Echocardiography parameters used in identifying right ventricle dysfunction in preterm infants with early bronchopulmonary dysplasia: A scoping review

Background Bronchopulmonary Dysplasia (BPD) is a chronic condition that affects preterm infants and is associated with long-term complications. Haemodynamic effects of BPD can lead to right ventricular (RV) dysfunction.ObjectiveTo synthesise and map the evidence of echo parameters used in identifying RV dysfunction in the first two weeks-after-birth (WAB) of preterm infants with early BPD.Information SourcesThis scoping review included the databases: Medline, CINAHL, PubMed, EMBASE, Scopus, ProQuest, Web of Science, Cochrane Library, JBI Evidence-Based Practise and Gray Literature.Search StrategyThe search utilised Boolean operators and descriptors registered in Medical Subject Headings.Inclusion and exclusion criteriaIncluded were studies utilising echo parameters to examine RV function in preterm infants with early BPD in the first two WAB.Synthesis of resultsThe results are presented as a map of the extracted findings in a tabular format with a narrative summary.ResultsEight studies were included. Differences were observed in the number and timing of echo scans performed in the first two WAB and the variations in the echo parameters used to compare preterm infants with and without early BPD. Only echo scans performed at the end of the first WAB, demonstrated significant differences in the echo parameters measurements between preterm infants with and without BPD. Studies using RV Myocardial Performance Index (MPI) to identify RV-dysfunction associated with early BPD demonstrated similar findings. The Pulsed-Wave Doppler technique identified differences in RV-MPI between preterm infants with and without BPD, while Tissue-Doppler-Imaging did not demonstrate similar results. Speckle tracking can measure strain (S) and strain rate (SR) and diagnose RV-dysfunction. However, the findings of studies that utilised speckle tracking varied. Finally, two of the included studies added blood tests to their diagnostic model of early BPD, which was able to demonstrate significant differences in blood test results between BPD-affected and control preterm infants.ConclusionBPD could adversely affect the myocardium function of the RV; these negative influences can be captured in the first two WAB. However, there are still knowledge gaps regarding the appropriate number, timing and the most suitable echo parameters to assess RV function

Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations

Temperature and precipitation extremes and their potential future changes are evaluated in an ensemble of global coupled climate models participating in the Intergovernmental Panel on Climate Change (IPCC) diagnostic exercise for the Fourth Assessment Report (AR4). Climate extremes are expressed in terms of 20-yr return values of annual extremes of near-surface temperature and 24-h precipitation amounts. The simulated changes in extremes are documented for years 2046–65 and 2081–2100 relative to 1981–2000 in experiments with the Special Report on Emissions Scenarios (SRES) B1, A1B, and A2 emission scenarios. Overall, the climate models simulate present-day warm extremes reasonably well on the global scale, as compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes, especially in sea ice–covered areas. Simulated present-day precipita-tion extremes are plausible in the extratropics, but uncertainties in extreme precipitation in the Tropics are very large, both in the models and the available observationally based datasets. Changes in warm extremes generally follow changes in the mean summertime temperature. Cold ex-tremes warm faster than warm extremes by about 30%–40%, globally averaged. The excessive warming of cold extremes is generally confined to regions where snow and sea ice retreat with global warming. With th

Protocol for a pragmatic feasibility randomised controlled trial of peer coaching for adults with long-term conditions: PEER CONNECT.

INTRODUCTION: Patients with low levels of knowledge, skills and confidence to manage their health and well-being (activation) are more likely to have unmet health needs, delay seeking healthcare and need emergency care. National Health Service England estimates that this may be applicable to 25%-40% of patients with long-term health conditions. Volunteer peer coaching may support people to increase their level of activation. This form of intervention may be particularly effective for people with low levels of activation. METHODS AND ANALYSIS: This single site, two-arm randomised controlled trial has been designed to assess the feasibility of conducting a definitive trial of volunteer peer health and well-being coaching for people with long-term health conditions (multiple sclerosis, rheumatic diseases or chronic pain) and low activation. Feasibility outcomes include recruitment and retention rates, and intervention adherence. We will measure patient activation, mental health and well-being as potential outcomes for a definitive trial. These outcomes will be summarised descriptively for each time point by allocated group and help to inform sample size calculation for the definitive trial. Criteria for progression to a full trial will be used. ETHICS AND DISSEMINATION: Ethical approval has been granted by the London - Surrey Research Ethics Committee, reference 21/LO/0715. Results from this feasibility trial will be shared directly with participants, presented at local, regional and national conferences and published in an open-access journal. TRIAL REGISTRATION NUMBER: ISRCTN12623577

Developing Intensity-Duration-Frequency (IDF) Curves From Satellite-Based Precipitation: Methodology and Evaluation

Given the continuous advancement in the retrieval of precipitation from satellites, it is important to develop methods that incorporate satellite-based precipitation data sets in the design and planning of infrastructure. This is because in many regions around the world, in situ rainfall observations are sparse and have insufficient record length. A handful of studies examined the use of satellite-based precipitation to develop intensity-duration-frequency (IDF) curves; however, they have mostly focused on small spatial domains and relied on combining satellite-based with ground-based precipitation data sets. In this study, we explore this issue by providing a methodological framework with the potential to be applied in ungauged regions. This framework is based on accounting for the characteristics of satellite-based precipitation products, namely, adjustment of bias and transformation of areal to point rainfall. The latter method is based on previous studies on the reverse transformation (point to areal) commonly used to obtain catchment-scale IDF curves. The paper proceeds by applying this framework to develop IDF curves over the contiguous United States (CONUS); the data set used is Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR). IDFs are then evaluated against National Oceanic and Atmospheric Administration (NOAA) Atlas 14 to provide a quantitative estimate of their accuracy. Results show that median errors are in the range of (17–22%), (6–12%), and (3–8%) for one-day, two-day and three-day IDFs, respectively, and return periods in the range (2–100) years. Furthermore, a considerable percentage of satellite-based IDFs lie within the confidence interval of NOAA Atlas 14

Extreme value laws in dynamical systems under physical observables

Extreme value theory for chaotic dynamical systems is a rapidly expanding area of research. Given a system and a real function (observable) defined on its phase space, extreme value theory studies the limit probabilistic laws obeyed by large values attained by the observable along orbits of the system. Based on this theory, the so-called block maximum method is often used in applications for statistical prediction of large value occurrences. In this method, one performs inference for the parameters of the Generalised Extreme Value (GEV) distribution, using maxima over blocks of regularly sampled observations along an orbit of the system. The observables studied so far in the theory are expressed as functions of the distance with respect to a point, which is assumed to be a density point of the system's invariant measure. However, this is not the structure of the observables typically encountered in physical applications, such as windspeed or vorticity in atmospheric models. In this paper we consider extreme value limit laws for observables which are not functions of the distance from a density point of the dynamical system. In such cases, the limit laws are no longer determined by the functional form of the observable and the dimension of the invariant measure: they also depend on the specific geometry of the underlying attractor and of the observable's level sets. We present a collection of analytical and numerical results, starting with a toral hyperbolic automorphism as a simple template to illustrate the main ideas. We then formulate our main results for a uniformly hyperbolic system, the solenoid map. We also discuss non-uniformly hyperbolic examples of maps (H\'enon and Lozi maps) and of flows (the Lorenz63 and Lorenz84 models). Our purpose is to outline the main ideas and to highlight several serious problems found in the numerical estimation of the limit laws

Bias correction of high-resolution regional climate model precipitation output gives the best estimates of precipitation in Himalayan catchments

The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognised, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5 km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a non‐linear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modelling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably under‐represented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments

MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration

Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution cameras to capture huge collections of images from the seabed. However, the timely evaluation of all these images presents a bottleneck problem as tens of thousands or more images can be collected during a single dive. This makes computational support for marine image analysis essential. Computer-aided analysis of environmental images (and marine images in particular) with machine learning algorithms is promising, but challenging and different to other imaging domains because training data and class labels cannot be collected as efficiently and comprehensively as in other areas. In this paper, we present Machine learning Assisted Image Annotation (MAIA), a new image annotation method for environmental monitoring and exploration that overcomes the obstacle of missing training data. The method uses a combination of autoencoder networks and Mask Region-based Convolutional Neural Network (Mask R-CNN), which allows human observers to annotate large image collections much faster than before. We evaluated the method with three marine image datasets featuring different types of background, imaging equipment and object classes. Using MAIA, we were able to annotate objects of interest with an average recall of 84.1% more than twice as fast as compared to “traditional” annotation methods, which are purely based on software-supported direct visual inspection and manual annotation. The speed gain increases proportionally with the size of a dataset. The MAIA approach represents a substantial improvement on the path to greater efficiency in the annotation of large benthic image collections

Ten Years’ Experience with Alendronate for Osteoporosis in Postmenopausal Women

Background Antiresorptive agents are widely used to treat osteoporosis. We report the results of a multinational randomized, double-blind study, in which postmenopausal women with osteoporosis were treated with alendronate for up to 10 years. Methods The initial three-year phase of the study compared three daily doses of alendronate with placebo. Women in the original placebo group received alendronate in years 4 and 5 and then were discharged. Women in the original active-treatment groups continued to receive alendronate during the initial extension (years 4 and 5). In two further extensions (years 6 and 7, and 8 through 10), women who had received 5 mg or 10 mg of alendronate daily continued on the same treatment. Women in the discontinuation group received 20 mg of alendronate daily for two years and 5 mg daily in years 3, 4, and 5, followed by five years of placebo. Randomized group assignments and blinding were maintained throughout the 10 years. We report results for the 247 women who participated in all four phases of the study. Results Treatment with 10 mg of alendronate daily for 10 years produced mean increases in bone mineral density of 13.7 percent at the lumbar spine (95 percent confidence interval, 12.0 to 15.5 percent), 10.3 percent at the trochanter (95 percent confidence interval, 8.1 to 12.4 percent), 5.4 percent at the femoral neck (95 percent confidence interval, 3.5 to 7.4 percent), and 6.7 percent at the total proximal femur (95 percent confidence interval, 4.4 to 9.1 percent) as compared with base-line values; smaller gains occurred in the group given 5 mg daily. The discontinuation of alendronate resulted in a gradual loss of effect, as measured by bone density and biochemical markers of bone remodeling. Safety data, including fractures and stature, did not suggest that prolonged treatment resulted in any loss of benefit. Conclusions The therapeutic effects of alendronate were sustained, and the drug was well tolerated over a 10-year period. The discontinuation of alendronate resulted in the gradual loss of its effects