959 resultados para Single-grade classes


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Objective Ankylosing spondylitis (AS) is a highly heritable common inflammatory arthritis that targets the spine and sacroiliac joints of the pelvis, causing pain and stiffness and leading eventually to joint fusion. Although previous studies have shown a strong association of IL23R with AS in white Europeans, similar studies in East Asian populations have shown no association with common variants of IL23R, suggesting either that IL23R variants have no role or that rare genetic variants contribute. The present study was undertaken to screen IL23R to identify rare variants associated with AS in Han Chinese. Methods A 170-kb region containing IL23R and its flanking regions was sequenced in 50 patients with AS and 50 ethnically matched healthy control subjects from a Han Chinese population. In addition, the 30-kb region of peak association in white Europeans was sequenced in 650 patients with AS and 1,300 healthy controls. Validation genotyping was undertaken in 846 patients with AS and 1,308 healthy controls. Results We identified 1,047 variants, of which 729 were not found in the dbSNP genomic build 130. Several potentially functional rare variants in IL23R were identified, including one nonsynonomous single-nucleotide polymorphism (nsSNP), Gly149Arg (position 67421184 GA on chromosome 1). Validation genotyping showed that the Gly149Arg variant was associated with AS (odds ratio 0.61, P = 0.0054). Conclusion This is the first study to implicate rare IL23R variants in the pathogenesis of AS. The results identified a low-frequency nsSNP with predicted loss-of-function effects that was protectively associated with AS in Han Chinese, suggesting that decreased function of the interleukin-23 (IL-23) receptor protects against AS. These findings further support the notion that IL-23 signaling has an important role in the pathogenesis of AS.

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By the time students reach the middle years they have experienced many chance activities based on dice. Common among these are rolling one die to explore the relationship of frequency and theoretical probability, and rolling two dice and summing the outcomes to consider their probabilities. Although dice may be considered overused by some, the advantage they offer is a familiar context within which to explore much more complex concepts. If the basic chance mechanism of the device is understood, it is possible to enter quickly into an arena of more complex concepts. This is what happened with a two hour activity engaged in by four classes of Grade 6 students in the same school. The activity targeted the concepts of variation and expectation. The teachers held extended discussions with their classes on variation and expectation at the beginning of the activity, with students contributing examples of the two concepts from their own experience. These notions are quite sophisticated for Grade 6, but the underlying concepts describe phenomena that students encounter every day. For example, time varies continuously; sporting results vary from game to game; the maximum temperature varies from day to day. However, there is an expectation about tomorrow’s maximum temperature based on the expert advice from the weather bureau. There may also be an expectation about a sporting result based on the participants’ previous results. It is this juxtaposition that makes life interesting. Variation hence describes the differences we see in phenomena around us. In a scenario displaying variation, expectation describes the effort to characterise or summarise the variation and perhaps make a prediction about the message arising from the scenario. The explicit purpose of the activity described here was to use the familiar scenario of rolling a die to expose these two concepts. Because the students had previously experienced rolling physical dice they knew instinctively about the variation that occurs across many rolls and about the theoretical expectation that each side should “come up” one-sixth of the time. They had observed the instances of the concepts in action, but had not consolidated the underlying terminology to describe it. As the two concepts are so fundamental to understanding statistics, we felt it would be useful to begin building in the familiar environment of rolling a die. Because hand-held dice limit the explorations students can undertake, the classes used the soft-ware TinkerPlots (Konold & Miller, 2011) to simulate rolling a die multiple times.

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Mobile devices are very popular among tertiary student populations. This study looks at student use of hand-held mobile devices within the context of a first year programming unit. This research sought for ways in which an educational app on these devices could be successfully integrated into such a class's learning.

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Background We have previously demonstrated that circulating NT-proBNP is truncated at the N and C termini. Aims of this study are three-fold: firstly to determine whether the NT-proBNP levels correlate with NYHA functional classes when measuring with different antibody pairs; secondly to evaluate the diagnostic potential of ProBNP and; thirdly to investigate whether combining NT-proBNP assays with or without ProBNP would lead to better diagnostic accuracies. Methods Plasma samples were collected from healthy controls (n = 52) and HF patients (n = 46). Customized AlphaLISA® immunoassays were developed and validated to measure the concentrations of proBNP and NT-proBNP (with antibodies targeting 13–45, 13–76, 28–76). The diagnostic performance and predictive value of proBNP and NT-proBNP assays and their combinations were evaluated. Results Plasma proBNP assay showed acceptable diagnostic performance. NT-proBNP13–76 assay is useful in diagnosing and stratifying HF patients. The diagnostic performance of NT-proBNP13–76 demonstrated improvement over commercial NT-proBNP tests. The combination of NT-proBNP13–76 with NT-proBNP28–76 assays gave the best diagnostic assay performance. Conclusion Our results demonstrate that while there is major heterogeneity in circulating NT-proBNP, specific epitopes of the peptides are extraordinarily stable, providing ideal targets for clinically useful diagnostic assays. Future new clinical diagnostic clinical trials should include a multimarker approach rather than using a single marker to diagnose HF.

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The paper presents an improved Phase-Locked Loop (PLL) for measuring the fundamental frequency and selective harmonic content of a distorted signal. This information can be used by grid interfaced devices and harmonic compensators. The single-phase structure is based on the Synchronous Reference Frame (SRF) PLL. The proposed PLL needs only a limited number of harmonic stages by incorporating Moving Average Filters (MAF) for eliminating the undesired harmonic content at each stage. The frequency dependency of MAF in effective filtering of undesired harmonics is also dealt with by a proposed method for adaptation to frequency variations of input signal. The method is suitable for high sampling rates and a wide frequency measurement range. Furthermore, an extended model of this structure is proposed which includes the response to both the frequency and phase angle variations. The proposed algorithm is simulated and verified using Hardware-in-the-Loop (HIL) testing.

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The issue of single-cell control has recently attracted enormous interest. However, in spite of the presently achievable intracellular-level physiological probing through bio-photonics, nano-probe-based, and some other techniques, the issue of inducing selective, single-cell-precision apoptosis, without affecting neighbouring cells remains essentially open. Here we resolve this issue and report on the effective single-cell-precision cancer cell treatment using the reactive chemistry of the localized corona-type plasma discharge around a needle-like electrode with the spot size ∼1 µm. When the electrode is positioned with the micrometer precision against a selected cell, a focused and highly-localized micro-plasma discharge induces apoptosis in the selected individual HepG2 and HeLa cancer cells only, without affecting any surrounding cells, even in small cell clusters. This is confirmed by the real-time monitoring of the morphological and structural changes at the cellular and cell nucleus levels after the plasma exposure.

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It is difficult to determine sulfur-containing volatile organic compounds in the atmosphere because of their reactivity. Primary off-line techniques may suffer losses of analytes during the transportation from field to laboratory and sample preparation. In this study, a novel method was developed to directly measure dimethyl sulfide at parts-per-billion concentration levels in the atmosphere using vacuum ultraviolet single photon ionization time-of-flight mass spectrometry. This technique offers continuous sampling at a response rate of one measurement per second, or cumulative measurements over longer time periods. Laboratory prepared samples of different concentrations of dimethyl sulfide in pure nitrogen gas were analyzed at several sampling frequencies. Good precision was achieved using sampling periods of at least 60 seconds with a relative standard deviation of less than 25%. The detection limit for dimethyl sulfide was below the 3 ppb olfactory threshold. These results demonstrate that single photon ionization time-of-flight mass spectrometry is a valuable tool for rapid, real-time measurements of sulfur-containing organic compounds in the air.

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From the onset of the first microscopic visualization of single fluorescent molecules in living cells at the beginning of this century, to the present, almost routine application of single molecule microscopy, the method has well-proven its ability to contribute unmatched detailed insight into the heterogeneous and dynamic molecular world life is composed of. Except for investigations on bacteria and yeast, almost the entire story of success is based on studies on adherent mammalian 2D cell cultures. However, despite this continuous progress, the technique was not able to keep pace with the move of the cell biology community to adapt 3D cell culture models for basic research, regenerative medicine, or drug development and screening. In this review, we will summarize the progress, which only recently allowed for the application of single molecule microscopy to 3D cell systems and give an overview of the technical advances that led to it. While initially posing a challenge, we finally conclude that relevant 3D cell models will become an integral part of the on-going success of single molecule microscopy.

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Many protocols have been used for extraction of DNA from Thraustochytrids. These generally involve the use of CTAB, phenol/chloroform and ethanol. They also feature mechanical grinding, sonication, N2 freezing or bead beating. However, the resulting chemical and physical damage to extracted DNA reduces its quality. The methods are also unsuitable for large numbers of samples. Commercially-available DNA extraction kits give better quality and yields but are expensive. Therefore, an optimized DNA extraction protocol was developed which is suitable for Thraustochytrids to both minimise expensive and time-consuming steps prior to DNA extraction and also to improve the yield. The most effective method is a combination of single bead in TissueLyser (Qiagen) and Proteinase K. Results were conclusive: both the quality and the yield of extracted DNA were higher than with any other method giving an average yield of 8.5 µg/100 mg biomass.

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The enhanced large-scale model and numerical simulations are used to clarify the growth mechanism and the differences between the plasma- and neutral gas-grown carbon nanotubes, and to reveal the underlying physics and the key growth parameters. The results show that the nanotubes grown by plasma can be longer due to the effects of hydrocarbon ions with velocities aligned with the nanotubes. We show that the low-temperature growth is possible when the hydrocarbon ion flux dominates over fluxes of other species. We have also analysed the dependencies of the nanotube growth rates on nanotube and process parameters. The results are verified by a direct comparison with the experimental data. The model is generic and can be used for other types of carbon nanostructures such as carbon nanowalls, vertical graphenes, etc.

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In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.

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Crashes at any particular transport network location consist of a chain of events arising from a multitude of potential causes and/or contributing factors whose nature is likely to reflect geometric characteristics of the road, spatial effects of the surrounding environment, and human behavioural factors. It is postulated that these potential contributing factors do not arise from the same underlying risk process, and thus should be explicitly modelled and understood. The state of the practice in road safety network management applies a safety performance function that represents a single risk process to explain crash variability across network sites. This study aims to elucidate the importance of differentiating among various underlying risk processes contributing to the observed crash count at any particular network location. To demonstrate the principle of this theoretical and corresponding methodological approach, the study explores engineering (e.g. segment length, speed limit) and unobserved spatial factors (e.g. climatic factors, presence of schools) as two explicit sources of crash contributing factors. A Bayesian Latent Class (BLC) analysis is used to explore these two sources and to incorporate prior information about their contribution to crash occurrence. The methodology is applied to the state controlled roads in Queensland, Australia and the results are compared with the traditional Negative Binomial (NB) model. A comparison of goodness of fit measures indicates that the model with a double risk process outperforms the single risk process NB model, and thus indicating the need for further research to capture all the three crash generation processes into the SPFs.

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Background Health risk behavior among young people is a public health problem in Vietnam. In addition, road traffic injuries are the leading cause of death for those aged 15–29 years. The consequences can be devastating for adolescents and their families, and can create a significant economic burden on society. Objective: The aim of this study was to identify protective and risk factors that may influence three health risk behaviors among school children: suicidal thinking (ST), drinking alcohol (DA), and underage motorbike driving (MD). Methods A cross-sectional survey of 972 adolescents (aged 12–15 years) was conducted in two secondary schools in Hanoi, Vietnam. The schools were purposely selected, one each from the inner city and a suburban area, from which classes (grade 6 to 8) were randomly selected. All students attending classes on survey days took part in the survey. The anonymous, self-completed questionnaire included measures of risk behavior, school connectedness, parental bonding, and other factors. Multivariable regression models were used to examine associations between the independent variables and the three health risk behaviors controlling for confounding factors. Results Young people in the inner city school reported a higher prevalence of all three risk behaviors than those in the suburban area (ST: 16.1% [95% confidence interval, or CI, 12.9–19.3] versus 4.6% [95% CI 2.7–6.5], p<0.001; DA: 20.3% [95% CI 16.8–23.8] versus 8.3% [95% CI 5.8–10.8], p<0.001, and MD: 10.1% [95% CI 7.4–12.8] versus 5.7% [95% CI 3.6–7.8], p<0.01). School connectedness and mother and father care appeared to be significant protective factors. For males, bullying in school was associated with suicidal thoughts, whereas for both males and females, school connectedness may be protective against suicidal ideation. Conclusion This study supports findings from other nations regarding suicidal thoughts and alcohol use, and appears to be one of the first to examine risk and protective factors forMD. Health promotion within schools should be introduced to improve students’ feelings of connectedness in combination with communication and education campaigns focusing on parental care and engaging teachers for the promotion of safer, supportive school environments.

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Single nucleotide polymorphisms (SNPs) have been classically used for dissecting various human complex disorders using candidate gene studies. During the last decade, large scale SNP analysis i.e. genome-wide association studies (GWAS) have provided an agnostic approach to identify possible genetic loci associated with heterogeneous disease such as cancer susceptibility, prognosis of survival or drug response. Further, the advent of new technologies, including microarray based genotyping as well as high throughput next generation sequencing has opened new avenues for SNPs to be used in clinical practice. It is speculated that the utility of SNPs to understand the mechanisms, biology of variable drug response and ultimately treatment individualization based on the individual’s genome composition will be indispensable in the near future. In the current review, we discuss the advantages and disadvantages of the clinical utility of genetic variants in disease risk-prediction, prognosis, clinical outcome and pharmacogenomics. The lessons and challenges for the utility of SNP based biomarkers are also discussed, including the need for additional functional validation studies.

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Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed 19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with diseaseIRGM for Crohns disease, HLA for Crohns disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetesalthough in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases. © 2010 Macmillan Publishers Limited. All rights reserved.