•Comparison of four IVS methods to predict flow in two distinct watersheds using ANNs.•Two IVS methods are model-free and two are model-based, improvements are proposed for the model-based ...methods.•Performance comparison between models with and without IVS, the termination criteria used, and predefined number of inputs.•Input usefulness is not binary; the correct number of selected inputs is dependent on the desired model complexity.
Artificial neural networks (ANNs) are increasingly used for flood forecasting. The performance of these models relies on the selection of appropriate inputs. However, Input Variable Selection (IVS) is typically performed using expert knowledge or simple linear methods. This research compares and evaluates four IVS methods including two model-free methods: partial correlation (PC), partial mutual information (PMI), and two novel model-based methods: an improved input omission (IO), and improved combined neural pathway strength (CNPS). A comprehensive comparison of performance efficacy for multiple IVS methods has not been published in literature before. Each method is used for daily and hourly lead times in the Bow and Don Rivers (both in Canada), respectively. These watersheds represent different hydrological systems and were selected to highlight the performance of the IVS methods under differing conditions. This research determines that the proposed CNPS produces the strongest performing ANNs based on the robustness of the inputs selected, comparison to other IVS methods, and models developed without IVS. Additionally, this research demonstrates that standard termination criteria do not reliably identify the optimum number of inputs for the ANNs and using a model-based optimization of inputs is recommended. As a result, it is recommended that the number of inputs be determined using a systematic approach, where each input selection is informed by an IVS-based input ranking, rather than a predefined termination criterion. Lastly, this research demonstrates that input usefulness is not binary concept; the correct number of selected inputs is dependant on the desired model complexity, instead of an arbitrarily selected IVS termination criteria.
The 2018 update of the Canadian Stroke Best Practice Recommendations for Acute Stroke Management, 6th edition, is a comprehensive summary of current evidence-based recommendations, appropriate for ...use by healthcare providers and system planners caring for persons with very recent symptoms of acute stroke or transient ischemic attack. The recommendations are intended for use by a interdisciplinary team of clinicians across a wide range of settings and highlight key elements involved in prehospital and Emergency Department care, acute treatments for ischemic stroke, and acute inpatient care. The most notable changes included in this 6th edition are the renaming of the module and its integration of the formerly separate modules on prehospital and emergency care and acute inpatient stroke care. The new module, Acute Stroke Management: Prehospital, Emergency Department, and Acute Inpatient Stroke Care is now a single, comprehensive module addressing the most important aspects of acute stroke care delivery. Other notable changes include the removal of two sections related to the emergency management of intracerebral hemorrhage and subarachnoid hemorrhage. These topics are covered in a new, dedicated module, to be released later this year. The most significant recommendation updates are for neuroimaging; the extension of the time window for endovascular thrombectomy treatment out to 24 h; considerations for treating a highly selected group of people with stroke of unknown time of onset; and recommendations for dual antiplatelet therapy for a limited duration after acute minor ischemic stroke and transient ischemic attack. This module also emphasizes the need for increased public and healthcare provider’s recognition of the signs of stroke and immediate actions to take; the important expanding role of paramedics and all emergency medical services personnel; arriving at a stroke-enabled Emergency Department without delay; and launching local healthcare institution code stroke protocols. Revisions have also been made to the recommendations for the triage and assessment of risk of recurrent stroke after transient ischemic attack/minor stroke and suggested urgency levels for investigations and initiation of management strategies. The goal of this updated guideline is to optimize stroke care across Canada, by reducing practice variations and reducing the gap between current knowledge and clinical practice.
Data-driven flow-forecasting models, such as artificial neural networks (ANNs), are increasingly featured in research for their potential use in operational riverine flood warning systems. However, ...the distributions of observed flow data are imbalanced, resulting in poor prediction accuracy on high flows in terms of both amplitude and timing error. Resampling and ensemble techniques have been shown to improve model performance on imbalanced datasets. However, the efficacy of these methods (individually or combined) has not been explicitly evaluated for improving high-flow forecasts. In this research, we systematically evaluate and compare three resampling methods, random undersampling (RUS), random oversampling (ROS), and the synthetic minority oversampling technique for regression (SMOTER), and four ensemble techniques, randomised weights and biases, bagging, adaptive boosting (AdaBoost), and least-squares boosting (LSBoost), on their ability to improve high stage prediction accuracy using ANNs. These methods are implemented both independently and in combined hybrid techniques, where the resampling methods are embedded within the ensemble methods. This systematic approach for embedding resampling methods is a novel contribution. This research presents the first analysis of the effects of combining these methods on high stage prediction accuracy. Data from two Canadian watersheds (the Bow River in Alberta and the Don River in Ontario), representing distinct hydrological systems, are used as the basis for the comparison of the methods. The models are evaluated on overall performance and on typical and high stage subsets. The results of this research indicate that resampling produces marginal improvements to high stage prediction accuracy, whereas ensemble methods produce more substantial improvements, with or without resampling. Many of the techniques used produced an asymmetric trade-off between typical and high stage performance; reduction of high stage error resulted in disproportionately larger error on a typical stage. The methods proposed in this study highlight the diversity-in-learning concept and help support future studies on adapting ensemble algorithms for resampling. This research contains many of the first instances of such methods for flow forecasting and, moreover, their efficacy in addressing the imbalance problem and heteroscedasticity, which are commonly observed in high-flow and flood-forecasting models.
In 2009 Fendt presented the first factory-installed tyre pressure regulation system on the market to be fully integrated in the vehicle concept. This innovation was awarded a silver medal at ...Agritechnica 2009. The benefits of optimum air pressure in tractor tyres are well known: in arable work damage to the soil can be minimised by reduced ground pressure and the concomitant reduction in surface compaction. Reducing wheel slip in the field also results in lower fuel consumption. At the same time a tractor’s towing capacity for the same vehicle weight can be increased by adjusting the tyre pressure. For transportation work rolling resistance is minimised and more stable road handling achieved by increasing the air pressure in the tyres. Adjusting tyre pressure cuts operating expenses, moreover, as both tyre wear and fuel costs can be minimised.
Data from the Genetic Association Information Network (GAIN) genome-wide association study (GWAS) in major depressive disorder (MDD) were used to explore previously reported candidate gene and ...single-nucleotide polymorphism (SNP) associations in MDD. A systematic literature search of candidate genes associated with MDD in case-control studies was performed before the results of the GAIN MDD study became available. Measured and imputed candidate SNPs and genes were tested in the GAIN MDD study encompassing 1738 cases and 1802 controls. Imputation was used to increase the number of SNPs from the GWAS and to improve coverage of SNPs in the candidate genes selected. Tests were carried out for individual SNPs and the entire gene using different statistical approaches, with permutation analysis as the final arbiter. In all, 78 papers reporting on 57 genes were identified, from which 92 SNPs could be mapped. In the GAIN MDD study, two SNPs were associated with MDD: C5orf20 (rs12520799; P=0.038; odds ratio (OR) AT=1.10, 95% CI 0.95-1.29; OR TT=1.21, 95% confidence interval (CI) 1.01-1.47) and NPY (rs16139; P=0.034; OR C allele=0.73, 95% CI 0.55-0.97), constituting a direct replication of previously identified SNPs. At the gene level, TNF (rs76917; OR T=1.35, 95% CI 1.13-1.63; P=0.0034) was identified as the only gene for which the association with MDD remained significant after correction for multiple testing. For SLC6A2 (norepinephrine transporter (NET)) significantly more SNPs (19 out of 100; P=0.039) than expected were associated while accounting for the linkage disequilibrium (LD) structure. Thus, we found support for involvement in MDD for only four genes. However, given the number of candidate SNPs and genes that were tested, even these significant may well be false positives. The poor replication may point to publication bias and false-positive findings in previous candidate gene studies, and may also be related to heterogeneity of the MDD phenotype as well as contextual genetic or environmental factors.
Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore ...the contribution of genotype-covariate interaction effects at common SNP loci. We find evidence for genotype-age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10
), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype-environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10
and LRT = 30.80, P = 1.42 × 10
), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.
We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome ...sequencing data that ∼97% and ∼68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ∼17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60-70% for height and 30-40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.
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