1,576 research outputs found
Assessing the knock-on effects of flooding on road transportation (article)
This is the final version. Available on open access from Elsevier via the DOI in this recordThe dataset associated with this article is available in ORE: https://doi.org/10.24378/exe.1384Flooding can affect every aspect of our lives and road transportation is no an exception. However, the interaction between floods and transportation was not investigated closely in the past. As transportation is the lifeline of any economy, it is essential to analyse potential dangers and threads that can lead to network capacity restraints. Considering the potential of flooding to affect large areas for long durations, disruptions to transportation can result in extensive knock-on effects. To examine how flooding can impact road transportation a novel methodology was developed into a software tool which integrates flood and traffic models. The flood is simulated with InfoWorks flood model and the traffic is represented by a detailed microscopic model (SUMO), which simulates individual vehicles and their interactions. The two systems are integrated in a dynamic way, whereby changes in the flood propagation dictates the temporal variation of road network capacity restrictions. Depending on the flood characteristics, a flooded road in the traffic model undergoes either a speed limit reduction or a complete closure. Once a road has been closed for traffic, vehicles that originally pass through it are forced to choose alternative routes to reach their unique destinations. The reroute will put an additional strain on a system that is already suffering reduced network capacity. The most vulnerable roads in the network are identified after a comparison of the traffic conditions under normal and flooded situation. The results indicate that the locations of flooded streets cannot be directly associated with the most severely congested areas emphasising the significance of the knock-on effects when describing flood impacts on road transportation.European Commissio
Toward standardized musculoskeletal ultrasound in pediatric rheumatology: Normal age related ultrasound findings
Educational recommendations for the conduct, content and format of EULAR musculoskeletal ultrasound Teaching the Teachers Courses
To produce educational guidelines for the conduct, content and format of theoretical and practical teaching at EULAR musculoskeletal ultrasound (MSUS) Teaching the Teachers (TTT) Courses
Preliminary definitions for the sonographic features of synovitis in children
Objectives Musculoskeletal ultrasonography (US) has the potential to be an important tool in the assessment of disease activity in childhood arthritides. To assess pathology, clear definitions for synovitis need to be developed first. The aim of this study was to develop and validate these definitions through an international consensus process. Methods The decision on which US techniques to use, the components to be included in the definitions as well as the final wording were developed by 31 ultrasound experts in a consensus process. A Likert scale of 1-5 with 1 indicating complete disagreement and 5 complete agreement was used. A minimum of 80% of the experts scoring 4 or 5 was required for final approval. The definitions were then validated on 120 standardized US images of the wrist, MCP and tibiotalar joints displaying various degrees of synovitis at various ages. Results B-Mode and Doppler should be used for assessing synovitis in children. A US definition of the various components (i.e. synovial hypertrophy, effusion and Doppler signal within the synovium) was developed. The definition was validated on still images with a median of 89% (range 80-100) of participants scoring it as 4 or 5 on a Likert scale. Conclusions US definitions of synovitis and its elementary components covering the entire pediatric age range were successfully developed through a Delphi process and validated in a web-based still images exercise. These results provide the basis for the standardized US assessment of synovitis in clinical practice and research
Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA
This is the final version. Available on open access from the American Geophysical Union via the DOI in this recordData Availability Statement:
The COVID-19 contagious persons per day data used for generating the COVID-19 hotspot prevalence in the study are available from the Dutch National Institute for Public Health and the Environment (RIVM) at https://data.rivm.nl/covid-19/COVID-19_prevalentie.json with license http://creativecommons.org/publicdomain/mark/1.0/deed.nl.Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently
Amendment of the OMERACT ultrasound definitions of joints' features in healthy children when using the DOPPLER technique
Background: Recently preliminary ultrasonography (US) definitions, in B mode, for normal components of pediatric joints have been developed by the OMERACT US group. The aim of the current study was to include Doppler findings in the evaluation and definition of normal joint features that can be visualized in healthy children at different age groups. Methods: A multistep approach was used. Firstly, new additional definitions of joint components were proposed during an expert meeting. In the second step, these definitions, along with the preliminary B-mode-US definitions, were tested for feasibility in an exercise in healthy children at different age groups. In the last step, a larger panel of US experts were invited to join a web-based consensus process in order to approve the developed definitions using the Delphi methodology. A Likert scale of 1-5 was used to assess agreement. Results: Physiological vascularity and fat pad tissue were identified and tested as two additional joint components in healthy children. Since physiological vascularity changes over the time in the growing skeleton, the final definition of Doppler findings comprised separate statements instead of a single full definition. A total of seven statements was developed and included in a written Delphi questionnaire to define and validate the new components. The final definitions for fat pad and physiological vascularity agreed by the group of experts reached 92.9% and 100% agreement respectively in a web survey. Conclusion: The inclusion of these two additional joints components which are linked to detection of Doppler signal in pediatric healthy joints will improve the identification of abnormalities in children with joint pathologies
Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model
Purpose: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI).
Materials and Methods: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas.
Results: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases.
Conclusion: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results
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Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.
Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels
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