Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study

BACKGROUND

Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records.

OBJECTIVE

This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.

METHODS

Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras.

RESULTS

There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras.

CONCLUSIONS

This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Bridging the Gaps in Meta-Omic Analysis: Workflows and Reproducibility

The past few years have seen significant advances in the study of complex microbial communities associated with the evolution of sequencing technologies and increasing adoption of whole genome shotgun sequencing methods over the once more traditional Amplicon-based methods. Although these advances have broadened the horizon of meta-omic analyses in planetary health, human health, and ecology from simple sample composition studies to comprehensive taxonomic and metabolic profiles, there are still significant challenges in processing these data. First, there is a widespread lack of standardization in data processing, including software choices and the ease of installing and running attendant software. This can lead to several inconsistencies, making comparing results across studies and reproducing original results difficult. We argue that these drawbacks are especially evident in metatranscriptomic analysis, with most analyses relying on ad hoc scripts instead of pipelines implemented in workflow managers. Additional challenges rely on integrating meta-omic data, since methods have to consider the biases in the library preparation and sequencing methods and the technical noise that can arise from it. Here, we critically discuss the current limitations in metagenomics and metatranscriptomics methods with a view to catalyze future innovations in the field of Planetary Health, ecology, and allied fields of life sciences. We highlight possible solutions for these constraints to bring about more standardization, with ease of installation, high performance, and reproducibility as guiding principles.

Should Transportation Be Transitioned to Ethanol with Carbon Capture and Pipelines or Electricity? A Case Study

An important issue today is whether gasoline vehicles should be replaced by flex-fuel vehicles (FFVs) that use ethanol-gasoline blends (e.g., E85), where some carbon dioxide (CO2) from ethanol's production is captured and piped, or battery-electric vehicles (BEVs) powered by wind or solar. This paper compares the options in a case study. It evaluates a proposal to capture fermentation CO2 from 34 ethanol refineries in 5 U.S. states and build an elaborate pipeline to transport the CO2 to an underground storage site. This "ethanol plan" is compared with building wind farms at the same cost to provide electricity for BEVs ("wind plan A"). Compared with the ethanol plan, wind plan A may reduce 2.4-4 times the CO2, save drivers in the five states $40-$66 billion (USD 2023) over 30 years even when BEVs initially cost $21,700 more than FFVs, require 1/400,000th the land footprint and 1/10th-1/20th the spacing area, and decrease air pollution. Even building wind to replace coal ("wind plan B") may avoid 1.5-2.5 times the CO2 as the ethanol plan. Thus, ethanol with carbon capture appears to be an opportunity cost that may damage climate and air quality, occupy land, and saddle consumers with high fuel costs for decades.

An Approach for Easy Detection of Buried FRP Composite/Non-Metallic Pipes Using Ground-Penetrating Radar

Pipelines remain the safest means of transporting natural gas and petroleum products. Nonetheless, the pipeline infrastructure in the US is facing major challenges, especially in terms of corrosion of steel/metallic pipes and excavation damage of onshore pipelines (leading to oil spills, explosions, and deaths). Corrosion of metallic pipelines can be avoided by using non-corrosive materials such as plastic pipes for low-pressure applications and glass-fiber-reinforced polymer (GFRP) composite pipes for transporting high-pressure oil and natural gas. However, buried non-metallic pipelines are not easily detectable, which can lead to increased excavation damage during construction and rehabilitation work. Alternative strategies for making buried non-metallic pipes easily locatable using ground-penetrating radar (GPR) were investigated in this study. Results from this study have shown that using carbon fabric or an aluminum foil overlay on non-metallic pipes before burying in soil significantly increases the reflected GPR signal amplitude, thereby making it easier to locate such pipelines. The reflected GPR signal amplitude for pipe sections with carbon fabric or aluminum foil overlays was found to have increased by a factor of up to 4.5 over the control samples. The results also highlight the importance of selecting the appropriate antenna frequency for GPR surveys, since wet silt loam soil and clay significantly reduce the penetration depths of the radar signals produced by the GPR antennae.

Study of the Precise Determination of Pipeline Geometries Using UAV Scanning Compared to Terrestrial Scanning, Aerial Scanning and UAV Photogrammetry

Transmission pipelines belong to technical infrastructure, the condition of which is subject to periodic monitoring. The research was to verify whether aerial measurement methods, especially UAV laser scanning, could determine the geometric shape of pipelines with a precision similar to that of terrestrial scanning, adopted as a reference method. The test object was a section of a district heating pipeline with two types of surfaces: matte and glossy. The pipeline was measured using four methods: terrestrial scanning, airborne scanning, UAV scanning and the structure from motion method. Then, based on the reference terrestrial scanning data, pipeline models were created, with which all methods were compared. The comparison made it possible to find that only the UAV scanning yielded results consistent with those of the terrestrial scanning for all the pipes. The differences usually did not exceed 10 mm, sometimes reaching 20 mm. The structure from motion method yielded unstable results. For the old, matte pipes, the results were similar to those of the UAV scan; however, for the new, shiny pipes, the differences were up to 60 mm.

Scalable and versatile container-based pipelines for de novo genome assembly and bacterial annotation

Background: Advancements in DNA sequencing technology have transformed the field of bacterial genomics, allowing for faster and more cost effective chromosome level assemblies compared to a decade ago. However, transforming raw reads into a complete genome model is a significant computational challenge due to the varying quality and quantity of data obtained from different sequencing instruments, as well as intrinsic characteristics of the genome and desired analyses. To address this issue, we have developed a set of container-based pipelines using Nextflow, offering both common workflows for inexperienced users and high levels of customization for experienced ones. Their processing strategies are adaptable based on the sequencing data type, and their modularity enables the incorporation of new components to address the community's evolving needs. Methods: These pipelines consist of three parts: quality control, de novo genome assembly, and bacterial genome annotation. In particular, the genome annotation pipeline provides a comprehensive overview of the genome, including standard gene prediction and functional inference, as well as predictions relevant to clinical applications such as virulence and resistance gene annotation, secondary metabolite detection, prophage and plasmid prediction, and more. Results: The annotation results are presented in reports, genome browsers, and a web-based application that enables users to explore and interact with the genome annotation results. Conclusions: Overall, our user-friendly pipelines offer a seamless integration of computational tools to facilitate routine bacterial genomics research. The effectiveness of these is illustrated by examining the sequencing data of a clinical sample of Klebsiella pneumoniae.

Two-Leak Case Diagnosis Based on Static Flow Model for Liquid Transmission Pipelines

The article deals with a diagnosis of multiple leaks from liquid transmission pipelines using analytical methods. Such solutions, based on advanced mathematical models of pipeline flow dynamics, usually turn out to be very complex and time-consuming. However, under certain operating conditions, a simpler approach may also be useful. Such an idea is presented in this paper, proposing two simplified methods for diagnosing double leakages. In principle, these methods apply to both simultaneous and non-simultaneous leaks. The first one uses a static model of a pipeline involving two leaks and takes advantage of the minimization of the objective function defined as the squared deviation of the modeled pressures from the pressures measured on the pipeline. The second method uses a pipeline flow model of a static type in combination with a gradient indicator aimed at detecting leaks and employing algorithms assigned to determining the location and size of leaks. The results of methods' validation, based on tests carried out with the use of measurement data obtained from an experimental water pipeline, were also presented. The outcomes of the performed tests proved the methods' effectiveness in terms of detection, isolation, localization, and intensity estimation of both simultaneous and non-simultaneous double leakages.

Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.

Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification

This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.

Non-Destructive Inspection of High Temperature Piping Combining Ultrasound and Eddy Current Testing

This paper presents an automated Non-Destructive Testing (NDT) system for the in-service inspection of orbital welds on tubular components operating at temperatures as high as 200 °C. The combination of two different NDT methods and respective inspection systems is here proposed to cover the detection of all potential defective weld conditions. The proposed NDT system combines ultrasounds and Eddy current techniques with dedicated approaches for dealing with high temperature conditions. Phased array ultrasound was employed, searching for volumetric defects within the weld bead volume while Eddy currents were used to look for surface and sub-surface cracks. The results from the phased array ultrasound results showed the effectiveness of the cooling mechanisms and that temperature effects on sound attenuation can be easily compensated for up to 200 °C. The Eddy current results showed almost no influence when temperatures were raised up to 300 °C.

Client Applications and Server-Side Docker for Management of RNASeq and/or VariantSeq Workflows and Pipelines of the GPRO Suite

The GPRO suite is an in-progress bioinformatic project for -omics data analysis. As part of the continued growth of this project, we introduce a client- and server-side solution for comparative transcriptomics and analysis of variants. The client-side consists of two Java applications called "RNASeq" and "VariantSeq" to manage pipelines and workflows based on the most common command line interface tools for RNA-seq and Variant-seq analysis, respectively. As such, "RNASeq" and "VariantSeq" are coupled with a Linux server infrastructure (named GPRO Server-Side) that hosts all dependencies of each application (scripts, databases, and command line interface software). Implementation of the Server-Side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server-Side can be installed, via a Docker container, in the user's PC under any operating system or on remote servers, as a cloud solution. "RNASeq" and "VariantSeq" are both available as desktop (RCP compilation) and web (RAP compilation) applications. Each application has two execution modes: a step-by-step mode enables each step of the workflow to be executed independently, and a pipeline mode allows all steps to be run sequentially. "RNASeq" and "VariantSeq" also feature an experimental, online support system called GENIE that consists of a virtual (chatbot) assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline jobs panel provides information about the status of each computational job executed in the GPRO Server-Side, while the expert system provides the user with a potential recommendation to identify or fix failed analyses. Our solution is a ready-to-use topic specific platform that combines the user-friendliness, robustness, and security of desktop software, with the efficiency of cloud/web applications to manage pipelines and workflows based on command line interface software.

Imposing calculations: The visibility and invisibility of harm in the Mackenzie Gas Project environmental assessment

Environmental assessment is an institutional apparatus through which proponents concede harm associated with extractive projects. Within these processes proponents define the nature and scope of harm, which is made visible through the production of indicators and measurements and made manageable through mitigation measures or economic compensation. That the activities of extractive industries may have effects on surrounding ecologies is rarely in question; proponents of extractive projects regularly concede that their activities will result in negative (but also positive) changes to environments and communities. What is often contested in the course of environmental assessment and regulatory processes is the "significance" of the impacts identified, the nature of the harm caused, and whether or not it is possible or acceptable to accommodate it. Drawing from ethnographic fieldwork conducted in the Sahtu Settlement Area, NWT during the Mackenzie Gas Project environmental assessment, along with regulatory documents and transcripts, this paper examines how proponents and regulatory regimes work to make the impacts of extractive industries visible, and how these logics deviate discursively and materially from many Indigenous peoples' understandings of appropriate relationships between human beings and nature.

Methane Emissions from Natural Gas Gathering Pipelines in the Permian Basin

The rapid reduction of methane emissions, especially from oil and gas (O&G) operations, is a critical part of slowing global warming. However, few studies have attempted to specifically characterize emissions from natural gas gathering pipelines, which tend to be more difficult to monitor on the ground than other forms of O&G infrastructure. In this study, we use methane emission measurements collected from four recent aerial campaigns in the Permian Basin, the most prolific O&G basin in the United States, to estimate a methane emission factor for gathering lines. From each campaign, we calculate an emission factor between 2.7 (+1.9/-1.8, 95% confidence interval) and 10.0 (+6.4/-6.2) Mg of CH₄ year^-1 km^-1, 14-52 times higher than the U.S. Environmental Protection Agency's national estimate for gathering lines and 4-13 times higher than the highest estimate derived from a published ground-based survey of gathering lines. Using Monte Carlo techniques, we demonstrate that aerial data collection allows for a greater sample size than ground-based data collection and therefore more comprehensive identification of emission sources that comprise the heavy tail of methane emissions distributions. Our results suggest that pipeline emissions are underestimated in current inventories and highlight the importance of a large sample size when calculating basinwide pipeline emission factors.

An Indirect Method for Determining the Local Heat Transfer Coefficient of Gas Flows in Pipelines

An indirect method and procedure for determining the local heat transfer coefficient in experimental studies on the intensity of heat transfer at a gas-surface interface is described. The article provides an overview of modern approaches and technical devices for determining the heat flux or friction stresses on surfaces in the study of thermophysical processes. The proposed method uses a constant-temperature hot-wire anemometer and a sensor with a thread sensitive element fixed on the surface of a fluoroplastic substrate. A substrate with the sensor's sensitive element was mounted flush with the wall of the investigated pipeline. This method is based on the Kutateladze-Leontiev approach (the laws of friction and heat transfer) and the hydrodynamic analogy of heat transfer (the Reynolds analogy): this is an assumption about the unity of momentum and heat transfer in a turbulent flow, which establishes a quantitative relationship between friction stresses on the heat exchange surface and heat transfer through this surface. The article presents a method for determining the speed of the developed measuring system. An example of a successful application of the proposed method in relation to the study of thermomechanical processes in the gas exchange systems of reciprocating internal combustion engines is described.

Designing and comparing cleaning pipelines for TMS-EEG data: a theoretical overview and practical example

Combining transcranial magnetic stimulation (TMS) with electroencephalography (EEG) is growing in popularity as a method for probing the reactivity and connectivity of neural circuits in basic and clinical research. However, using EEG to measure the neural responses to TMS is challenging due to the unique artifacts introduced by combining the two techniques. In this paper, we overview the artifacts present in TMS-EEG data and the offline cleaning methods used to suppress these unwanted signals. We then describe how open science practices, including the development of open-source toolboxes designed for TMS-EEG analysis (e.g., TESA - the TMS-EEG signal analyser), have improved the availability and reproducibility of TMS-EEG cleaning methods. We provide theoretical and practical considerations for designing TMS-EEG cleaning pipelines and then give an example of how to compare different pipelines using TESA. We show that changing even a single step in a pipeline designed to suppress decay artifacts results in TMS-evoked potentials (TEPs) with small differences in amplitude and spatial topography. The variability in TEPs resulting from the choice of cleaning pipeline has important implications for comparing TMS-EEG findings between research groups which use different online and offline approaches. Finally, we discuss the challenges of validating cleaning pipelines and recommend that researchers compare outcomes from TMS-EEG experiments using multiple pipelines to ensure findings are not related to the choice of cleaning methods. We conclude that the continued improvement, availability, and validation of cleaning pipelines is essential to ensure TMS-EEG reaches its full potential as a method for studying human neurophysiology.

Accurate Estimation of Yield Strength and Ultimate Tensile Strength through Instrumented Indentation Testing and Chemical Composition Testing

Federal rule changes governing natural gas pipelines have made non-destructive techniques, such as instrumented indentation testing (IIT), an attractive alternative to destructive tests for verifying properties of steel pipeline segments that lack traceable records. Ongoing work from Pacific Gas and Electric Company’s (PG&E) materials verification program indicates that IIT measurements may be enhanced by incorporating chemical composition data. This paper presents data from PG&E’s large-scale IIT program that demonstrates the predictive capabilities of IIT and chemical composition data, with particular emphasis given to differences between ultimate tensile strength (UTS) and yield strength (YS). For this study, over 80 segments of line pipe were evaluated through tensile testing, IIT, and compositional testing by optical emission spectroscopy (OES) and laboratory combustion. IIT measurements of UTS were, generally, in better agreement with destructive tensile data than YS and exhibited about half as much variability as YS measurements on the same sample. The root-mean squared error for IIT measurements of UTS and YS, respectively, were 27 MPa (3.9 ksi) and 43 MPa (6.2 ksi). Next, a machine learning model was trained to estimate YS and UTS by combining IIT with chemical composition data. The agreement between the model’s estimated UTS and tensile UTS values was only slightly better than the IIT-only measurements, with an RMSE of 21 MPa (3.1 ksi). However, the YS estimates showed much greater improvement with an improved RMSE of 27 MPa (3.9 ksi). The experimental, mechanical, and metallurgical factors that contributed to IIT’s ability to consistently determine destructive UTS, and the differences in its interaction with composition as compared to YS, are discussed herein.

Two Simultaneous Leak Diagnosis in Pipelines Based on Input-Output Numerical Differentiation

This paper addresses the two simultaneous leak diagnosis problem in pipelines based on a state vector reconstruction as a strategy to improve water shortages in large cities by only considering the availability of the flow rate and pressure head measurements at both ends of the pipeline. The proposed algorithm considers the parameters of both leaks as new state variables with constant dynamics, which results in an extended state representation. By applying a suitable persistent input, an invertible mapping in x can be obtained as a function of the input and output, including their time derivatives of the third-order. The state vector can then be reconstructed by means of an algebraic-like observer through the computation of time derivatives using a Numerical Differentiation with Annihilatorsconsidering its inherent noise rejection properties. Experimental results showed that leak parameters were reconstructed with accuracy using a test bed plant built at Cinvestav Guadalajara.

Proposed pipelines and environmental justice: Exploring the association between race, socioeconomic status, and pipeline proposals in the United States

The current natural gas and oil boom in North America requires new pipelines which pose environmental risks from the wellhead to their destinations. The environmental justice literature suggests that minority populations, people with low socio-economic status, and rural communities are disproportionally exposed to risks associated with potentially harmful land uses. Using data from the 2015 five-year American Community Survey and pipeline route data compiled by The FracTracker Alliance, this study tests whether the above assumptions are true for proposed FERC permitted natural gas transmission pipelines in the United States for which planned routes have been made available. Using binary logistic regression, the study provides only limited, and in some cases contradictory, support for these hypotheses. Although a higher share of highly educated residents significantly decreases the likelihood of a pipeline proposal in a census tract, a higher poverty rate also significantly lowers this probability. Only the share of Black and Asian residents is significantly (negatively) associated with pipeline proposals. However, to test whether this holds true for built pipelines, reliable routing data are needed, which are considered confidential in the United States.

The role of metadata in reproducible computational research

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

Accuracy and Uncertainty of Gradient Based Leak Localization Procedure for Liquid Transmission Pipelines

This paper describes issues of leakage localization in liquid transmission pipelines. It focuses on the standard leak localization procedure, which is based on the calculation of pressure gradients using pressure measurements captured along a pipeline. The procedure was verified in terms of an accuracy and uncertainty assessment of the resultant coordinate of a leak spot. An important aim of the verification was to assess the effectiveness of the procedure in the case of localization of low intensity leakages with a level of 0.25–2.00% of the nominal flow rate. An uncertainty assessment was carried out according to the GUM convention. The assessment was based on the metrological characteristics of measuring devices and measurement data obtained from the laboratory model of the pipeline.

Stress Distribution and Fracture Toughness of Underground Reinforced Plastic Pipe Composite

Reinforced composite materials have many applications in the aerospace, marine, and petroleum industries. Glass fiber-reinforced pipes are of considerable importance as pressurized vessels, infrastructure materials, and petroleum wastewater pipelines. The stress intensity factor due to through-thickness discontinuities is a major parameter in fracture mechanics to understand the failure mechanisms in glass fiber-composite pipes. The stress intensity factor is calculated for a composite cylinder subjected to internal pressure using the linear extended finite element method based on the law of energy release evaluation of surface damage. The analytical model needs two material properties; they are the tensile strength and the fracture toughness; therefore, a standard tensile test was carried out on a standard specimen taken from the pipe's wall thickness. Moreover, the compact tension test specimen was manufactured from the pipe's wall thickness to obtain the fracture toughness. The average tensile strength was measured as 21.5 MPa with a standard deviation of 5.59 MPa, moreover, the average Young's modulus was measured as 32.75 GPa with a standard deviation of 6.64 GPa. The fracture toughness was measured as 2322 (MPa √m) with a standard deviation of 142.5 (MPa √m), whereas the average surface release energy (G_IC) was 153.6 kJ/m2 with a standard deviation of 22.53 kJ/m². A valuable design equation was extracted from the finite element model to measure the effect of cracks on the hoop stress of the cylinder wall thickness based on a nonlinear model. Moreover, an acceptable equation was used to calculate the correction and shape factor of a cylinder with movable and unmovable through-thickness cracks. This study provides useful tools and guidance for the design and analysis of composite cylinders.

Close encounters of the fatal kind: Landscape features associated with central mountain caribou mortalities

In western Canada, anthropogenic disturbances resulting from resource extraction activities are associated with habitat loss and altered predator-prey dynamics. These habitat changes are linked to increased predation risk and unsustainable mortality rates for caribou (Rangifer tarandus caribou). To inform effective habitat restoration, our goal was to examine whether specific linear disturbance features were associated with caribou predation in central mountain caribou ranges. We used predation-caused caribou mortalities and caribou GPS-collar data collected between 2008 and 2015 to assess caribou predation risk within and outside of protected areas at four spatio-temporal scales: habitat use during the (a) 30 days, (b) 7 days, and (c) 24 hours prior to caribou being killed, and (d) characteristics at caribou kill site locations. Outside of protected areas, predation risk increased closer to pipelines, seismic lines, and streams. Within protected areas, predation risk increased closer to alpine habitat. Factors predicting predation risk differed among spatio-temporal scales and linear feature types: predation risk increased closer to pipelines during the 30 and 7 days prior to caribou being killed and closer to seismic lines during the 30 days, 7 days, and 24 hours prior, but decreased closer to roads during the 30 days prior to being killed. By assessing habitat use prior to caribou being killed, we identified caribou predation risk factors that would not have been detected by analysis of kill site locations alone. These results provide further evidence that restoration of anthropogenic linear disturbance features should be an immediate priority for caribou recovery in central mountain caribou ranges.

MP3: Medical Software for Processing Multi-Parametric Images Pipelines

This article presents an open source software able to convert, display, and process medical images. It differentiates itself from the existing software by its ability to design complex processing pipelines and to wisely execute them on a large databases. An MP3 pipeline can contain unlimited homemade or ready-made processes and can be carried out with a parallel execution system. As a viewer, MP3 allows display of up to four images together and to draw Regions Of Interest (ROI). Two applications showing the strengths of the software are presented as examples: a preclinical study involving Magnetic Resonance Imaging (MRI) data and a clinical one involving Computed Tomography (CT) images. MP3 is downloadable at https://github.com/nifm-gin/MP3.

Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals

There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman-Harris window), EEG time window choices (-750 ms to 0 ms and -250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch's method, Fast Fourier Transform, and Burg's method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared.

Laboratory Testing of FBGs for Pipeline Monitoring

The monitoring of the effects of geohazards on pipelines can be addressed by optical fiber Bragg gratings (FBGs). They are sensitive to strain and bending, and are installed on the external surface of pipelines at discrete locations. A joint approach of theoretical analysis and laboratory experiments is useful to check the reliability of the performance of this technology. We focus on the theoretical analysis of pipeline buckling and investigate the reliability of FBG monitoring both by examining the analytical model available and by performing a laboratory-scale experiment. The novelty lies in the analysis of models and methods originally developed for the detection of pipeline upheaval buckling caused by externally imposed forces in the context of service loads (temperature). Although thermal strain is very relevant in view of its potentially disruptive effects on both pipelines and the FBG response, it has not been yet fully investigated. We point out the merits of the approach, such as the functionality and simplicity of design, the accessibility and inexpensiveness of materials, the controllability and repeatability of processes, the drawbacks are also described, such as temperature effects, the problem of slipping of gages and the challenge of performing quasi-distributed strain measurements.

Analytical Approaches for ATAC-seq Data Analysis

ATAC-seq, the assay for transposase-accessible chromatin using sequencing, is a quick and efficient approach to investigating the chromatin accessibility landscape. Investigating chromatin accessibility has broad utility for answering many biological questions, such as mapping nucleosomes, identifying transcription factor binding sites, and measuring differential activity of DNA regulatory elements. Because the ATAC-seq protocol is both simple and relatively inexpensive, there has been a rapid increase in the availability of chromatin accessibility data. Furthermore, advances in ATAC-seq protocols are rapidly extending its breadth to additional experimental conditions, cell types, and species. Accompanying the increase in data, there has also been an explosion of new tools and analytical approaches for analyzing it. Here, we explain the fundamentals of ATAC-seq data processing, summarize common analysis approaches, and review computational tools to provide recommendations for different research questions. This primer provides a starting point and a reference for analysis of ATAC-seq data. © 2020 Wiley Periodicals LLC.

Oscillating U-Shaped Body for Underwater Piezoelectric Energy Harvester Power Optimization

Vibration energy harvesting (VeH) techniques by means of intentionally designed mechanisms have been used in the last decade for frequency bandwidth improvement under excitation for adequately high-vibration amplitudes. Oil, gas, and water are vital resources that are usually transported by extensive pipe networks. Therefore, wireless self-powered sensors are a sustainable choice to monitor in-pipe system applications. The mechanism, which is intended for water pipes with diameters of 2–5 inches, contains a piezoelectric beam assembled to the oscillating body. A novel U-shaped geometry of an underwater energy harvester has been designed and implemented. Then, the results have been compared with the traditional circular cylinder shape. At first, a numerical study has been carried at Reynolds numbers Re = 3000, 6000, 9000, and 12,000 in order to capture as much as kinetic energy from the water flow. Consequently, unsteady Reynolds Averaged Navier–Stokes (URANS)-based simulations are carried out to investigate the dynamic forces under different conditions. In addition, an Adaptive Differential Evolution (JADE) multivariable optimization algorithm has been implemented for the optimal design of the harvester and the maximization of the power extracted from it. The results show that the U-shaped geometry can extract more power from the kinetic energy of the fluid than the traditional circular cylinder harvester under the same conditions.

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

Air Emissions from Natural Gas Facilities in New York State

While New York has banned fracking, new and expanded natural gas pipelines are being constructed across the state. Our previous studies have reported that compressor stations are a major source of air pollution at fracking sites. We have used two federal datasets, the U.S. Environmental Protection Agency's (EPA) National Emissions Inventory and Greenhouse Gas Inventory, to determine what is known concerning emissions from the compressor stations along natural gas pipelines in the state. From a total of 74 compressor stations only 18 report to EPA on emissions. In the seven year period between 2008 and 2014 they released a total of 36.99 million pounds of air pollutants, not including CO₂ and methane. This included emissions of 39 chemicals known to be human carcinogens. There was in addition 6.1 billion pounds of greenhouse gases release from ten stations in a single year. These data clearly underestimate the total releases from the state's natural gas transportation and distribution system. However, they demonstrate significant releases of air pollutants, some of which are known to cause human disease. In addition, they release large amounts of greenhouse gases that contribute to climate change.

SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines

BACKGROUND

The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complex workflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machine learning.

FINDINGS

SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline.

CONCLUSIONS

SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine learning, through a flexible application programming interface suitable for scientists used to programming or scripting.

Candidate gene identification of existing or induced mutations with pipelines applicable to large genomes

Bulked segregant analysis (BSA) is used to identify existing or induced variants that are linked to phenotypes. Although it is widely used in Arabidopsis and rice, it remains challenging for crops with large genomes, such as maize. Moreover, analysis of huge data sets can present a bottleneck linking phenotypes to their molecular basis, especially for geneticists without programming experience. Here, we identified two genes of maize defective kernel mutants with newly developed analysis pipelines that require no programing skills and should be applicable to any large genome. In the 1970s, Neuffer and Sheridan generated a chemically induced defective kernel (dek) mutant collection with the potential to uncover critical genes for seed development. To locate such mutations, the dek phenotypes were introgressed into two inbred lines to take advantage of maize haplotype variations and their sequenced genomes. We generated two pipelines that take fastq files derived from next-generation (nextGen) paired-end DNA and cDNA sequencing as input, call on several well established and freely available genomic analysis tools to call SNPs and INDELs, and generate lists of the most likely causal mutations together with variant index plots to locate the mutation to a specific sequence position on a chromosome. The pipelines were validated with a known strawberry mutation before cloning the dek mutants, thereby enabling phenotypic analysis of large genomes by next-generation sequencing.

Detection of Internal Metal Loss in Steel Pipes and Storage Tanks via Magnetic-Based Fiber Optic Sensor

A monitoring solution was developed for detection of material loss in metals such as carbon steel using the force generated by permanent magnets in addition to the optical strain sensing technology. The working principle of the sensing system is related to the change in thickness of a steel plate, which typically occurs due to corrosion. As thickness decreases, the magnetostatic force between the magnet and the steel structure also decreases. This, in turn, affects the strain measured using the optical fiber. The sensor prototype was designed and built after verifying its sensitivity using a numerical model. The prototype was tested on steel plates of different thicknesses to establish the relationship between the metal thickness and measured strain. The results of experiments and numerical models demonstrate a strong relationship between the metal thickness and the measured strain values.

CCP4i2: the new graphical user interface to the CCP4 program suite

The CCP4 (Collaborative Computational Project, Number 4) software suite for macromolecular structure determination by X-ray crystallography groups brings together many programs and libraries that, by means of well established conventions, interoperate effectively without adhering to strict design guidelines. Because of this inherent flexibility, users are often presented with diverse, even divergent, choices for solving every type of problem. Recently, CCP4 introduced CCP4i2, a modern graphical interface designed to help structural biologists to navigate the process of structure determination, with an emphasis on pipelining and the streamlined presentation of results. In addition, CCP4i2 provides a framework for writing structure-solution scripts that can be built up incrementally to create increasingly automatic procedures.

Automating tasks in protein structure determination with the clipper python module

Scripting programming languages provide the fastest means of prototyping complex functionality. Those with a syntax and grammar resembling human language also greatly enhance the maintainability of the produced source code. Furthermore, the combination of a powerful, machine-independent scripting language with binary libraries tailored for each computer architecture allows programs to break free from the tight boundaries of efficiency traditionally associated with scripts. In the present work, we describe how an efficient C++ crystallographic library such as Clipper can be wrapped, adapted and generalized for use in both crystallographic and electron cryo-microscopy applications, scripted with the Python language. We shall also place an emphasis on best practices in automation, illustrating how this can be achieved with this new Python module.

Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines

This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced), and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM) classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%.

ELIXIR pilot action: Marine metagenomics - towards a domain specific set of sustainable services

Metagenomics, the study of genetic material recovered directly from environmental samples, has the potential to provide insight into the structure and function of heterogeneous microbial communities.  There has been an increased use of metagenomics to discover and understand the diverse biosynthetic capacities of marine microbes, thereby allowing them to be exploited for industrial, food, and health care products. This ELIXIR pilot action was motivated by the need to establish dedicated data resources and harmonized metagenomics pipelines for the marine domain, in order to enhance the exploration and exploitation of marine genetic resources. In this paper, we summarize some of the results from the ELIXIR pilot action "Marine metagenomics - towards user centric services".

ViennaNGS: A toolbox for building efficient next- generation sequencing analysis pipelines

Recent achievements in next-generation sequencing (NGS) technologies lead to a high demand for reuseable software components to easily compile customized analysis workflows for big genomics data. We present ViennaNGS, an integrated collection of Perl modules focused on building efficient pipelines for NGS data processing. It comes with functionality for extracting and converting features from common NGS file formats, computation and evaluation of read mapping statistics, as well as normalization of RNA abundance. Moreover, ViennaNGS provides software components for identification and characterization of splice junctions from RNA-seq data, parsing and condensing sequence motif data, automated construction of Assembly and Track Hubs for the UCSC genome browser, as well as wrapper routines for a set of commonly used NGS command line tools.

The anaesthesia gas supply system

The anaesthesia gas supply system is designed to provide a safe, cost-effective and convenient system for the delivery of medical gases at the point of-use. The doctrine of the anaesthesia gas supply system is based on four essential principles: Identity, continuity, adequacy and quality. Knowledge about gas supply system is an integral component of safe anaesthetic practice. Mishaps involving the malfunction or misuse of medical gas supply to operating theatres have cost many lives. The medical gases used in anaesthesia and intensive care are oxygen, nitrous oxide, medical air, entonox, carbon dioxide and heliox. Oxygen is one of the most widely used gases for life-support and respiratory therapy besides anaesthetic procedures. In this article, an effort is made to describe the production, storage and delivery of anaesthetic gases. The design of anaesthesia equipment must take into account the local conditions such as climate, demand and power supply. The operational policy of the gas supply system should have a backup plan to cater to the emergency need of the hospital, in the event of the loss of the primary source of supply.

Identification and validation of loss of function variants in clinical contexts

The choice of an appropriate variant calling pipeline for exome sequencing data is becoming increasingly more important in translational medicine projects and clinical contexts. Within GOSgene, which facilitates genetic analysis as part of a joint effort of the University College London and the Great Ormond Street Hospital, we aimed to optimize a variant calling pipeline suitable for our clinical context. We implemented the GATK/Queue framework and evaluated the performance of its two callers: the classical UnifiedGenotyper and the new variant discovery tool HaplotypeCaller. We performed an experimental validation of the loss-of-function (LoF) variants called by the two methods using Sequenom technology. UnifiedGenotyper showed a total validation rate of 97.6% for LoF single-nucleotide polymorphisms (SNPs) and 92.0% for insertions or deletions (INDELs), whereas HaplotypeCaller was 91.7% for SNPs and 55.9% for INDELs. We confirm that GATK/Queue is a reliable pipeline in translational medicine and clinical context. We conclude that in our working environment, UnifiedGenotyper is the caller of choice, being an accurate method, with a high validation rate of error-prone calls like LoF variants. We finally highlight the importance of experimental validation, especially for INDELs, as part of a standard pipeline in clinical environments.

A Review of Fatigue Crack Growth for Pipeline Steels Exposed to Hydrogen

Hydrogen pipeline systems offer an economical means of storing and transporting energy in the form of hydrogen gas. Pipelines can be used to transport hydrogen that has been generated at solar and wind farms to and from salt cavern storage locations. In addition, pipeline transportation systems will be essential before widespread hydrogen fuel cell vehicle technology becomes a reality. Since hydrogen pipeline use is expected to grow, the mechanical integrity of these pipelines will need to be validated under the presence of pressurized hydrogen. This paper focuses on a review of the fatigue crack growth response of pipeline steels when exposed to gaseous hydrogen environments. Because of defect-tolerant design principles in pipeline structures, it is essential that designers consider hydrogen-assisted fatigue crack growth behavior in these applications.

The threats from oil spills: now, then, and in the future

The ongoing oil spill from the blown-out well by the name of Macondo, drilled by the ill-fated rig Deepwater Horizon, has many features in common with another blowout in the Mexican Gulf that happened three decades ago. Then the oil gushed out from the Ixtoc I well drilled by the Sedco 135-F semi-submersible rig. In the years between these catastrophes, the source and nature of oil spills have undergone large changes. Huge spills from tankers that ran aground or collided used to be what caught the headlines and caused large ecological damage. The number and size of such accidental spills have decreased significantly. Instead, spills from ageing, ill-maintained or sabotaged pipelines have increased, and places like Arctic Russia, the Niger Delta, and the northwestern Amazon have become sites of reoccurring oil pollution. As for blowouts, there is no clear trend with regard to the number of incidences or amounts of spilled oil, but deepwater blowouts are much harder to cap and thus tend to go on longer and result in the release of larger quantities of oil. Also, oil exploration and extraction is moving into ever-deeper water and into stormier and icier seas, increasing potential risks. The risk for reoccurring spills like the two huge Mexican Gulf ones is eminent and must be reduced.