What can visual electrophysiology tell about possible visual-field defects in paediatric patients

Recognising a potential visual-field (VF) defect in paediatric patients might be challenging, especially in children before the age of 5 years and those with developmental delay or intellectual disability. Visual electrophysiological testing is an objective and non-invasive technique for evaluation of visual function in paediatric patients, which can characterise the location of dysfunction and differentiate between disorders of the retina, optic nerve and visual pathway. The recording of electroretinography (ERG) and visual-evoked potentials (VEP) is possible from early days of life and requires no subjective input from the patient. As the origins of ERG and VEP tests are known, the pattern of electrophysiological changes can provide information about the VF of a child unable to perform accurate perimetry. This review summarises previously published electrophysiological findings in several common types of VF defects that can be found in paediatric patients (generalised VF defect, peripheral VF loss, central scotoma, bi-temporal hemianopia, altitudinal VF defect, quadrantanopia and homonymous hemianopia). It also shares experience on using electrophysiological testing as additional functional evidence to other tests in the clinical challenge of diagnosing or excluding VF defects in complex paediatric patients. Each type of VF defect is illustrated with one or two clinical cases.

Bayesian Crime Investigations: Integrating Actuarial And Expert Models

In response to concerns that some of the most methodologically rigorous predictive studies of criminal offender characteristics may yet be less generalizable and applicable than advertised or assumed, this research first tests how well seven regression analysis models (represented by 28 equations) predict characteristics across three conditions: familiar cases (used to create the regressions), less familiar cases (native to the sample used to create the regressions) and foreign cases (from a similar but novel sample). Here a linear trend shows overfitting of the models to their own sample: a drop-off in prediction accuracy relative to simple mean-based prediction as cases become more foreign (ηp 2 = .646). In response to hopes that subjective input from expert police investigators could be integrated into the models to correct for this overfitting bias, this research also tests an algorithm combining expert ratings with the regression equations. Here moderate and significant improvement in novel-case prediction is observed overall (p = .036, r = .44) and equations for all twelve expert participants are shown to improve prediction to varying degrees. These results suggest that current best methods would perform poorly in the field, but can be improved by expert insight.

Bayesian Crime Investigations: Integrating Actuarial And Expert Models

In response to concerns that some of the most methodologically rigorous predictive studies of criminal offender characteristics may yet be less generalizable and applicable than advertised or assumed, this research first tests how well seven regression analysis models (represented by 28 equations) predict characteristics across three conditions: familiar cases (used to create the regressions), less familiar cases (native to the sample used to create the regressions) and foreign cases (from a similar but novel sample). Here a linear trend shows overfitting of the models to their own sample: a drop-off in prediction accuracy relative to simple mean-based prediction as cases become more foreign (ηp 2 = .646). In response to hopes that subjective input from expert police investigators could be integrated into the models to correct for this overfitting bias, this research also tests an algorithm combining expert ratings with the regression equations. Here moderate and significant improvement in novel-case prediction is observed overall (p = .036, r = .44) and equations for all twelve expert participants are shown to improve prediction to varying degrees. These results suggest that current best methods would perform poorly in the field, but can be improved by expert insight.

Computerized Sunnybrook facial grading scale (SBface) application for facial paralysis evaluation

Background The Sunnybrook facial grading scale is a comprehensive scale for the evaluation of facial paralysis patients. Its results greatly depend on subjective input. This study aimed to develop and validate an automated Sunnybrook facial grading scale (SBface) to more objectively assess disfigurement due to facial paralysis.Methods An application compatible with iOS version 11.0 and up was developed. The software automatically detected facial features in standardized photographs and generated scores following the Sunnybrook facial grading scale. Photographic data from 30 unilateral facial paralysis patients were randomly sampled for validation. Intrarater reliability was tested by conducting two identical tests at a 2-week interval. Interrater reliability was tested between the software and three facial nerve clinicians.Results A beta version of the SBface application was tested. Intrarater reliability showed excellent congruence between the two tests. Moderate to strong positive correlations were found between the software and an otolaryngologist, including the total scores of the three individual software domains and composite scores. However, 74.4% (29/39) of the subdomain items showed low to zero correlation with the human raters (κ<0.2). The correlations between the human raters showed good congruence for most of the total and composite scores, with 10.3% (4/39) of the subdomain items failing to correspond (κ<0.2).Conclusions The SBface application is efficient and accurate for evaluating the degree of facial paralysis based on the Sunnybrook facial grading scale. However, correlations of the software-derived results with those of human raters are limited by the software algorithm and the raters’ inconsistency.

Multi-criteria decision-making framework for selecting sustainable private partners for housing projects

Purpose This paper aims to develop a framework for the selection of private partners in the housing industry of Saudi Arabia under the scheme of the partnership between the public and private sectors. Design/methodology/approach By investigating criteria from a comprehensive literature review and experts input through surveys, developing further surveys incorporating decision-making methods: analytic hierarchy process (AHP) and multi-attribute utility theory (MAUT) to construct a framework for selection based on weightages and utilities. Findings The results identified criteria categorized under four categories: financial (C1), technical (C2), managerial (C3) and safety/environment (C4) and their sub-criteria. The study found that the main criteria were relatively close to each other in importance based on the subjective input of the experts with the technical and safety/environment criteria tying equally with 27% followed by the managerial with 24% and trailed by the financial with 22%. Research limitations/implications The study and surveys were conducted for the Saudi market and the experts were within the country. Originality/value The study contributes to the Saudi housing initiative which is a part of the 2030 Vision and provides insight to international investors who would be willing to invest in the Saudi market; and to the literature as there is a notable lack of study on public-private partnership in housing in Saudi Arabia.

Embracing data uncertainty in water decision-making: an application to evaluate water supply and sewerage in Spain

Analyses of complex water management decision-making problems, involving tradeoffs amongst multiple criteria, are often undertaken using multi-criteria decision analysis (MCDA) techniques. Various forms of uncertainty may arise in the application of MCDA methods, including imprecision, inaccuracy or ill determination of data. The ELECTRE family methods deal with imperfect knowledge of data by incorporating ‘pseudo-criteria’, with discrimination thresholds, to interpret the outranking relation as a fuzzy relation. However, the task of selecting thresholds for each criterion can be difficult and ambiguous for decision-makers. In this paper, we propose a confidence-interval-based approach which aims to reduce the subjective input required by decision-makers. The proposed approach involves defining the uncertainty in the input values using confidence intervals and expressing thresholds as a function of the interval estimates. The usefulness of the approach is illustrated by applying it to evaluate the water supply and sewerage services in Spain. Results show that the confidence interval approach may be interesting in some cases (e.g. when dealing with statistical data from surveys or measuring equipment), but should never replace the preferences or judgments of the actors involved in the decision process.

Effects of meditation experience on functional connectivity of distributed brain networks

This study sought to examine the effect of meditation experience on brain networks underlying cognitive actions employed during contemplative practice. In a previous study, we proposed a basic model of naturalistic cognitive fluctuations that occur during the practice of focused attention meditation. This model specifies four intervals in a cognitive cycle: mind wandering (MW), awareness of MW, shifting of attention, and sustained attention. Using subjective input from experienced practitioners during meditation, we identified activity in salience network regions during awareness of MW and executive network regions during shifting and sustained attention. Brain regions associated with the default mode were active during MW. In the present study, we reasoned that repeated activation of attentional brain networks over years of practice may induce lasting functional connectivity changes within relevant circuits. To investigate this possibility, we created seeds representing the networks that were active during the four phases of the earlier study, and examined functional connectivity during the resting state in the same participants. Connectivity maps were then contrasted between participants with high vs. low meditation experience. Participants with more meditation experience exhibited increased connectivity within attentional networks, as well as between attentional regions and medial frontal regions. These neural relationships may be involved in the development of cognitive skills, such as maintaining attention and disengaging from distraction, that are often reported with meditation practice. Furthermore, because altered connectivity of brain regions in experienced meditators was observed in a non-meditative (resting) state, this may represent a transference of cognitive abilities “off the cushion” into daily life.

Exploring the use of transformation group priors and the method of maximum relative entropy for Bayesian glaciological inversions

Ice-sheet models can be used to forecast ice losses from Antarctica and Greenland, but to fully quantify the risks associated with sea-level rise, probabilistic forecasts are needed. These require estimates of the probability density function (PDF) for various model parameters (e.g. the basal drag coefficient and ice viscosity). To infer such parameters from satellite observations it is common to use inverse methods. Two related approaches are in use: (1) minimization of a cost function that describes the misfit to the observations, often accompanied by explicit or implicit regularization, or (2) use of Bayes’ theorem to update prior assumptions about the probability of parameters. Both approaches have much in common and questions of regularization often map onto implicit choices of prior probabilities that are made explicit in the Bayesian framework. In both approaches questions can arise that seem to demand subjective input. One way to specify prior PDFs more objectively is by deriving transformation group priors that are invariant to symmetries of the problem, and then maximizing relative entropy, subject to any additional constraints. Here we investigate the application of these methods to the derivation of priors for a Bayesian approach to an idealized glaciological inverse problem.

Minimum convex hull mass estimations of complete mounted skeletons

Body mass is a critical parameter used to constrain biomechanical and physiological traits of organisms. Volumetric methods are becoming more common as techniques for estimating the body masses of fossil vertebrates. However, they are often accused of excessive subjective input when estimating the thickness of missing soft tissue. Here, we demonstrate an alternative approach where a minimum convex hull is derived mathematically from the point cloud generated by laser-scanning mounted skeletons. This has the advantage of requiring minimal user intervention and is thus more objective and far quicker. We test this method on 14 relatively large-bodied mammalian skeletons and demonstrate that it consistently underestimates body mass by 21 per cent with minimal scatter around the regression line. We therefore suggest that it is a robust method of estimating body mass where a mounted skeletal reconstruction is available and demonstrate its usage to predict the body mass of one of the largest, relatively complete sauropod dinosaurs: Giraffatitan brancai (previously Brachiosaurus ) as 23200 kg.