Miracle-dz/UniFAR-FTUs huggingface dataset:
Change in Population Prevalences of Human Papillomavirus after Initiation of Vaccination: The High-Throughput HPV Monitoring Study

Background: Organized human papillomavirus (HPV) vaccination was introduced in Sweden in 2012. On-demand vaccination was in effect from 2006 to 2011. We followed the HPV prevalences in Southern Sweden from 2008 to 2013. Methods: Consecutive, anonymized samples from the Chlamydia trachomatis screening were analyzed for HPV DNA for two low-risk types and 14 high-risk types using PCR with genotyping using mass spectrometry. We analyzed 44,146 samples in 2008, 5,224 in 2012, and 5,815 in 2013. Results: Registry-determined HPV vaccination coverages of the population in Southern Sweden increased mainly among 13- to 22-year-old women. Most analyzed samples contained genital swabs from women and the HPV6 prevalence in these samples decreased from 7.0% in 2008 to 4.2% in 2013 [−40.0%; P < 0.0005 (χ2 test)]. HPV16 decreased from 14.9% to 8.7% (−41.6%; P < 0.0005) and HPV18 decreased from 7.9% to 4.3% (−45.6%; P < 0.0005) among 13- to 22-year-old women. There were only small changes in vaccination coverage among 23- to 40-year-old women. In this age group, HPV18 decreased marginally (−19.6%; P = 0.04) and there were no significant changes for HPV6 or HPV16. Two nonvaccine HPV types (HPV52 and HPV56) were increased among 13- to 22-year-old women, both in 2012 and 2013. Conclusions: A major reduction of HPV6, 16, and 18 prevalences is seen in the age groups with a concomitant increase in HPV vaccination coverage. The minor changes seen for nonvaccine types will require further investigation. Impact: Monitoring of type-specific HPV prevalences may detect early effects of HPV vaccination. Cancer Epidemiol Biomarkers Prev; 23(12); 2757–64. ©2014 AACR.
Correlating Speaker Gestures in Political Debates with Audience Engagement Measured via EEG

We hypothesize that certain speaker gestures can convey significant information that are correlated to audience engagement. We propose gesture attributes, derived from speakers' tracked hand motions to automatically quantify these gestures from video. Then, we demonstrate a correlation between gesture attributes and an objective method of measuring audience engagement: electroencephalography (EEG) in the domain of political debates. We collect 47 minutes of EEG recordings from each of 20 subjects watching clips of the 2012 U.S. Presidential debates. The subjects are examined in aggregate and in subgroups according to gender and political affiliation. We find statistically significant correlations between gesture attributes (particularly extremal pose) and our feature of engagement derived from EEG both with and without audio. For some stratifications, the Spearman rank correlation reaches as high as rho = 0.283 with p < 0.05, Bonferroni corrected. From these results, we identify those gestures that can be used to measure engagement, principally those that break habitual gestural patterns.
Legal Issues and Ethical Dilemmas in Respiratory Care

Spend your few moment to read a book even only few pages. Reading book is not obligation and force for everybody. When you don't want to read, you can get punishment from the publisher. Read a book becomes a choice of your different characteristics. Many people with reading habit will always be enjoyable to read, or on the contrary. For some reasons, this legal issues and ethical dilemmas in respiratory care tends to be the representative book in this website.
Parallel verification of UML using DiVinE tool

Unified Modeling Language is being used for modeling of business processes, real-time systems, embedded systems and hybrid systems. In software engineering, there are different techniques for verification of such systems. Model checking is one of the techniques for the automatic verification of software models. Earlier, these model were verified by sequential model checking tools. For very large complex systems, parallel verification can save verification time. DiVinE is a model checking tool for both CPUs and GPUs clusters to perform parallel linear time logic (LTL) verification of systems. In this paper, we propose a parallel verification methodology for UML models using DiVinE tool. We explain this methodology by the case study example of fuel filling machine.
Guillain-Barré syndrome and influenza virus infection.

BACKGROUND In Western countries, the cause of 60% of all Guillain-Barré syndrome (GBS) cases remains unidentified. The number of cases of unidentified cause peaks in winter, and these cases are commonly preceded by respiratory tract infection or influenza-like illness. We investigated the triggering role of influenza virus infection. METHODS Of 405 patients with GBS who were admitted to a French reference center during 1996-2004, 234 had cases caused by an unidentified agent. We used time-series methods to study the correlation between the monthly incidence of such cases and influenza-like illnesses reported by the Sentinelles surveillance network. We analyzed anti-influenza antibodies using complement fixation testing and hemagglutination-inhibition assays. We studied etiological subgroups using Wilcoxon and Fisher's exact tests. RESULTS We found a positive association between the monthly incidence of GBS caused by an unidentified agent and reported influenza-like illnesses. Of 73 patients whose cases occurred during periods in which there was a possible link to influenza, 10 (13.7%) had serological evidence of recent influenza A, and 4 (5.5%) had serological evidence of influenza B. Eight of 10 influenza A-related cases occurred during "major" influenza seasons, and antibodies specific to the current epidemic strain were found in 9 cases. Most patients with influenza A-related cases were aged < 65 years, and none had antiganglioside antibodies. Influenza-related cases differed both from Campylobacter jejuni-related cases, with regard to the lack of need for mechanical ventilation (P = .014), and from the cases caused by an unidentified agent, with regard to the presence of preceding influenza-like illness or respiratory tract infection (P = .015) and longer time from the infectious event to GBS onset (P = .04). CONCLUSIONS Influenza viruses are infrequent triggering agents of GBS but may play a significant role during major influenza outbreaks. Influenza-related GBS displays specific features and is not associated with antiganglioside antibody response, which suggests the presence of underlying immune mechanisms.
Outage Analysis of Cooperative CDMA Systems in Nakagami- $m$ Fading Channels

The performance of uplink cooperative code-division multiple-access (CDMA) systems using adaptive decode-and-forward (DF) relaying is investigated over Nakagami-m fading channels. A closed-form expression for the outage probability for a multirelay system with relay selection is derived. Our method is based on the moment-generating function (MGF) for the total signal-to-noise ratio (SNR) at the base station where the cumulative density function (cdf) is obtained. We also examine the asymptotic performance of the system at high SNR from which we evaluate the achievable diversity gain for different system parameters. Both simulations and analytical results are presented for performance evaluation.
Mobility Systems and Economic Growth: a Theoretical Analysis of the Long-Term Effects of Alternative Transportation Policies

We present an example of how public policies affect the evolution of the economy by influencing consumption habits, life styles and work attitudes. In particular, we show that governments can boost long-run growth by moving public investment away from collective transportation systems and towards infrastructures necessary for using private vehicles. Indeed, by augmenting the relative convenience of using private mobility systems, which are those more costly for the households, the government induces them to increase their labor supply so as to afford larger expenditures in transportation. This has long-term welfare implications depending also on the negative externalities associated with transport.
Studying Positive Speech on Twitter

We present results of empirical studies on positive speech on Twitter. By positive speech we understand speech that works for the betterment of a given situation, in this case relations between different communities in a conflict-prone country. We worked with four Twitter data sets. Through semi-manual opinion mining, we found that positive speech accounted for < 1% of the data . In fully automated studies, we tested two approaches: unsupervised statistical analysis, and supervised text classification based on distributed word representation. We discuss benefits and challenges of those approaches and report empirical evidence obtained in the study.
Vector-based Sentiment Analysis of Movie Reviews

Sentiment analysis is an important step towards comprehension in natural language processing. Movie reviews are a convenient source of highly polarized sentences for use in sentiment analysis. Achieving a high level of accuracy in the sign of nonneutral sentiment is a challenge. When the problem is expanded to choosing one of 5 sentiment levels the problem becomes significantly harder. Among the models that we tested, softmax regression with a bag of phrases feature set provided the best 5-bin error rate, while SVM with the same bag of phrases features and a simple word sentiment sum model provided the best error rate on sign prediction.
Very luminous carbon stars in the outer disk of the Triangulum spiral galaxy

Stars with masses in the range from about 1.3 to 3.5 M ○. pass through an evolutionary stage where they become carbon stars. In this stage, which lasts a few Myr, these stars are extremely luminous pulsating giants. They are so luminous in the near-infrared that just a few of them can double the integrated luminosity of intermediate-age (0.6 to 2 Gyr) Magellanic Cloud clusters at 2.2 microns. Astronomers routinely use such near-infrared observations to minimize the effects of dust extinction, but it is precisely in this band that carbon stars can contribute hugely. The actual contribution of carbon stars to the outer disk light of evolving spiral galaxies has not previously been morphologically investigated. Here we report new and very deep near-IR images of the Triangulum spiral galaxy M 33 = NGC 598, delineating spectacular arcs of carbon stars in its outer regions. It is these arcs which dominate the near-infrared m = 2 Fourier spectra of M 33. We present near-infrared photometry with the Hale 5-m reflector, and propose that the arcs are the signature of accretion of low metallicity gas in the outer disk of M 33.
A Hybrid Approach for Tissue Recognition on Wound Images

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear o friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task for optimizing the effectiveness of treatments and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. In this article, a hybrid approach based on neural networks and Bayesian classifiers is used in the designing of a computational system for automatic tissue identification on wound images. A mean shift procedure and a region-growing strategy are implemented for effective region segmentation. Color and texture features are extracted from the segmented regions. A set of k multi-layer perceptrons are trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes which are determined by clinical experts. This training procedure is driven by a k-fold cross-validation method. Finally, a Bayesian committee machine is formed by training a Bayesian classifier to combine the classifications of the k neural networks. Our outcomes show high performance scores of a two-stage cascade approach for tissue identification.
A Cost-Effective Method for Providing Improved Data Availability During DBMS Restart Recovery After a Failure

We present a cost-effective method for improving data availability during restart recovery of a data base management system (DBMS) after a failure. The method achieves its objective by enabling the processing of new transactions to begin even before restart recovery is completed by exploiting the Comnlt-rs~V concept. It supports finegranularity (e.g., record) locking with semanticallyrich lock modes and operation logging, partial rollbacks, write-ahead logging, and the steal and no-force buffer management policies. The overhead imposed by this method during normal transaction processing is insignificant. We require very few changes to an existing DBMS in order to support our method. Our method can be implemented with different degrees of sophistication depending on the existing features of a DBMS.
Playing PISA with bebras

To test and adopt the methodology of future PISA surveys in Computer Science, we have utilized the Bebras Contest as a test field. We performed latent trait analysis of the questions of the German Bebras contest 2009 to find sets of questions that might measure joint psychometric constructs (competencies). After having identified several sets, we have tested the fit of the mono-parametric Rasch Model and evaluated the distribution of person parameters.
An information extraction approach for spoken language understanding

This paper presents an Information Extraction (IE) approach for spoken language understanding. The goal in IE is to find proper values for pre-defined slots of given templates. IE for spoken language understanding proposes a concept spotting approach for spoken language because IE approach is interested in only pre-defined concept slots. In spite of this partial understanding, we can acquire necessary information for an application from the values of pre-defined slots because the slots are properly designed for speech understanding in a specific domain. Spoken language has so many recognition errors especially in a poor environment so it is more difficult to understand than textual language. Considering this fact, we attempt to understand the languages by concentrating on the specified information. In experiments on the car navigation domain, F-measure for concept spotting for textual input (WER 0%) and spoken input (WER 39%) are 96.33% and 78.30% respectively.
Ternary SRAM for low power applications

Design and Performance verification of Ternary CMOS SRAM is presented in this paper. Ternary SRAM is designed in 180nm, 90nm & 65nm technology process. The Ternary SRAM cell consists of two cross coupled Ternary inverters. READ and WRITE operations of the Ternary SRAM cell are performed with the help of Sense Amplifier, Tritline Conditioning circuits and Fast Decoders using TSPICE. The proposed work can be used for Low Power Application as the Fast Decoders use less number of Transistors compared to the conventional Decoders. The Ternary SRAM array module (1X1) in 65 nm technology consumes only 0.608mW power and data access time is about 9.88ns.
omparative Study on Discrete Orthonormal Chebyshev Moments and

Moment functions are widely used in image analysis as feature descriptors. Compared to geometric moments, orthogonal moments have become more popular in image analysis for their better representation capabilities. In comparison to continuous orthogonal moments discrete orthogonal moments provide a more accurate description of the image features. This paper compares the performance of discrete orthonormal Chebyshev moments with continuous orthogonal Legendre moments for representation of noisy printed characters of the Assamese alphabet. Experimental results show that at all noise levels Chebyshev moments outperforms Legendre moments. The performance of both the moments increases when higher order moments are considered but after some point the performance of Chebyshev moments decreases.
Methanolic Extracts of Bitter Melon Inhibit Colon Cancer Stem Cells by Affecting Energy Homeostasis and Autophagy

Bitter melon fruit is recommended in ancient Indian and Chinese medicine for prevention/treatment of diabetes. However its effects on cancer progression are not well understood. Here, we have determined the efficacy of methanolic extracts of bitter melon on colon cancer stem and progenitor cells. Both, whole fruit (BMW) and skin (BMSk) extracts showed significant inhibition of cell proliferation and colony formation, with BMW showing greater efficacy. In addition, the cells were arrested at the S phase of cell cycle. Moreover, BMW induced the cleavage of LC3B but not caspase 3/7, suggesting that the cells were undergoing autophagy and not apoptosis. Further confirmation of autophagy was obtained when western blots showed reduced Bcl-2 and increased Beclin-1, Atg 7 and 12 upon BMW treatment. BMW reduced cellular ATP levels coupled with activation of AMP activated protein kinase; on the other hand, exogenous additions of ATP lead to revival of cell proliferation. Finally, BMW treatment results in a dose-dependent reduction in the number and size of colonospheres. The extracts also decreased the expression of DCLK1 and Lgr5, markers of quiescent, and activated stem cells. Taken together, these results suggest that the extracts of bitter melon can be an effective preventive/therapeutic agent for colon cancer.
The Social organization of schools : new conceptualizations of the learning process

1 The Social Organization of Schools: An Overview. - 2 An Organizational Analysis of Curriculum and Instruction. - 3 Ability Grouping and Student Learning. - 4 Diversity, Equity, and Classroom Processes. - 5 The Organizational Differentiation of Students in Schools as an Opportunity Structure. - 6 Evaluating the Trade-offs in Student Outcomes from Alternative School Organization Policies. - 7 Implications of an Institutional View of Education for the Study of Educational Effects. - 8 The Relations between School and Social Structure. - 9 Moral Education and School Social Organization. - Author Index.
Vehicle position estimation using geometric constants in traffic scene

Determination of the correct positional relation is vital for human driver. For unmanned vehicles, the obstacle position in front of the view is also necessary for collision detection. The paper is devoted to the problem that estimates the vehicle position in real world using only a single 2D image. The estimation is an ill-posed problem due to the projective transform; however, through incorporating the geometric constants in the traffic scene, we proposed a solution that calculates the position with sufficient accuracy. The contribution of the proposed method is the use of two geometric constants: the standard size of license plate and the lane-width. Observation shows that the size of license plates has limited patterns and lane-width between two adjacent lanes is usually constant. Introduction of the two constants compensates the uncertainty caused by lack of depth in the mapping between the detected license plates and its position in real world. The conducted experiments show that compared to the conventional methods, the proposed one is accurate for estimating the position.
Fundamentals of crisis-proof economics

The major problem of the current market economy is absence of appropriate mathematical description of processes taking place in it, which results in misunderstanding of these processes, as well as lack of efficient means of preventing economic crises. The manuscripts suggests such a mathematical description, making use of the circumstance that the 'black box' of market economy has the corresponding 'white box', which turned out to be the processes well known in radio-electronics. This approach allowed proving that the current market economy, due to the significant influence of the human factor, is described with parametric differential equations with random coefficients, the solutions of which are also random functions of time. Therefore, it is basically unpredictable and uncontrollable, which makes economic crises inevitable. In order for the economy to become crisis-proof, it has to be reformed. We suggest using the new economic tools which allow solving the problem. Here belong business-interfaces that can neutralize the internal human factor, and the new global/regional information network TV
NTNUiS at the TREC 2014 Federated Web Search Track

This paper describes our participa- tion in the Federated Web Search track at TREC 2014. For the resource selection task we employ a learning-to-rank approach to combine various (in- stantiations of) resource ranking models. For the vertical selection task we treat the estimated col- lection relevance scores as binary judgements.
Service Recommendation Method on Multiple Dimension O2O

The concept of O2O caught great interests from operators and researchers. Although O2O brings great opportunity to the market, the chaos and lack of credit make big problems to the thrive of O2O. The context and preference clustering model is proposed in this paper to study the affection of both customer context and service preference. The proposed model aims to realize a clustering method for both sides, looking for a service recommendation strategy. Test results showed that the proposed assumptions worked well in the model.