A Natural History of Repetition

The purpose of this study was to understand typically developing children’s repetitive behavior in a free-play, daycare setting. By studying repetition in a non-Montessori setting, we tested the assumption that repetition is a characteristic behavior of all young children and not limited to the Montessori environment. Although Maria Montessori identified repetition during her observations, there is little empirical evidence to support her claim: most research has considered repetition in terms of psychopathology. We collected naturalistic observational data on 31 3- to 6-year-old children for a total of 101 hours to investigate the frequency, contexts, and structure of repetitive bouts. Multilevel model results suggest the ubiquity of repetition, as all children in the study engaged in motor repetition. Furthermore, repetition occurred throughout all free-play activities (construction, animation, fantasy play, rough-and-tumble play, and undirected activity), although repetition was not equally distributed across activities. Motor repetition was not equal across ages either; younger children engaged in more motor repetition than did older children. To understand the structure of repetition, our study also looked at the length of repetition bouts, which ranged from 2 to 19 repetitions and averaged 2.86 repetitions per bout. This natural history of repetition is an influential starting point for understanding the role of repetition in development and is informative to both Montessori and non-Montessori early childhood educators

Scotland Registry for Ankylosing Spondylitis (SIRAS) – Protocol

Funding SIRAS was funded by unrestricted grants from Pfizer and AbbVie. The project was reviewed by both companies, during the award process, for Scientific merit, to ensure that the design did not compromise patient safety, and to assess the global regulatory implications and any impact on regulatory strategy.Publisher PD

Prospective External Validation of the Clinical Effectiveness of an Emergency Department-Based Early Goal-Directed Therapy Protocol for Severe Sepsis and Septic Shock

Objective: To determine the clinical effectiveness of implementing early goal-directed therapy (EGDT) as a routine protocol in the emergency department (ED). Methods: Prospective interventional study conducted over 2 years at an urban ED. Inclusion criteria included suspected infection, criteria for systemic inflammation, and either systolic BP < 90 mm Hg after a fluid bolus or lactate concentration ≥ 4 mol/L. Exclusion criteria were age < 18 years, contraindication to a chest central venous catheter, and need for immediate surgery. We prospectively recorded preintervention clinical and mortality data on consecutive, eligible patients for 1 year when treatment was at the discretion of board-certified emergency physicians. We then implemented an EGDT protocol (the intervention) and recorded clinical data and mortality rates for 1 year. Prior to the first year, we defined a 33% relative reduction in mortality (relative mortality reduction that was found in the original EGDT trial) to indicate clinical effectiveness of the intervention. Results: We enrolled 79 patients in the preintervention year and 77 patients in the postintervention year. Compared with the preintervention year, patients in the postintervention year received significantly greater crystalloid volume (2.54 L vs 4.66 L, p < 0.001) and frequency of vasopressor infusion (34% vs 69%, p < 0.001) during the initial resuscitation. In-hospital mortality was 21 of 79 patients (27%) before intervention, compared with 14 of 77 patients (18%) after intervention (absolute difference, − 9%; 95% confidence interval, + 5 to − 21%). Conclusions: Implementation of EGDT in our ED was associated with a 9% absolute (33% relative) mortality reduction. Our data provide external validation of the clinical effectiveness of EGDT to treat sepsis and septic shock in the ED

Ozone Response to Aircraft Emissions: Sensitivity Studies with Two-dimensional Models

Our first intercomparison/assessment of the effects of a proposed high-speed civil transport (HSCT) fleet on the stratosphere is presented. These model calculations should be considered more as sensitivity studies, primarily designed to serve the following purposes: (1) to allow for intercomparison of model predictions; (2) to focus on the range of fleet operations and engine specifications giving minimal environmental impact; and (3) to provide the basis for future assessment studies. The basic scenarios were chosen to be as realistic as possible, using the information available on anticipated developments in technology. They are not to be interpreted as a commitment or goal for environmental acceptability

Robust MR-based approaches to quantifying white matter structure and structure/function alterations in Huntington's disease

Background: Huge advances have been made in understanding and addressing confounds in diffusion MRI data to quantify white matter microstructure. However, there has been a lag in applying these advances in clinical research. Some confounds are more pronounced in HD which impedes data quality and interpretability of patient-control differences. This study presents an optimised analysis pipeline and addresses specific confounds in a HD patient cohort. Method: 15 HD gene-positive and 13 matched control participants were scanned on a 3T MRI system with two diffusion MRI sequences. An optimised post processing pipeline included motion, eddy current and EPI correction, rotation of the B matrix, free water elimination ( FWE ) and tractography analysis using an algorithm capable of reconstructing crossing fibres. The corpus callosum was examined using both a region-of-interest and a deterministic tractography approach, using both conventional diffusion tensor imaging ( DTI )-based and spherical deconvolution analyses. Results: Correcting for CSF contamination significantly altered microstructural metrics and the detection of group differences. Reconstructing the corpus callosum using spherical deconvolution produced a more complete reconstruction with greater sensitivity to group differences, compared to DTI-based tractography. Tissue volume fraction ( TVF ) was reduced in HD participants and was more sensitive to disease burden compared to DTI metrics. Conclusion: Addressing confounds in diffusion MR data results in more valid, anatomically faithful white matter tract reconstructions with reduced within-group variance. TVF is recommended as a complementary metric, providing insight into the relationship with clinical symptoms in HD not fully captured by conventional DTI metrics

Huntington's disease gene hunters: an expanding tale

MacDonald ME. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington's disease chromosomes. Cell 1993;72:971–983. It is 28 years since the Huntington's Disease (HD) gene and mutation were identified and published in Cell by the Huntington's Disease Collaborative Research Group (HD-CRG; Fig. 1A).1 The genetic defect causing HD had been assigned to chromosome 4 in 1983 in one of the first successful linkage analyses using polymorphic DNA markers in humans,2 but it took another ten years to pinpoint the gene and determine the mutation. The long lag was largely because this research was conducted before the human genome was mapped, and was the culmination of a painstaking process involving repeatedly refining the location of the gene, based on locating markers and cloning transcripts from the genome across six independent laboratories. The nature of the genetic mutation—an expanded CAG repeat sequence—was also instrumental in the resolution of this detective story. Expanded repeats in DNA had already been associated with several diseases that had features in common with HD, such as genetic anticipation, including fragile X syndrome,3 spinal and bulbar muscular atrophy,4 and myotonic dystrophy.5-7 This meant the HD-CRG were actively looking for length mutations that segregated with disease that might indicate the presence of an expanding repeat tract. As we enter a new phase of HD research, with the advent of trials of potential disease-modifying treatments, it seems a good time to reflect on the legacy of the HD-CRG publication

Environmental change and Rift Valley fever in eastern Africa: projecting beyond HEALTHY FUTURES

Outbreaks of Rift Valley fever (RVF), a relatively recently emerged zoonosis endemic to large parts of sub-Saharan Africa that has the potential to spread beyond the continent, have profound health and socio-economic impacts, particularly in communities where resilience is already low. Here output from a new, dynamic disease model [the Liverpool RVF (LRVF) model], driven by downscaled, bias-corrected climate change data from an ensemble of global circulation models from the Inter-Sectoral Impact Model Intercomparison Project run according to two radiative forcing scenarios [representative concentration pathway (RCP)4.5 and RCP8.5], is combined with results of a spatial assessment of social vulnerability to the disease in eastern Africa. The combined approach allowed for analyses of spatial and temporal variations in the risk of RVF to the end of the current century. Results for both scenarios highlight the high-risk of future RVF outbreaks, including in parts of eastern Africa to date unaffected by the disease. The results also highlight the risk of spread from/to countries adjacent to the study area, and possibly farther afield, and the value of considering the geography of future projections of disease risk. Based on the results, there is a clear need to remain vigilant and to invest not only in surveillance and early warning systems, but also in addressing the socio-economic factors that underpin social vulnerability in order to mitigate, effectively, future impacts

Bayesian optimisation of restriction zones for bluetongue control.

We investigate the restriction of animal movements as a method to control the spread of bluetongue, an infectious disease of livestock that is becoming increasingly prevalent due to the onset of climate change. We derive control policies for the UK that minimise the number of infected farms during an outbreak using Bayesian optimisation and a simulation-based model of BT. Two cases are presented: first, where the region of introduction is randomly selected from England and Wales to find a generalised strategy. This "national" model is shown to be just as effective at subduing the spread of bluetongue as the current strategy of the UK government. Our proposed controls are simpler to implement, affect fewer farms in the process and, in so doing, minimise the potential economic implications. Second, we consider policies that are tailored to the specific region in which the first infection was detected. Seven different regions in the UK were explored and improvements in efficiency from the use of specialised policies presented. As a consequence of the increasing temperatures associated with climate change, efficient control measures for vector-borne diseases such as this are expected to become increasingly important. Our work demonstrates the potential value of using Bayesian optimisation in developing cost-effective disease management strategies

Climate prediction of El Niño malaria epidemics in north-west Tanzania

Malaria is a significant public health problem in Tanzania. Approximately 16 million malaria cases are reported every year and 100,000 to 125,000 deaths occur. Although most of Tanzania is endemic to malaria, epidemics occur in the highlands, notably in Kagera, a region that was subject to widespread malaria epidemics in 1997 and 1998. This study examined the relationship between climate and malaria incidence in Kagera with the aim of determining whether seasonal forecasts may assist in predicting malaria epidemics. A regression analysis was performed on retrospective malaria and climatic data during each of the two annual malaria seasons to determine the climatic factors influencing malaria incidence. The ability of the DEMETER seasonal forecasting system in predicting the climatic anomalies associated with malaria epidemics was then assessed for each malaria season. It was found that malaria incidence is positively correlated with rainfall during the first season (Oct-Mar) (R-squared = 0.73, p < 0.01). For the second season (Apr-Sep), high malaria incidence was associated with increased rainfall, but also with high maximum temperature during the first rainy season (multiple R-squared = 0.79, p < 0.01). The robustness of these statistical models was tested by excluding the two epidemic years from the regression analysis. DEMETER would have been unable to predict the heavy El Niño rains associated with the 1998 epidemic. Nevertheless, this epidemic could still have been predicted using the temperature forecasts alone. The 1997 epidemic could have been predicted from observed temperatures in the preceding season, but the consideration of the rainfall forecasts would have improved the temperature-only forecasts over the remaining years. These results demonstrate the potential of a seasonal forecasting system in the development of a malaria early warning system in Kagera region