Evolutionary relationships between Rhynchosporium lolii sp. nov. and other Rhynchosporium species on grass.

Copyright: 2013 King et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedThe fungal genus Rhynchosporium (causative agent of leaf blotch) contains several host-specialised species, including R. commune (colonising barley and brome-grass), R. agropyri (couch-grass), R. secalis (rye and triticale) and the more distantly related R. orthosporum (cocksfoot). This study used molecular fingerprinting, multilocus DNA sequence data, conidial morphology, host range tests and scanning electron microscopy to investigate the relationship between Rhynchosporium species on ryegrasses, both economically important forage grasses and common wild grasses in many cereal growing areas, and other plant species. Two different types of Rhynchosporium were found on ryegrasses in the UK. Firstly, there were isolates of R. commune that were pathogenic to both barley and Italian ryegrass. Secondly, there were isolates of a new species, here named R. lolii, that were pathogenic only to ryegrass species. R. lolii was most closely related to R. orthosporum, but exhibited clear molecular, morphological and host range differences. The species was estimated to have diverged from R. orthosporum ca. 5735 years before the present. The colonisation strategy of all of the different Rhynchosporium species involved extensive hyphal growth in the sub-cuticular regions of the leaves. Finally, new species-specific PCR diagnostic tests were developed that could distinguish between these five closely related Rhynchosporium species.Peer reviewedFinal Published versio

Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder

<p>Abstract</p> <p>Background</p> <p>Survival analysis methods such as the Kaplan-Meier method, log-rank test, and Cox proportional hazards regression (Cox regression) are commonly used to analyze data from randomized withdrawal studies in patients with major depressive disorder. However, unfortunately, such common methods may be inappropriate when a long-term censored relapse-free time appears in data as the methods assume that if complete follow-up were possible for all individuals, each would eventually experience the event of interest.</p> <p>Methods</p> <p>In this paper, to analyse data including such a long-term censored relapse-free time, we discuss a semi-parametric cure regression (Cox cure regression), which combines a logistic formulation for the probability of occurrence of an event with a Cox proportional hazards specification for the time of occurrence of the event. In specifying the treatment's effect on disease-free survival, we consider the fraction of long-term survivors and the risks associated with a relapse of the disease. In addition, we develop a tree-based method for the time to event data to identify groups of patients with differing prognoses (cure survival CART). Although analysis methods typically adapt the log-rank statistic for recursive partitioning procedures, the method applied here used a likelihood ratio (LR) test statistic from a fitting of cure survival regression assuming exponential and Weibull distributions for the latency time of relapse.</p> <p>Results</p> <p>The method is illustrated using data from a sertraline randomized withdrawal study in patients with major depressive disorder.</p> <p>Conclusions</p> <p>We concluded that Cox cure regression reveals facts on who may be cured, and how the treatment and other factors effect on the cured incidence and on the relapse time of uncured patients, and that cure survival CART output provides easily understandable and interpretable information, useful both in identifying groups of patients with differing prognoses and in utilizing Cox cure regression models leading to meaningful interpretations.</p

Multiple imputation for estimation of an occurrence rate in cohorts with attrition and discrete follow-up time points: a simulation study

<p>Abstract</p> <p>Background</p> <p>In longitudinal cohort studies, subjects may be lost to follow-up at any time during the study. This leads to attrition and thus to a risk of inaccurate and biased estimations. The purpose of this paper is to show how multiple imputation can take advantage of all the information collected during follow-up in order to estimate the cumulative probability <it>P(E) </it>of an event <it>E</it>, when the first occurrence of this event is observed at <it>t </it>successive time points of a longitudinal study with attrition.</p> <p>Methods</p> <p>We compared the performance of multiple imputation with that of Kaplan-Meier estimation in several simulated attrition scenarios.</p> <p>Results</p> <p>In missing-completely-at-random scenarios, the multiple imputation and Kaplan-Meier methods performed well in terms of bias (less than 1%) and coverage rate (range = [94.4%; 95.8%]). In missing-at-random scenarios, the Kaplan-Meier method was associated with a bias ranging from -5.1% to 7.0% and with a very poor coverage rate (as low as 0.2%). Multiple imputation performed much better in this situation (bias <2%, coverage rate >83.4%).</p> <p>Conclusions</p> <p>Multiple imputation shows promise for estimation of an occurrence rate in cohorts with attrition. This study is a first step towards defining appropriate use of multiple imputation in longitudinal studies.</p

Training school teachers to deliver a mindfulness program: exploring scalability, acceptability, effectiveness and cost-effectiveness

This is the final version. Available on open access from SAGE Publications via the DOI in this recordBackground: There is growing research support for the use of mindfulness training (MT) in schools, but almost no high-quality evidence about different training models for people wishing to teach mindfulness in this setting. Effective dissemination of MT relies on the development of scalable training routes. Objective: To compare four training routes for school teachers wishing to deliver MT differing in intensity and potential scalability, considering teaching competency, training acceptability and cost-effectiveness. Methods: Schools were randomised to an existing route comprising an eight-session instructor-led personal mindfulness course, combined with four-day MT program training, or one of three more scalable, lower-intensity, alternatives: an instructor-led personal mindfulness course combined with one-day MT program training; a selftaught personal mindfulness course (delivered through a course book) combined with four-day MT program training and a self-taught personal mindfulness course combined with one-day MT program training. Results: Attrition from training was substantial across all routes. The instructor-led course was more effective than the self-taught course in increasing teachers’ personal mindfulness skills. Even the most intensive (existing) training route brought only 29% of the teachers commencing training, and 56% of those completing the study protocol, to the required minimum competency threshold (an advanced beginner rating on an adapted version of the MBI-TAC). The differences in levels of competency achieved by existing training compared with the more scalable alternatives were modest, with economic evaluation suggesting that the existing route was both more expensive and more effective than lower intensity alternatives, but with no statistically significant differences between routes. Conclusions: This research questions the move towards abbreviating teacher training to increase scalability and suggests instead that many teachers require additional support to ensure competency from first delivery of MT in the classroom.Wellcome Trus

Age at quitting smoking as a predictor of risk of cardiovascular disease incidence independent of smoking status, time since quitting and pack-years

Background: Risk prediction for CVD events has been shown to vary according to current smoking status, pack-years smoked over a lifetime, time since quitting and age at quitting. The latter two are closely and inversely related. It is not known whether the age at which one quits smoking is an additional important predictor of CVD events. The aim of this study was to determine whether the risk of CVD events varied according to age at quitting after taking into account current smoking status, lifetime pack-years smoked and time since quitting. Findings. We used the Cox proportional hazards model to evaluate the risk of developing a first CVD event for a cohort of participants in the Framingham Offspring Heart Study who attended the fourth examination between ages 30 and 74 years and were free of CVD. Those who quit before the median age of 37 years had a risk of CVD incidence similar to those who were never smokers. The incorporation of age at quitting in the smoking variable resulted in better prediction than the model which had a simple current smoker/non-smoker measure and the one that incorporated both time since quitting and pack-years. These models demonstrated good discrimination, calibration and global fit. The risk among those quitting more than 5 years prior to the baseline exam and those whose age at quitting was prior to 44 years was similar to the risk among never smokers. However, the risk among those quitting less than 5 years prior to the baseline exam and those who continued to smoke until 44 years of age (or beyond) was two and a half times higher than that of never smokers. Conclusions: Age at quitting improves the prediction of risk of CVD incidence even after other smoking measures are taken into account. The clinical benefit of adding age at quitting to the model with other smoking measures may be greater than the associated costs. Thus, age at quitting should be considered in addition to smoking status, time since quitting and pack-years when counselling individuals about their cardiovascular risk

The impact of measurement errors in the identification of regulatory networks

<p>Abstract</p> <p>Background</p> <p>There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise.</p> <p>Results</p> <p>This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data.</p> <p>Conclusions</p> <p>Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.</p

Differences in HIV Natural History among African and Non-African Seroconverters in Europe and Seroconverters in Sub-Saharan Africa

Introduction It is unknown whether HIV treatment guidelines, based on resource-rich country cohorts, are applicable to African populations. Methods We estimated CD4 cell loss in ART-naïve, AIDS-free individuals using mixed models allowing for random intercept and slope, and time from seroconversion to clinical AIDS, death and antiretroviral therapy (ART) initiation by survival methods. Using CASCADE data from 20 European and 3 sub-Saharan African (SSA) cohorts of heterosexually-infected individuals, aged ≥15 years, infected ≥2000, we compared estimates between non-African Europeans, Africans in Europe, and Africans in SSA. Results Of 1,959 (913 non-Africans, 302 Europeans - African origin, 744 SSA), two-thirds were female; median age at seroconversion was 31 years. Individuals in SSA progressed faster to clinical AIDS but not to death or non-TB AIDS. They also initiated ART later than Europeans and at lower CD4 cell counts. In adjusted models, Africans (especially from Europe) had lower CD4 counts at seroconversion and slower CD4 decline than non-African Europeans. Median (95% CI) CD4 count at seroconversion for a 15–29 year old woman was 607 (588–627) (non-African European), 469 (442–497) (European - African origin) and 570 (551–589) (SSA) cells/µL with respective CD4 decline during the first 4 years of 259 (228–289), 155 (110–200), and 199 (174–224) cells/µL (p<0.01). Discussion Despite differences in CD4 cell count evolution, death and non-TB AIDS rates were similar across study groups. It is therefore prudent to apply current ART guidelines from resource-rich countries to African populations

Do Adolescents Like School-Based Mindfulness Training? Predictors of Mindfulness Practice and Responsiveness in the MYRIAD Trial

Objective: We explored what predicts secondary school students’ mindfulness practice and responsiveness to universal school-based mindfulness training (SBMT), and how students experience SBMT. Method: A mixed-methods design was used. Participants were 4,232 students (11-13 years of age), in 43 UK secondary schools, who received universal SBMT (ie, “.b” program), within the MYRIAD trial (ISRCTN86619085). Following previous research, student, teacher, school, and implementation factors were evaluated as potential predictors of students’ out-of-school mindfulness practice and responsiveness (ie, interest in and attitudes toward SBMT), using mixed-effects linear regression. We explored pupils’ SBMT experiences using thematic content analysis of their answers to 2 free-response questions, 1 question focused on positive experiences and 1 question on difficulties/challenges. Results: Students reported practicing out-of-school mindfulness exercises on average once during the intervention (mean [SD] = 1.16 [1.07]; range, 0-5). Students’ average ratings of responsiveness were intermediate (mean [SD] = 4.72 [2.88]; range, 0-10). Girls reported more responsiveness. High risk of mental health problems was associated with lower responsiveness. Asian ethnicity and higher school-level economic deprivation were related to greater responsiveness. More SBMT sessions and better quality of delivery were associated with both greater mindfulness practice and responsiveness. In terms of students’ experiences of SBMT, the most frequent themes (60% of the minimally elaborated responses) were an increased awareness of bodily feelings/sensations and increased ability to regulate emotions. Conclusion: Most students did not engage with mindfulness practice. Although responsiveness to the SMBT was intermediate on average, there was substantial variation, with some youth rating it negatively and others rating it positively. Future SBMT developers should consider co-designing curricula with students, carefully assessing the student characteristics, aspects of the school environment, and implementation factors associated with mindfulness practice and responsiveness. SBMT teacher training is key, as more observed proficiency in SBMT teaching is associated with greater student mindfulness practice and responsiveness to SBMT