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Beta-lactam allergy is reported in 5–10% of children in North America, but up to 94–97% of patients are deemed not allergic after allergist assessment. The utility of standardized skin testing for penicillin allergy in the pediatric population has been recently questioned. Oral drug challenges when appropriate, are preferred over skin testing, and can definitively rule out immediate, IgE-mediated drug allergy. To our knowledge, this is the only pediatric study to assess the reliability of a penicillin allergy stratification tool using a paper and electronic clinical algorithm. By using an electronic algorithm, we identified 61 patients (of 95 deemed not allergic by gold standard allergist decision) as low risk for penicillin allergy, with no false negatives and without the need for allergist assessment or skin testing. In this study, we demonstrate that an electronic algorithm can be used by various pediatric clinicians when evaluating possible penicillin allergy to reliably identify low risk patients. We identified the electronic algorithm was superior to the paper version, capturing an even higher percentage of low risk patients than the paper version. By developing an electronic algorithm to accurately assess penicillin allergy risk based on appropriate history, without the need for diagnostic testing or allergist assessment, we can empower non-allergist health care professionals to safely de-label low risk pediatric patients and assist in alleviating subspecialty wait times for penicillin allergy assessment. [ABSTRACT FROM AUTHOR]

This paper describes a new homogenization algorithm validation methodology, and its use to assess the skill of eight different algorithms, when applied to synthetic daily temperature time series. These algorithms were ACMANT, Climatol (in both daily and monthly configurations), DAP, HOM, MAC‐D, MASH and SpliDHOM. Algorithms were tested on benchmark data replicating daily temperature variability in four regions in North America: Wyoming, the South East, the North East and the South West. These benchmarks contained plausible spatial and temporal correlation, differing station densities and both abrupt and gradual inhomogeneities. Algorithm ability was assessed both according to detection of inhomogeneities and correction of their effects, investigating bias, root‐mean‐square error (RMSE), linear trend recovery, station extremes and variability recovery. Inhomogeneities with a magnitude greater than 1°C were those most commonly detected with smaller inhomogeneities, and those that were not constant over time, proving harder to identify. Regional RMSE was always reduced by all algorithms and regional bias was reduced in over half of the region/scenario pairs. Trend recovery was variable, but the correct sign of regional trends was retained by all algorithms. Areas for future algorithm improvement include working with autocorrelated data and correcting moments higher than the mean. The data are available from https://www.metoffice.gov.uk/hadobs/benchmarks and the validation code from https://github.com/RachelKillick/Daily%5fbenchmarks allowing the extension of this work and its application to new algorithms. [ABSTRACT FROM AUTHOR]

Introduction: This paper seeks to identify which of three published formulas used for estimating the blood volume of normal human subjects correlates most closely with blood volumes measured in a published study where erythrocyte volume was determined by a method using 51Cr and a nonradioactive dye was used to determine the plasma volume. Methods: Blood volumes predicted by three published algorithms were compared with blood volume estimates from a study by Retzlaff et al. using the two-tailed Wilcoxon signed rank test and a robust version of the Bland–Altman test. Results: When applied to a sample of normal subjects selected from Mayo Clinic personnel and patients, the Nadler formula correlated more closely with blood volume measured using a radio nucleotide technique than did the Allen formula or one based on a saline haemodilution technique. Conclusions: The Nadler formula correlated more closely with blood volume measurements derived from Retzlaff's study than the other formulas for estimating blood volume in a population with height and weight distribution more consistent with that seen in North America. It should be used in preference to the Allen formula for estimating blood volume in adult patients currently undergoing cardiac surgical procedures. Saline haemodilution techniques used to measure blood volume require validation against more recently developed nuclear medicine techniques using statistical methods other than regression analysis. Until validated, they should be used with caution for estimating blood volume in adult patients currently undergoing cardiac surgical procedures. If a formula using height, weight and sex is used to estimate blood volume in the context of cardiac surgery, then it must be derived using a much more comprehensive sample of the population to which it is applied than has occurred to date. In particular, it should include broader distributions of height, weight and the presence or absence and type of significant valvular disease. [ABSTRACT FROM AUTHOR]

Maintaining an up-to-date repository of horizontal curve attributes is extremely important due to the role curves play in the safe operation of highways. Such attributes are typically collected using traditional surveying techniques which have been shown to be time-consuming, traffic disruptive, and potentially unsafe methods. This burden is further aggravated when the data collection is required on a large highway network across North America. To overcome this burden, this paper proposes a framework for network-level detection and extraction of horizontal curve elements from Light Detection and Ranging (LiDAR) data in a fully automated manner. The proposed technique was validated and then tested on LiDAR data collected on 242 km of highways in Alberta, Canada. The algorithm was successful in detecting all curves on the test highways and estimated their attributes with accuracies ranging from 96% to 100% demonstrating the robustness of the extraction method and the feasibility of performing the extraction on such a large scale. The proposed method is an alternative approach that could help transportation agencies maintain an updated inventory of horizontal alignment information on a large-scale. [ABSTRACT FROM AUTHOR]

Floating Catchment Area (FCA) methods are a popular tool to investigate accessibility to public facilities, in particular health care services. FCA approaches are attractive because, unlike other accessibility measures, they take into account the potential for congestion of facilities. This is done by 1) considering the population within the catchment area of a facility to calculate a variable that measures level of service, and then 2) aggregating the level of service by population centers subject to catchment area constraints. In this paper we discuss an effect of FCA approaches, an artifact that we term demand and level of service inflation. These artifacts are present in previous implementations of FCA methods. We argue that inflation makes interpretation of estimates of accessibility difficult, which has possible deleterious consequences for decision making. Next, we propose a simple and intuitive approach to proportionally allocate demandand and level of service in FCA calculations. The approach is based on a standardization of the impedance matrix, similar to approaches popular in the spatial statistics and econometrics literature. The result is a more intiuitive measure of accessibility that 1) provides a local version of the provider-to-population ratio; and 2) preserves the level of demand and the level of supply in a system. We illustrate the relevant issues with some examples, and then empirically by means of a case study of accessibility to family physicians in the Hamilton Census Metropolitan Area (CMA), in Ontario, Canada. Results indicate that demand and supply inflation/deflation affect the interpretation of accessibility analysis using existing FCA methods, and that the proposed adjustment can lead to more intuitive results. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Vehicular ad Hoc networks (VANETs) support a variety of applications ranging from critical safety applications to "infotainment" or "comfort" applications. In North America, 75 MHz of the spectrum in the 5.9 GHz band has been allocated for vehicular communication. Safety applications rely on event-driven "alert" messages as well as the periodic broadcast of Basic Safety Messages (BSMs) containing critical information, e.g., position, speed, and heading from participating vehicles. The limited channel capacity and high message rates needed to ensure an adequate level of awareness make the reliable delivery of BSMs a challenging problem for VANETs. In this paper, we propose a decentralized congestion control algorithm that uses variable transmission power levels to reduce the channel busy ratio while maintaining a high level of awareness for nearby vehicles. The simulation results indicate that the proposed approach is able to achieve a suitable balance between awareness and bandwidth usage. [ABSTRACT FROM AUTHOR]

• Method to evaluate allowable differential settlement for open top tank is proposed. • The proposed method is based on GMNA of 21 different tank models. • GMNA is performed to obtain the limiting settlement value for a tank geometry. • Proposed method is validated by randomly selected tanks and settlement profiles. • The drawback of the existing API 653 method-1 are highlighted. A method for evaluating the mechanical integrity of open-top cylindrical storage tanks subjected to a differential settlement is given. The settlement profile underneath the bottom circumference of the tank shell can be transformed into different harmonic components using the Fourier transformation. The existing method in American Petroleum Institute's standard API 653, which is currently used by the industry in North America, does not differentiate the effect of different harmonic components. Nevertheless, the proposed method evaluates a cumulative damage factor by considering the effects of first five harmonic components individually. The paper further discusses other limitations of the existing method in API 653 document. Numerous finite element analysis (FEA) simulations are conducted to formulate and validate the proposed method by employing geometric nonlinear algorithm with nonlinear plastic material properties (GMNA) in ABAQUS finite element software. The trend for limiting settlement values with respect to different harmonic components under consideration and different tank geometries are discussed. The proposed method is validated by performing FEA using four actual settlement data profiles on different tank geometries. Lastly, the comparison is drawn between the FEA results, the existing API 653 method and the proposed method. The results of the allowable settlement indicate that the existing method is not consistent with the FEA findings. For some of the actual settlement data, the existing method results in overly conservative values and for others it gives non-conservative values. Thus, the existing method may not capture the true behavior of tanks under settlement and needs modifications. The results of allowable settlement from the proposed method are found to be consistent with the FEA results for all different settlement data and tank geometries. Therefore, it is recommended that the new method is used instead of the existing method. [ABSTRACT FROM AUTHOR]

Fire disturbance is a significant component of the climate system. Analysis of satellite-derived burned areas has allowed the identification of fire patches and their morphology as a new resource for tracking fire spread to improve fire models used to assess the impact of fires on climate and the carbon cycle. A critical parameter of the flood-fill algorithm used to create fire patches is the cut-off (in days) below which it aggregates two contiguous burned pixels to the same fire patch. However, the current level of validation is insufficient to understand the effect of the cut-off values and sensor resolutions on the subsequent fire-patch morphology. The FRY v1.0 database of functional fire-patch traits (e.g., size, elongation, and direction) emanates from the analyses of two global burned-area products derived from MODIS and MERIS sensors with different spatial and temporal resolutions and with cut-off values of 3, 5, 9, and 14 days. To evaluate whether the FRY products are accurately identifying the spatial features of fire patches and what are the most realistic cut-off values to use in different sub-regions of North America, we propose a new functional diversity trait-based approach, which compares the satellite-derived fire patches to forest service perimeters as reference data. This paper shows the accuracy of the FRY fire patches ≥300 ha in North America during 2005–2011. Our analysis demonstrates that fire patches with a high cut-off of 14 days and those derived from the MODIS sensor, with their high temporal resolution, better identify the fire diversity in North America. In conclusion, our statistical framework can be used for assessing satellite-derived fire patches. Furthermore, the temporal resolution of satellite sensors is the most important factor in identifying fire patches — thus space agencies should consider it when planning the future development of cost-effective climate observation systems. Unlabelled Image • The trait-based method can be used for assessing satellite-derived fire patches. • Functional–structure relationships are complemented by functional dissimilarities. • High temporal resolution is most important for fire-patch identification. • Fire patches with a cut-off of 14 days are better identified in North America. [ABSTRACT FROM AUTHOR]

The planned simultaneous availability of visible and near-IR observations from the geostationary platforms of Tropospheric Emissions: Monitoring of Pollution (TEMPO) and Geostationary Operational Environmental Satellites (GOES) 16/17 Advanced Base Imager (ABI) will present the opportunity of deriving an accurate aerosol product taking advantage of both ABI's high spatial resolution in the visible range and TEMPO's sensitivity to aerosol absorption in the near-UV range. Because the wavelengths of ABI are similar to those of the Moderate Resolution Imaging Spectroradiometer (MODIS), existing aerosol algorithms of MODIS can be applied to ABI observations. In this work, we evaluate three distinct aerosol algorithms of MODIS deriving aerosol optical thickness (AOT) over land surfaces using visible and near-IR observations. The Dark Target (DT), Deep Blue (DB), and Multiangle Implementation of Atmospheric Correction (MAIAC) algorithms are all applied to the radiance measurements of MODIS on board the Aqua satellite. We have evaluated each algorithm by comparing the satellite-retrieved AOT to space-time collocated ground-based sun photometer measurements of the same parameter at 171 sites of the Aerosol Robotic Network (AERONET) over North America (NA). A spatiotemporal scheme collocating the satellite retrievals with the ground-based measurements was applied consistently to all three retrieval datasets. We find that the statistical performance of all three algorithms is comparable over darker surfaces over eastern NA with the MAIAC algorithm providing relatively better comparison over western NA sites characterized by inhomogeneous elevation and bright surfaces. The higher spatial resolution of the MAIAC product (1 km) allows a substantially larger number of matchups than DB 10 km and DT 10 km (DT 3 km) products by 115 % and 120 % (86 %), respectively, over eastern NA and by 150 % and 220 % (197 %) over western NA. The characterization of the error in AOT for the three aerosol products as a function of bidirectional surface reflectance derived from both MAIAC and an independent MOD09 atmospheric correction shows a systematic positive bias in DT retrievals over brighter surfaces, whereas DB and MAIAC retrievals show no such bias throughout the wide range of surface brightness, with MAIAC offering the lowest spread in errors. The results reported here represent an objective, unbiased evaluation of existing over-land aerosol retrieval algorithms of MODIS. The detailed statistical evaluation of the performance of each of these three algorithms may be used as guidance in the development of inversion schemes to derive aerosol properties from ABI or other MODIS-like sensors. [ABSTRACT FROM AUTHOR]

The tribe Plantagineae (Lamiales) is a group of plants with worldwide distribution, notorious for its complicated taxonomy and still unresolved natural history. We describe the result of a broadly sampled phylogenetic study of tribe. The expanded sampling dataset is based on the trnL-F spacer, rbcL, and ITS2 markers across all three included genera (Aragoa, Littorella and Plantago) and makes this the most comprehensive study to date. The other dataset uses five markers and provides remarkably good resolution throughout the tree, including support for all of the major clades. In addition to the molecular phylogeny, a morphology database of 114 binary characters was assembled to provide comparison with the molecular phylogeny and to develop a means to assign species not sampled in the molecular analysis to their most closely related species that were sampled. Based on the molecular phylogeny and the assignment algorithm to place unsampled species, a key to sections is presented, and a revised classification of the tribe is provided. We also include the description of new species from North America. [ABSTRACT FROM AUTHOR]