Communication of scientific uncertainty : international case studies on the development of folate and vitamin D Dietary Reference Values.

Transparent evidence-based decision making has been promoted worldwide to engender trust in science and policy making. Yet, little attention has been given to transparency implementation. The degree of transparency (focused on how uncertain evidence was handled) during the development of folate and vitamin D Dietary Reference Values was explored in three a priori defined areas: (i) value request; (ii) evidence evaluation; and (iii) final values.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Adaptive-multi-reference least means squares filter

Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson’s r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

Protocol-directed sedation versus non-protocol-directed sedation to reduce duration of mechanical ventilation in mechanically ventilated intensive care patients (review)

Background : The sedation needs of critically ill patients have been recognized as a core component of critical care and meeting these is vital to assist recovery and ensure humane treatment. There is growing evidence to suggest that sedation requirements are not always optimally managed. Sub-optimal sedation incorporates both under- and over-sedation and has been linked to both short-term (e.g. length of stay) and long-term (e.g. psychological recovery) outcomes. Various strategies have been proposed to improve sedation management and address aspects of assessment as well as delivery of sedation.

Objectives : To assess the effects of protocol-directed sedation management on the duration of mechanical ventilation and other relevant patient outcomes in mechanically ventilated intensive care unit (ICU) patients. We looked at various outcomes and examined the role of bias in order to examine the level of evidence for this intervention.

Search methods : We searched the Cochrane Central Register of Controlled trials (CENTRAL) (2013; Issue 11), MEDLINE (OvidSP) (1990 to November 2013), EMBASE (OvidSP) (1990 to November 2013), CINAHL (BIREME host) (1990 to November 2013), Database of Abstracts of Reviews of Effects (DARE) (1990 to November 2013), LILACS (1990 to November 2013), Current Controlled Trials and US National Institutes of Health Clinical Research Studies (1990 to November 2013), and reference lists of articles. We re-ran the search in October 2014. We will deal with any studies of interest when we update the review.

Selection criteria : We included randomized controlled trials (RCTs) conducted in adult ICUs comparing management with and without protocol-directed sedation.

Data collection and analysis : Two authors screened the titles and abstracts and then the full-text reports identified from our electronic search. We assessed seven domains of potential risk of bias for the included studies. We examined the clinical, methodological and statistical heterogeneity and used the random-effects model for meta-analysis where we considered it appropriate. We calculated the mean difference (MD) for duration of mechanical ventilation and risk ratio (RR) for mortality across studies, with 95% confidence intervals (CI).

Main results : We identified two eligible studies with 633 participants. Both included studies compared the use of protocol-directed sedation, specifically protocols delivered by nurses, with usual care. We rated the risk of selection bias due to random sequence generation low for one study and unclear for one study. The risk of selection bias related to allocation concealment was low for both studies. We also assessed detection and attrition bias as low for both studies while we considered performance bias high due to the inability to blind participants and clinicians in both studies. Risk due to other sources of bias, such as potential for contamination between groups and reporting bias, was considered unclear. There was no clear evidence of differences in duration of mechanical ventilation (MD -5.74 hours, 95% CI -62.01 to 50.53, low quality evidence), ICU length of stay (MD -0.62 days, 95% CI -2.97 to 1.73) and hospital length of stay (MD -3.78 days, 95% CI -8.54 to 0.97) between people being managed with protocol-directed sedation versus usual care. Similarly, there was no clear evidence of difference in hospital mortality between the two groups (RR 0.96, 95% CI 0.71 to 1.31, low quality evidence). ICU mortality was only reported in one study preventing pooling of data. There was no clear evidence of difference in the incidence of tracheostomy (RR 0.77, 95% CI 0.31 to 1.89). The studies reported few adverse event outcomes; one study reported self extubation while the other study reported re-intubation; given this difference in outcomes, pooling of data was not possible. There was significant heterogeneity between studies for duration of mechanical ventilation (I2 = 86%, P value = 0.008), ICU length of stay (I2 = 82%, P value = 0.02) and incidence of tracheostomy (I2 = 76%, P value = 0.04), with one study finding a reduction in duration of mechanical ventilation and incidence of tracheostomy and the other study finding no difference.

Authors' conclusions : There is currently insufficient evidence to evaluate the effectiveness of protocol-directed sedation. Results from the two RCTs were conflicting, resulting in the quality of the body of evidence as a whole being assessed as low. Further studies, taking into account contextual and clinician characteristics in different ICU environments, are necessary to inform future practice. Methodological strategies to reduce the risk of bias need to be considered in future studies.

Constructing optimal prediction intervals by using neural networks and bootstrap method

This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.

Improving the quality of prediction intervals through optimal aggregation

Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting performance significantly drops in the presence of process uncertainties and disturbances. NN-based prediction intervals (PIs) offer an alternative solution to appropriately quantify uncertainties and disturbances associated with point forecasts. In this paper, an NN ensemble procedure is proposed to construct quality PIs. A recently developed lower-upper bound estimation method is applied to develop NN-based PIs. Then, constructed PIs from the NN ensemble members are combined using a weighted averaging mechanism. Simulated annealing and a genetic algorithm are used to optimally adjust the weights for the aggregation mechanism. The proposed method is examined for three different case studies. Simulation results reveal that the proposed method improves the average PI quality of individual NNs by 22%, 18%, and 78% for the first, second, and third case studies, respectively. The simulation study also demonstrates that a 3%-4% improvement in the quality of PIs can be achieved using the proposed method compared to the simple averaging aggregation method.

Writing scientific papers, with special reference to evolutionary ecology

© 2015, Springer International Publishing Switzerland. The advancement of science, as well as scientific careers, depends upon good and clear scientific writing. Science is the most democratic of human endeavours because, in principle, anyone can replicate a scientific discovery. In order for this to continue, writing must be clear enough to be understood well enough to allow replication, either in principle or in fact. In this paper I will present data on the publication process in Evolutionary Ecology, use it to illustrate some of the problems in scientific papers, make some general remarks about writing scientific papers, summarise two new paper categories in the journal which will fill gaps that appear to be expanding in the literature, and summarise new journal policies to help mitigate existing problems. Most of the suggestions about writing would apply to any scientific journal.

Sedation protocols to reduce duration of mechanical ventilation in the ICU: a Cochrane Systematic Review

AIMS: Assess the effects of protocol-directed sedation management on the duration of mechanical ventilation and other relevant patient outcomes in mechanically ventilated intensive care unit patients. BACKGROUND: Sedation is a core component of critical care. Sub-optimal sedation management incorporates both under- and over-sedation and has been linked to poorer patient outcomes. DESIGN: Cochrane systematic review of randomized controlled trials. DATA SOURCES: Cochrane Central Register of Controlled trials, MEDLINE, EMBASE, CINAHL, Database of Abstracts of Reviews of Effects, LILACS, Current Controlled Trials and US National Institutes of Health Clinical Research Studies (1990-November 2013) and reference lists of articles were used. REVIEW METHODS: Randomized controlled trials conducted in intensive care units comparing management with and without protocol-directed sedation were included. Two authors screened titles, abstracts and full-text reports. Potential risk of bias was assessed. Clinical, methodological and statistical heterogeneity were examined and the random-effects model used for meta-analysis where appropriate. Mean difference for duration of mechanical ventilation and risk ratio for mortality, with 95% confidence intervals, were calculated. RESULTS: Two eligible studies with 633 participants comparing protocol-directed sedation delivered by nurses vs. usual care were identified. There was no evidence of differences in duration of mechanical ventilation or hospital mortality. There was statistically significant heterogeneity between studies for duration of mechanical ventilation. CONCLUSIONS: There is insufficient evidence to evaluate the effectiveness of protocol-directed sedation as results from the two randomized controlled trials were conflicting.

Accounting for uncertainty in volumes of seabed change measured with repeat multibeam sonar surveys

Seafloors of unconsolidated sediment are highly dynamic features; eroding or accumulating under the action of tides, waves and currents. Assessing which areas of the seafloor experienced change and measuring the corresponding volumes involved provide insights into these important active sedimentation processes. Computing the difference between Digital Elevation Models (DEMs) obtained from repeat Multibeam Echosounders (MBES) surveys has become a common technique to identify these areas, but the uncertainty in these datasets considerably affects the estimation of the volumes displaced. The two main techniques used to take into account uncertainty in volume estimations are the limitation of calculations to areas experiencing a change in depth beyond a chosen threshold, and the computation of volumetric confidence intervals. However, these techniques are still in their infancy and, as a result, are often crude, seldom used or poorly understood. In this article, we explored a number of possible methodological advances to address this issue, including: (1) using the uncertainty information provided by the MBES data processing algorithm CUBE, (2) adapting fluvial geomorphology techniques for volume calculations using spatially variable thresholds and (3) volumetric histograms. The nearshore seabed off Warrnambool harbour - located in the highly energetic southwest Victorian coast, Australia - was used as a test site. Four consecutive MBES surveys were carried out over a four-months period. The difference between consecutive DEMs revealed an area near the beach experiencing large sediment transfers - mostly erosion - and an area of reef experiencing increasing deposition from the advance of a nearby sediment sheet. The volumes of sediment displaced in these two areas were calculated using the techniques described above, both traditionally and using the suggested improvements. We compared the results and discussed the applicability of the new methodological improvements. We found that the spatially variable uncertainty derived from the CUBE algorithm provided the best results (i.e. smaller confidence intervals), but that similar results can be obtained using as a fixed uncertainty value derived from a reference area under a number of operational conditions.