Confronting conflicts:history teachers’ reactions on spontaneous controversial remarks

Sometimes, things don't go to plan. Current events come into the classroom, especially the history classroom. How should students' responses to current affairs be dealt with there? How should students' desire to voice their opinions be handled if their opinion is unpopular. What if the student is simply wrong? How far can moral relativism be acknowledged, explored and scrutinised in the history classroom, when the topic under discussion is controversial and urgent? Working in the Netherlands and Belgium, Wansink, Patist, Zuiker, Savenije and Janssenswillen have developed and refined ways of doing this. In this article they provide an overview of researchers' thinking on the issue, and clear strategies and guidelines for what a history teacher might do to ensure that any unplanned discussion is, at least, respectful, engaging and rigorous

Tensions experienced by teachers when participating in a professional learning community

Contains fulltext : 199560.pdf (publisher's version ) (Open Access)29 november 201

Fit for purpose of on-the-road driving and simulated driving: a randomised crossover study using the effect of sleep deprivation

IntroductionDrivers should be aware of possible impairing effects of alcohol, medicinal substance, or fatigue on driving performance. Such effects are assessed in clinical trials, including a driving task or related psychomotor tasks. However, a choice between predicting tasks must be made. Here, we compare driving performance with on-the-road driving, simulator driving, and psychomotor tasks using the effect of sleep deprivation.Method This two-way cross over study included 24 healthy men with a minimum driving experience of 3000km per year. Psychomotor tasks, simulated driving, and on-the-road driving were assessed in the morning and the afternoon after a well-rested night and in the morning after a sleep-deprived night. Driving behaviour was examined by calculating the Standard Deviation of Lateral Position (SDLP).Results SDLP increased after sleep deprivation for simulated (10cm, 95%CI:6.7–13.3) and on-the-road driving (2.8cm, 95%CI:1.9–3.7). The psychomotor test battery detected effects of sleep deprivation in almost all tasks. Correlation between on-the-road tests and simulator SDLP after a well-rested night (0.63, p Discussion The lack of apparent correlations and difference in sensitivity of performance of the psychomotor tasks, simulated driving and, on-the-road driving indicates that the tasks may not be interchangeable and may assess different aspects of driving behaviour.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Using machine learning techniques to characterize sleep-deprived driving behavior

OBJECTIVESleep deprivation is known to affect driving behavior and may lead to serious car accidents similar to the effects from e.g., alcohol. In a previous study, we have demonstrated that the use of machine learning techniques allows adequate characterization of abnormal driving behavior after alprazolam and/or alcohol intake. In the present study, we extend this approach to sleep deprivation and test the model for characterization of new interventions. We aimed to classify abnormal driving behavior after sleep deprivation, and, by using a machine learning model, we tested if this model could also pick up abnormal driving behavior resulting from other interventions.METHODSData were collected during a previous study, in which 24 subjects were tested after being sleep-deprived and after a well-rested night. Features were calculated from several driving parameters, such as the lateral position, speed of the car, and steering speed. In the present study, we used a gradient boosting model to classify sleep deprivation. The model was validated using a 5-fold cross validation technique. Next, probability scores were used to identify the overlap of driving behavior after sleep deprivation and driving behavior affected by other interventions. In the current study alprazolam, alcohol, and placebo are used to test/validate the approach.RESULTSThe sleep deprivation model detected abnormal driving behavior in the simulator with an accuracy of 77 ± 9%. Abnormal driving behavior after alprazolam, and to a lesser extent also after alcohol intake, showed remarkably similar characteristics to sleep deprivation. The average probability score for alprazolam and alcohol measurements was 0.79, for alcohol 0.63, and for placebo only 0.27 and 0.30, matching the expected relative drowsiness.CONCLUSION​​​​​​​We developed a model detecting abnormal driving induced by sleep deprivation. The model shows the similarities in driving characteristics between sleep deprivation and other interventions, i.e., alcohol and alprazolam. Consequently, our model for sleep deprivation may serve as a next reference point for a driving test battery of newly developed drugs.Pharmacolog

Sputum RNA signature in allergic asthmatics following allergen bronchoprovocation test

Background: Inhaled allergen challenge is a validated disease model of allergic asthma offering useful pharmacodynamic assessment of pharmacotherapeutic effects in a limited number of subjects. Objectives: To evaluate whether an RNA signature can be identified from induced sputum following an inhaled allergen challenge, whether a RNA signature could be modulated by limited doses of inhaled fluticasone, and whether these gene expression profiles would correlate with the clinical endpoints measured in this study. Methods: Thirteen non-smoking, allergic subjects with mild-to-moderate asthma participated in a randomised, placebo-controlled, 2-period cross-over study following a single-blind placebo run-in period. Each period consisted of three consecutive days, separated by a wash-out period of at least 3 weeks. Subjects randomly received inhaled fluticasone ((FP) MDI; 500 mcg BID×5 doses in total) or placebo. On day 2, house dust mite extract was inhaled and airway response was measured by FEV1 at predefined time points until 7 h post-allergen. Sputum was induced by NaCl 4.5%, processed and analysed at 24 h pre-allergen and 7 and 24 h post-allergen. RNA was isolated from eligible sputum cell pellets (<80% squamous of 500 cells), amplified according to NuGEN technology, and profiled on Affymetrix arrays. Gene expression changes from baseline and fluticasone treatment effects were evaluated using a mixed effects ANCOVA model at 7 and at 24 h post-allergen challenge. Results: Inhaled allergen-induced statistically significant gene expression changes in sputum, which were effectively blunted by fluticasone (adjusted p<0.025). Forty-seven RNA signatures were selected from these responses for correlation analyses and further validation. This included Th2 mRNA levels for cytokines, chemokines, high-affinity IgE receptor FCER1A, histamine receptor HRH4, and enzymes and receptors in the arachidonic pathway. Individual messengers from the 47 RNA signatures correlated significantly with lung function and sputum eosinophil counts. Conclusion: Our RNA extraction and profiling protocols allowed reproducible assessments of inflammatory signatures in sputum including quantification of drug effects on this response in allergic asthmatics. This approach offers novel possibilities for the development of pharmacodynamic (PD) biomarkers in asthma