comparison to a control group without intervention. The study population was randomized into three groups: shoe group, repellent group and control group and monitored for ten weeks. The intensity of infestation, the attack rate and the severity of tungiasis-associated morbidity were assessed every two weeks. Findings In the repellent group, the median attack rate became zero already after two weeks. The intensity of the infestation decreased constantly during the observation period and tungiasis-associated morbidity was lowered to an insignificant level. In the shoe group, only a marginal decrease in the intensity of infestation and in the attack rate was observed. At week 10, the intensity of infestation, the attack rate and the severity score for acute tungiasis remained significantly higher in the shoe group than in the repellent group. Per protocol analysis showed that the protective effect of shoes was closely related to the regularity with which shoes were worn. Conclusions Although shoes were requested by the villagers and wearing shoes was encouraged by the investigators at the beginning of the study, the availability of shoes only marginally influenced the attack rate of female sand fleas. The twice-daily application of a plant-based repellent active against sand fleas reduced the attack to zero and lowered tungiasis-associated morbidity to an insignificant level.
Multi-agents SIP architecture for online Feature Interaction detection and resolution

This article presents a multi-agents architecture solution for runtime management of feature interactions (FI) in SIP-based networks. The problem that is addressed results from a major SIP architecture property: services are distributed over many nodes in the network, including application servers, third party providers, and sophisticated end user devices. Consequently, information about service logic as well as decision to run services are distributed over those nodes. This complicates the FI problem management. The present paper explains how this problem can be solved by assigning a Feature Interaction Management Agent (FIMA) to every network node that runs services. It also explains how those agents co-operate at runtime to find a common solution to avoid FI situations. MOTS-CLÉS : Interaction de service, détection et résolution en-ligne, agent de gestion d’interaction de services.
Readability metric feedback for aiding node-link visualization designers

Analyzing network data can provide valuable insights in many diverse fields. However, designing node-link visualizations that effectively communicate the underlying network is challenging, as for every network there are many potential unintelligible or even misleading layouts. Automated layout algorithms have helped, but frequently generate ineffective visualizations. In order to build awareness of effective node-link visualization strategies, we detail new global readability metrics on a "0,1] continuous scale for node-node overlap, edge crossing angle, angular resolution, group overlap, and visualization coverage. In addition, we define novel node-and-edge readability metrics to provide more localized identification of where improvement is needed. We describe the trade-offs inherent in optimizing individual metrics as well as recommend metric optimizations for particular tasks. Our metrics are implemented in a JavaScript® API (application programming interface) to make them widely available to designers of web-based visualization tools, who can use metrics to direct users towards poor areas of the drawing. Our prototype system using the API aims to help designers and theorists evaluate and compare their layouts.
Pulmonary lymphangioleiomyomatosis: a clinicopathological analysis of ten cases.

BACKGROUND Lymphangioleiomyomatosis (LAM) of the lung is a very rare disease of unknown etiology which occurs particularly in women of childbearing age. So far, there have been very few reports of LAM in Taiwan. METHODS Data on all patients with a clinical or pathological diagnosis of LAM in Chang Gung Memorial Hospital from 1990 to 2001 were collected from the pathology files or by consultation with thoracic surgeons, chest physicians, and radiologists of this institution. Cases were confirmed by lung biopsy and high-resolution computed tomographic scanning. Clinical data were obtained from patients' charts. Additional immunostaining for HMB-45 antigen, estrogen receptor, and progesterone receptor was carried out for cases with available paraffin blocks. RESULTS In total, 10 patients confirmed with a diagnosis of LAM and adequate clinical follow-up information were found from 1991 to 2001. All were females of fertility age. The follow-up periods ranged from 18 to 167 months. Six patients received hormonal therapy. According to the pulmonary function tests and clinical symptoms, 6 patients were in stable condition and 4 showed slow progression of disease by the time of the last follow-up. Two patients died after lung transplantation and abdominal surgery for retroperitoneal LAM, respectively, at another medical center. CONCLUSIONS The clinicopathological features of our series are similar to those reported from other countries. The clinical course was slowly progressive with no significant response to hormonal therapy. Lung biopsies are important for a diagnosis, but the size and location of the biopsy specimens greatly affect the diagnostic reliability.
Randomize-Then-Optimize for Sampling and Uncertainty Quantification in Electrical Impedance Tomography

In a typical inverse problem, a spatially distributed parameter in a physical model is estimated from measurements of model output. Since measurements are stochastic in nature, so is any parameter estimate. Moreover, in the Bayesian setting, the choice of regularization corresponds to the definition of the prior probability density function, which in turn is an uncertainty model for the unknown parameters. For both of these reasons, significant uncertainties exist in the solution of an inverse problem. Thus to fully understand the solution, quantifying these uncertainties is important. When the physical model is linear and the error model and prior are Gaussian, the posterior density function is Gaussian with a known mean and covariance matrix. However, the electrical impedance tomography inverse problem is nonlinear, and hence no closed form expression exists for the posterior density. The typical approach for such problems is to sample from the posterior and then use the samples to compute statistics (s.
Lack of evidence of HPV etiology of prostate cancer following radical surgery and higher frequency of the Arg/Pro genotype in turkish men with prostate cancer

ABSTRACT Objectives The aim of this study was to assess the possible role of HPV in the development of prostate cancer (PCa) and investigate the distribution of the p53 codon 72 polymorphism in PCa in a Turkish population. Materials and methods A total of 96 tissues, which had been obtained using a radical surgery method, formalin-fixed and parafin-embedded, were used in this study. The study group consisted of 60 PCa tissues (open radical prostatectomy) and the control group contained 36 benign prostatic hyperplasia tissues (BPH) (transvesical open prostatectomy). The presence of HPV and the p53 codon 72 polymorphism was investigated in both groups using real-time PCR and pyrosequencing. Results The results of the real-time PCR showed no HPV DNA in any of the 36 BPH tissue samples. HPV-DNA was positive in only 1 of the 60 PCa samples (1.7%). The HPV type of this sample was identified as HPV-57. The distribution of the three genotypes, Arg/Arg, Arg/Pro and Pro/Pro was found to be 45.6, 45.6, and 8.8% in the PCa group and 57.1%, 34.3% and 8.6% in the control group, respectively. Compared with the control group, patients with PCa had a higher frequency of the Arg/Pro genotype and Proline allele (odds ratio (OR)=1.67, 95% confidence interval (CI)=0.68-4.09, p=0.044; OR=1.13, 95% CI=0.76-1.68, p=0.021, respectively). Conclusions The results of the study do not support the hyphothesis that prostate cancer is associated with HPV infection but indicated that Proline allele can be a risk factor in the development of PCa in the Turkish population.
Efficient subcarrier and bit allocation algorithm for OFDMA system with adaptive modulation

This paper investigates the adaptive subcarrier and bit allocation algorithm for OFDMA systems. To minimize overall transmit power, we propose a novel adaptive subcarrier and bit allocation algorithm based on channel state information, Moreover, the block-wise method is considered based on channel variation for adaptive subcarrier and bit allocation. It is shown that a near optimal solution is obtained by the proposed algorithm which has low complexity compared to that of other conventional algorithms. Also, it is shown that the block-wise method significantly reduces the complexity and the feedback or side information though has a slight transmit power increase.
Graph abstraction for closed pattern mining in attributed networks

We address the problem of finding patterns in an attributed graph. Our approach consists in extending the standard methodology of frequent closed pattern mining to the case in which the set of objects, in which are found the pattern supports, is the set of vertices of a graph, typically representing a social network. The core idea is then to define graph abstractions as subsets of the vertices satisfying some connectivity property within the corresponding induced subgraphs. Preliminary experiments illustrate the reduction in closed patterns we obtain as well as what kind of abstract knowledge is found via abstract implications rules.
A multi-resolution-concentration based feature construction approach for spam filtering

This paper proposes a multi-resolution-concentration (MRC) based feature construction approach for spam filtering by progressively partitioning an email into local areas on smaller and smaller resolutions. The MRC approach depicts a dynamic process of gradual refinement in locating the pathogens by calculating concentrations of detectors on local areas, and is considered to be able to extract the position-correlated and process-correlated information from emails. Furthermore, A weighted MRC (WMRC) approach is presented by considering the different activity levels of detectors in calculation of concentrations. A generic structure of the MRC model, which mainly contains detector sets construction and multi-resolution concentrations calculation, is designed. The implementations of MRC and WMRC approaches are described in detail. Experiments are conducted on five benchmark corpora using cross-validation to evaluate the proposed MRC model. Comprehensive experimental results suggest that the MRC and WMRC approaches perform far better than the prevalent bag-of-words approach in both performance and efficiency. Compared with the concentration based feature construction approach and local-concentration based feature extraction approach, MRC and WMRC achieve higher accuracy and μ1 measure, which demonstrates the effectiveness of the MRC model. In addition, it is shown that both the MRC and WMRC approaches cooperate well with variety of classification methods, which endows the MRC model with flexible capability in the real world.
A Prospective Study of Telomere Length Measured by Monochrome Multiplex Quantitative PCR and Risk of Non-Hodgkin Lymphoma

Purpose: Telomere length plays an important role in the maintenance of chromosomal stability and in tumorigenesis. We hypothesized that telomere length in peripheral WBC DNA obtained from healthy individuals would be a predictor of future risk of developing non-Hodgkin lymphoma. Experimental Design: Using a new assay to measure relative telomere length, monochrome multiplex quantitative PCR, which strongly correlates with telomere length measured by Southern blot (Spearman r = 0.91, P < 0.0001) and has high precision (coefficient of variation = 7%), we compared telomere length in peripheral WBC DNA in 107 incident male non-Hodgkin lymphoma cases and 107 matched controls within the prospective Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Results: Median (10th, 90th percentile) telomere length was 1.10 (0.79, 1.43) in cases and 1.02 (0.78, 1.26) in controls (P = 0.0017, Wilcoxon sign test). There was a strong dose-response relationship between quartiles of telomere length and risk of non-Hodgkin lymphoma overall [odds ratios (95% confidence intervals) by quartile: 1.0; 1.1 (0.4-2.7); 1.8 (0.7-4.9); and 3.6 (1.4-8.9); P trend = 0.003], and this association was similar across the most common non-Hodgkin lymphoma subtypes present in this study. Conclusion: These results suggest that longer telomere length may be a potential predictor for future risk of non-Hodgkin lymphoma. (Clin Cancer Res 2009;15(23):7429–33)
Transcriptome Analysis Reveals the Same 17 S-Locus F-Box Genes in Two Haplotypes of the Self-Incompatibility Locus of Petunia in fl ata

Justin S. Williams,a Joshua P. Der,b Claude W. dePamphilis,b,c and Teh-hui Kaoa,c,1 a Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania 16802 bDepartment of Biology, Pennsylvania State University, University Park, Pennsylvania 16802 c Intercollege Graduate Degree Program in Plant Biology, Pennsylvania State University, University Park, Pennsylvania 16802
A Prerecognition Model for Hot Topic Discovery Based on Microblogging Data

The microblogging is prevailing since its easy and anonymous information sharing at Internet, which also brings the issue of dispersing negative topics, or even rumors. Many researchers have focused on how to find and trace emerging topics for analysis. When adopting topic detection and tracking techniques to find hot topics with streamed microblogging data, it will meet obstacles like streamed microblogging data clustering, topic hotness definition, and emerging hot topic discovery. This paper schemes a novel prerecognition model for hot topic discovery. In this model, the concepts of the topic life cycle, the hot velocity, and the hot acceleration are promoted to calculate the change of topic hotness, which aims to discover those emerging hot topics before they boost and break out. Our experiments show that this new model would help to discover potential hot topics efficiently and achieve considerable performance.
Individually and Combined Water-Based Exercise With Ginger Supplement, on Systemic Inflammation and Metabolic Syndrome Indices, Among the Obese Women With Breast Neoplasms

Background: Breast neoplasms has known as the most common cancer among the women worldwide, and relationship between obesity, metabolic syndrome, inflammation and cancer has been recognized since many years ago. Objectives: The aim of this study was to determine the individual and concomitant effect of 6-weeks water-based exercise and oral ginger supplement on markers that have related to metabolic syndrome and systemic inflammation in obese women with breast neoplasms. Patients and Methods: Forty women whose have diagnosed with breast neoplasms have volunteered to participate in the study. Subjects have randomly assigned into four groups; placebo, exercise training, ginger supplement and exercise training+ ginger supplement groups. Subjects in the ginger supplement group and the exercise training+ ginger supplement group have orally received 4 capsules, 7 days a week and for 6 weeks. The water-based exercise training program have collected at a progressive intensity and time, have ranged from 50% to 75% of heart rate reserve, in a pool, 4 times a week for 6 weeks. Fasting blood sampling has collected at the pretest and post-test. Results: The ginger supplementation and the water-base exercise have resulted in a reduction of hs-CRP, IL-10, insulin, glucose, insulin resistance, LDL-C, TG; but an increase in HDL-C and HDL-C/LDL-C. The water-base exercise and ginger supplement group have significantly shown larger positive effect in all outcomes, in comparison with the water-base exercise or ginger supplement alone groups. Conclusions: Findings have suggested that obese breast neoplasms survivors have commonly shown metabolic syndrome and elevated inflammation, which placed them at an increased risk for cardiovascular diseases. Moreover, data has indicated a protective effect of the nondrug strategies, such as water-base exercise and ginger supplementation have played an important role in pathogenesis of inflammatory and metabolic responses, among diagnosed breast neoplasms.
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces

A new access method, called M-tree, is proposed to organize and search large data sets from a generic “metric space”, i.e. where object proximity is only defined by a distance function satisfying the positivity, symmetry, and triangle inequality postulates. We detail algorithms for insertion of objects and split management, which keep the M-tree always balanced - several heuristic split alternatives are considered and experimentally evaluated. Algorithms for similarity (range and k-nearest neighbors) queries are also described. Results from extensive experimentation with a prototype system are reported, considering as the performance criteria the number of page I/O’s and the number of distance computations. The results demonstrate that the Mtree indeed extends the domain of applicability beyond the traditional vector spaces, performs reasonably well in high-dimensional data spaces, and scales well in case of growing files.
Examining the cognitive theory of depression using Descriptive Experience Sampling

Examining the Cognitive Theory of Depression Using Descriptive Experience Sampling by Jedidiah Gunter Dr. Christopher Heavey Professor of Psychology University o f Nevada, Las Vegas The goal of this study was to examine the experience of depressed and nondepressed individuals, exploring the extent to which the specific constructs identified in Beck’s cognitive theory of depression were differentially reflected in the momentary experiences of depressed individuals. This involved recruiting depressed and nondepressed individuals and then assessing their inner experience using both the traditional questionnaire measures developed in accord with Beck’s theory and Descriptive Experience Sampling. Depressed participants showed substantial variability in the nature of their inner experience, had less frequent inner speech and more feelings than nondepressed participants. Salient characteristics found in the depressed participants’ sampled moments were depressive content, depressive feelings, anxiety content, anxiety feelings, and a propensity for overall negative experiences. Participants’ overall scores on the questionnaires correlated with the corresponding rated construct on the following: depressive content, the cognitive triad, and automatic thoughts. Further analyses suggested that these constructs were highly intercorrelated.
Inducing Content Based User Models with Inductive Logic Programming Techniques

In this paper we describe an approach for conceptual user modeling as realized in the OySTER meta web search engine. Instead of collaboratively modeling user interests we use web document classi cations in order to describe individual user models. Information relevance is expressed with respect to an underlying ontology of text categories. Logic expressions over semi{lattices are interpreted as horn clauses| thus allowing to prove di erent levels of interestingness. Furthermore, this approach presents a well{de ned learning problem for inductive logic programming which yields inspectable user models that include sets of interest aspects. By using both explicit positive and negative feedback for both interest and explicit dis{interest we can use few examples to generate a larger set of labeled data for the learning task.
Personal Data Management with the Databox: What's Inside the Box?

We are all increasingly the subjects of data collection and processing systems that use data generated both about and by us to provide and optimise a wide range of services. Means for others to collect and process data that concerns each of us -- often referred to possessively as "your data" -- are only increasing with the long-heralded advent of the Internet of Things just the latest example. As a result, means to enable personal data management is generally recognised as a pressing societal issue. We have previously proposed that one such means might be realised by the Databox, a collection of physical and cloud-hosted software components that provide for an individual data subject to manage, log and audit access to their data by other parties. In this paper we elaborate on this proposal, describing the software architecture we are developing, and the current status of a prototype implementation. We conclude with a brief discussion of Databox's limitations.
Detangling capital from context: a critical investigation of human capital explanations for immigrant wage inequality

ABSTRACT Researchers propose that novel measures of human capital, such as place of education or sending country GDP, can account for inequalities between foreign- and native-born persons that classic measures of human capital, such as years of education, leave unexplained. However, interpreting the relationship between these novel measures and outcomes of interest is challenging. In Western countries, variables such as sending country GDP, which are intended to assess the effects of human capital quality on earnings, may instead capture the effects high levels of pre-migration acculturation among migrants from developed countries, or natives’ positive attitudes towards white migrants. To avoid these problematic collinearities between human capital on one hand, and native attitudes on the other, I analyse data from Japan, where immigrants are not simultaneously advantaged or disadvantaged in terms of their level of acculturation, their human capital quality, and the attitudes that natives hold towards their group. In a setting where collinearities with native attitudes are attenuated, I find little evidence that novel measures of human capital predict higher earnings. The findings imply that previous research overstates the explanatory power of these novel measures of human capital, and potentially understates the impact of native attitudes.
Evaluating the effort involved in relevance assessments for images

How assessors and end users judge the relevance of images has been studied in information science and information retrieval for a considerable time. The criteria by which assessors' judge relevance has been intensively studied, and there has been a large amount of work which has investigated how relevance judgments for test collections can be more cheaply generated, such as through crowd sourcing. Relatively little work has investigated the process individual assessors go through to judge the relevance of an image. In this paper, we focus on the process by which relevance is judged for images, and in particular, the degree of effort a user must expend to judge relevance for different topics. Results suggest that topic difficulty and how semantic/visual a topic is impact user performance and perceived effort.