Performance Of Pipeline Research Articles

The Behavior of Elbow Elements at Pure Bending Applications Compared to Beam and Shell Element Models

Abstract This paper studies the response of ELBOW31 and ELBOW31B element types under pure bending conditions, using shell and beam element models for benchmarking. Various model lengths are evaluated, showing that a model length of six pipe diameters exhibits a hardening effect when total strain exceeds 3.5%, though a strain up to 1% is deemed sufficient for pipeline design. The study examines the effects of ovality modes and boundary conditions such as NOWARP and NOOVAL on the bending response. ELBOW31 with one or two ovality modes yields accurate results, while additional ovality modes or zero ovality mode can lead to overprediction of the elastic bending moment capacity. The introduction of the NOWARP condition enhances the accuracy of the ELBOW31 model, while the NOOVAL condition alone produces unrealistic results. The simplified ELBOW31B model shows good agreement with the ELBOW31-NOWARP model but similarly overpredicts the bending moment when zero ovality mode is used. The study also finds that Poisson's ratio and model length have no significant impact on the bending response when no restrictions are applied. Additional analyses, as presented in Appendices A and B, highlight the importance of D/t ratios in pipeline performance. A D/t ratio of 20 offers a stiffer response with reduced ovalization, while a D/t ratio of 50 results in greater flexibility and increased ovalization. These findings provide valuable insights for the selection of element types, boundary conditions, and D/t ratios in robust pipeline design.

Proposal of an alternative method for residual stress measurements in clad nickel alloy after shot peening

Cladding of Ni-based superalloy 625 is often used to enhance mechanical resistance and corrosion performance of the high strength low alloy pipeline, for use in CO2 injection units in oil wells. It is well known that the use of the shot peening can improve its properties by creating a thin surface layer with compressive residual stresses. In this regard, the aim of this work is to propose an alternative method to evaluate the residual stresses of clad Ni-based superalloy 625 based on the deflection measurements of a thin strip separated from the shot peened samples by EDM cutting. The stress distribution in shot peened layer was measured by XRD sin2ψ method with electrolytic removal of surface layers. Measurements by the alternative and XRD method showed that the difference between the average stress values determined by these methods is 4%.

The reliability of pre-deformation to control lateral buckling of pipelines: An evaluation based on observed embedment trends

Pre-deformation of a pipeline into a continuous sinusoidal wave-like form has been shown to be effective at controlling lateral buckling of subsea pipelines due to a substantially lower axial stiffness and the limiting of maximum strain at any location. This paper explores the feasibility and reliability of using such an approach, with an existing operating pipeline, which was installed using zero-radius bend (ZRB) initiator structures, used for comparison. Survey data of the pipeline profile and seabed bathymetry are adopted along with the pipe-soil interaction (PSI) inputs from the original design allowing a like-for-like comparison of the two approaches to management of lateral buckling. The comparison shows that, for the assumptions made in the numerical modelling, use of a pre-deformed pipeline results in lower strain than using ZRBs. Furthermore, the performance of the pre-deformed pipeline is robust, and shown to be unaffected by uncertainties in horizontal out-of-straightness, PSI input and seabed features. This study shows that pre-deformed pipelines can be an effective alternative for controlling the lateral buckling of subsea pipelines, which eliminates the need for buckle initiation structures to be installed along the pipeline route. This provides impetus for further work on installation methodologies to create the required level of pre-deformation.

Study of hydraulic transport characteristics and erosion wear of twisted four-lobed pipe based on CFD-DEM

Pipeline hydraulic transportation is extensively utilized across diverse sectors, with enhancing the performance of pipeline hydrodynamic transport and minimizing erosion wear on the pipeline walls being essential for ensuring the stability of pipeline operations. This paper introduces a methodology for the hydraulic transport of a twisted four-lobed pipe, employing a numerical and erosion model developed through the CFD-DEM (computational fluid dynamics and discrete element method) coupling approach. An experimental circulating flow platform is constructed for validation purposes. The performance of the pipe is assessed by analyzing key indices including fluid velocity, pressure drop, particle trajectory, and erosion wear. The results indicate that twisted four-lobed pipe enhances fluid flow rates, facilitating particle discharge and mitigating accumulation, with reduced wear compared to the twin twist triangle spiral pipe. The analysis of structural parameters’ impact on hydraulic conveyance is also presented. These findings offer theoretical insights for optimizing pipeline performance in hydraulic conveyance while minimizing wear.

Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports

AimsThe primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies.MethodsThe study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system’s computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the presence or absence of pCR. Secondly, we fine-tuned the Generative Pre-trained Transformer-2 (GPT-2) model by attaching a simple feed-forward neural network (FFNN) layer to improve the detection performance of pCR from pathology reports.ResultsIn a cohort of 351 female breast cancer patients who underwent neoadjuvant chemotherapy (NAC) and subsequent surgery between 2010 and 2017 in Calgary, the optimized method displayed a sensitivity of 95.3% (95%CI: 84.0–100.0%), a positive predictive value of 90.9% (95%CI: 76.5–100.0%), and an F1 score of 93.0% (95%CI: 83.7–100.0%). The results, achieved through diverse LLM integration, surpassed traditional machine learning models, underscoring the potential of LLMs in clinical pathology information extraction.ConclusionsThe study successfully demonstrates the efficacy of LLMs in interpreting and processing digital pathology data, particularly for determining pCR in breast cancer patients post-NAC. The superior performance of LLM-based pipelines over traditional models highlights their significant potential in extracting and analyzing key clinical data from narrative reports. While promising, these findings highlight the need for future external validation to confirm the reliability and broader applicability of these methods.

Seismic Performance Evaluation of Pipelines Buried in Sandy Soils Reinforced with FRP Micropiles: A Numerical Study

Unstable sandy soil poses significant challenges for buried pipelines, particularly due to the increased risk of displacement and stress-induced fractures resulting from soil settlement and earthquake-induced ground deformation. These concerns are especially critical in seismically active regions where underground infrastructure is at higher risk. Fiber-reinforced polymer (FRP) composites present a promising and sustainable alternative for deep foundations, offering durability and reduced maintenance costs compared to conventional materials. This study introduces a novel approach to enhancing the seismic performance of pipelines buried in sandy soils by numerically investigating a three-dimensional (3D) multipipe grouting micro anti-slide pile system, utilizing a polyurethane polymer slurry as the grouting material. Key parameters such as pile spacing, diameter, and length, along with the effects of soil wetting and various earthquake intensities, were examined under the influence of surface loads exerted by a fully loaded truck. The results demonstrate that using polymer micropiles significantly reduces soil and pipeline settlement by 15% to 50%, with larger pile diameters and lengths further decreasing settlement and strain on pipelines. While seismic excitation increases settlement, polymer grouting effectively mitigates this impact, leading to substantial reductions in settlement.

The GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring.

Polygenic scoring is an approach for estimating an individual's likelihood of a given outcome. Polygenic scores are typically calculated from genome-wide association study (GWAS) summary statistics and individual-level genotype data for the target sample. Going from genotype to interpretable polygenic scores involves many steps and there are many methods available, limiting the accessibility of polygenic scores for research and clinical application. Additional challenges exist for studies in ancestrally diverse populations. We have implemented the leading polygenic scoring methodologies within an easy-to-use pipeline called GenoPred. Here, we present the GenoPred pipeline, an easy-to-use, high-performance, reference-standardized, and reproducible workflow for polygenic scoring. It requires minimal inputs and offers various configuration options to cater to a range of use cases. GenoPred implements a comprehensive set of analyses, including genotype and GWAS quality control, target sample ancestry inference, polygenic score file generation using a range of leading methods, and target sample scoring. GenoPred standardizes the polygenic scoring process using reference genetic data, providing interpretable polygenic scores. The pipeline is applicable to GWAS and targets data from any population within the reference, facilitating studies of diverse ancestry. GenoPred is a Snakemake pipeline with associated Conda software environments, ensuring reproducibility. We apply the pipeline to UK Biobank data demonstrating the pipeline's simplicity, efficiency, and performance. The GenoPred pipeline provides a novel resource for polygenic scoring, integrating a range of complex processes within an easy-to-use framework. GenoPred widens access to the leading polygenic scoring methodology and their application to studies of diverse ancestry. Freely available on the web at https://github.com/opain/GenoPred.

Development and validation of an open-source pipeline for automatic population of case report forms from electronic health records: a pediatric multi-center prospective study

Clinical trials and registry studies are essential for advancing research and developing novel treatments. However, these studies rely on manual entry of thousands of variables for each patient. Repurposing real-world data can significantly simplify the data collection, reduce transcription errors, and make the data entry process more efficient, consistent, and cost-effective. We developed an open-source computational pipeline to collect laboratory and medication information from the electronic health record (EHR) data and populate case report forms. The pipeline was developed and validated with data from two independent pediatric hospitals in the US as part of the Long-terM OUtcomes after Multisystem Inflammatory Syndrome In Children (MUSIC) study. Our pipeline allowed the completion of two of the most time-consuming forms. We compared automatically extracted results with manually entered values in one hospital and applied the pipeline to a second hospital, where the output served as the primary data source for case report forms. We extracted and populated 51,845 laboratory and 4913 medication values for 159 patients in two hospitals participating in a prospective pediatric study. We evaluated pipeline performance against data for 104 patients manually entered by clinicians in one of the hospitals. The highest concordance was found during patient hospitalization, with 91.59% of the automatically extracted laboratory and medication values corresponding with the manually entered values. In addition to the successfully populated values, we identified an additional 13,396 laboratory and 567 medication values of interest for the study. The automatic data entry of laboratory and medication values during admission is feasible and has a high concordance with the manually entered data. By implementing this proof of concept, we demonstrate the quality of automatic data extraction and highlight the potential of secondary use of EHR data to advance medical science by improving data entry efficiency and expediting clinical research. NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685.

NCBench: providing an open, reproducible, transparent, adaptable, and continuous benchmark approach for DNA-sequencing-based variant calling

We present the results of the human genomic small variant calling benchmarking initiative of the German Research Foundation (DFG) funded Next Generation Sequencing Competence Network (NGS-CN) and the German Human Genome-Phenome Archive (GHGA). In this effort, we developed NCBench, a continuous benchmarking platform for the evaluation of small genomic variant callsets in terms of recall, precision, and false positive/negative error patterns. NCBench is implemented as a continuously re-evaluated open-source repository. We show that it is possible to entirely rely on public free infrastructure (Github, Github Actions, Zenodo) in combination with established open-source tools. NCBench is agnostic of the used dataset and can evaluate an arbitrary number of given callsets, while reporting the results in a visual and interactive way. We used NCBench to evaluate over 40 callsets generated by various variant calling pipelines available in the participating groups that were run on three exome datasets from different enrichment kits and at different coverages. While all pipelines achieve high overall quality, subtle systematic differences between callers and datasets exist and are made apparent by NCBench.These insights are useful to improve existing pipelines and develop new workflows. NCBench is meant to be open for the contribution of any given callset. Most importantly, for authors, it will enable the omission of repeated re-implementation of paper-specific variant calling benchmarks for the publication of new tools or pipelines, while readers will benefit from being able to (continuously) observe the performance of tools and pipelines at the time of reading instead of at the time of writing.

A Deep Learning Approach to Uncover Voltage-Gated Ion Channels' Intermediate States.

Owing to recent advancements in cryo-electron microscopy, voltage-gated ion channels have gained a greater comprehension of their structural characteristics. However, a significant enigma remains unsolved for a large majority of these channels: their gating mechanism. This mechanism, which encompasses the conformational changes between open and closed states, is pivotal to their proper functioning. Beyond the binary states of open and closed, an ensemble of intermediate states defines the transition path in-between. Due to the lack of experimental data, one might resort to molecular dynamics simulations as an alternative to decipher these states and the transitions between them. However, the high-energy barriers and the colossal time scales involved hinder access to the latter. We present here an application of deep learning as a reliable pipeline for a comprehensive exploration of voltage-gated ion channel conformational rearrangements during gating. We showcase the pipeline performance specifically on the Kv1.2 voltage sensor domain and confront the results with existing data. We demonstrate how our physics-based deep learning approach contributes to the theoretical understanding of these channels and how it might provide further insights into the exploration of channelopathies.

Simulated High Throughput Sequencing Datasets: A Crucial Tool for Validating Bioinformatic Pathogen Detection Pipelines.

The validation of diagnostic assays in plant pathogen detection is a critical area of research. It requires the use of both negative and positive controls containing a known quantity of the target pathogen, which are crucial elements when calculating analytical sensitivity and specificity, among other diagnostic performance metrics. High Throughput Sequencing (HTS) is a method that allows the simultaneous detection of a theoretically unlimited number of plant pathogens. However, accurately identifying the pathogen from HTS data is directly related to the bioinformatic pipeline utilized and its effectiveness at correctly assigning reads to their associated taxa. To this day, there is no consensus about the pipeline that should be used to detect the pathogens in HTS data, and results often undergo review and scientific evaluation. It is, therefore, imperative to establish HTS resources tailored for evaluating the performance of bioinformatic pipelines utilized in plant pathogen detection. Standardized artificial HTS datasets can be used as a benchmark by allowing users to test their pipelines for various pathogen infection scenarios, some of the most prevalent being multiple infections, low titer pathogens, mutations, and new strains, among others. Having these artificial HTS datasets in the hands of HTS diagnostic assay validators can help resolve challenges encountered when implementing bioinformatics pipelines for routine pathogen detection. Offering these purely artificial HTS datasets as benchmarking tools will significantly advance research on plant pathogen detection using HTS and enable a more robust and standardized evaluation of the bioinformatic methods, thereby enhancing the field of plant pathogen detection.

Development, deployment and scaling of operating room-ready artificial intelligence for real-time surgical decision support

Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous (“Go”/”No-Go”) zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed.

Optimization of indirect wastewater characterization using led spectrophotometry: a comparative analysis of regression, scaling, and dimensionality reduction methods

LED spectrophotometry is a robust technique for the indirect characterization of wastewater pollutant load through correlation modeling. To tackle this issue, a dataset with 1300 samples was collected, from both raw and treated wastewater from 45 wastewater treatment plants in Spain and Chile collected over 4 years. The type of regressor, scaling, and dimensionality reduction technique and nature of the data play crucial roles in the performance of the processing pipeline. Eighty-four pipelines were tested through exhaustive experimentation resulting from the combination of 7 regression techniques, 3 scaling methods, and 4 possible dimensional reductions. Those combinations were tested on the prediction of chemical oxygen demand (COD) and total suspended solids (TSS). Each pipeline underwent a tenfold cross-validation on 15 sub-datasets derived from the original dataset, accounting for variations in plants and wastewater types. The results point to the normalization of the data followed by a conversion through the PCA to finally apply a Random Forest Regressor as the combination which stood out These results highlight the importance of modeling strategies in wastewater management using techniques such as LED spectrophotometry.

A simplified analytical solution of pipeline response under normal faulting

Buried pipelines are one of the critical lifeline structures, and the evaluation of pipeline performance under seismic faulting is essential for protection of pipeline networks. This paper explores an analytical solution incorporating implementation of Winkler models around a beam-type structure using the finite difference method to evaluate the pipeline response under normal faulting. The effectiveness of the analytical solution is assessed by comparing the calculated pipe strains with centrifuge and full-scale laboratory testing measurements. Subsequently, the effect of pipe size, faulting condition, and burial condition on pipe response are investigated. It can be found that a pipeline with a smaller pipe wall thickness or larger pipe diameter is generally more prone to failure. Larger pipe wall thickness or lower D/t ratio should be used for pipe design crossing an active fault. In geotechnically problematic areas, shallow burial depth and less stiff soil with lower modulus are recommended to reduce the probability of exceedance of allowable limit states due to normal faulting. In the end, empirical functions relating to critical D/t ratio are proposed to evaluate the critical states of fault displacement, burial depth, and spread of fault rupture with different diameter-to-thickness ratio considering the controlled limit of failure modes.

Impact of Seismic Geohazards on water supply systems and pipeline performance: Insights from the 2023 Kahramanmaras Earthquakes

This study delves into the profound repercussions of geohazards on water supply systems, specifically in the aftermath of the Kahramanmaras earthquakes. The influence of these geohazards was far-reaching, impacting a vast geographical expanse affected by the seismic events. The primary focus of this investigation centers on the provinces of Adiyaman, Gaziantep, and Hatay, providing representative damage examples from the earthquake-affected areas. The study illustrates various types of pipe failures induced by geohazards such as fault displacements, landslides, and liquefaction. The analysis encompasses diverse cases of damage, starting from the water resources, progressing through issues at transmission lines, and extending to challenges faced by pumping and treatment facilities. Key aspects of damages and geohazards are presented, shedding light on the intricate dynamics of these interactions. It is crucial to note the scarcity of real cases in the existing literature, emphasizing the need for extensive site investigations and dedicated research endeavors to construct a comprehensive database of case histories in this domain. This study addresses this gap, contributing valuable insights into the tangible impacts of geohazards on water supply systems. By comprehending and effectively addressing the risks associated with geohazards, water supply organizations can fortify the safety and resilience of their infrastructure. The findings presented herein offer a foundation for informed decision-making and strategic planning, fostering a proactive approach to mitigate potential damages and enhance the overall robustness of water supply systems in regions prone to seismic events and associated geohazards.

Radiomics signature for automatic hydronephrosis detection in unenhanced Low-Dose CT

PurposeTo investigate the diagnostic performance of an automatic pipeline for detection of hydronephrosis on kidney's parenchyma on unenhanced low-dose CT of the abdomen. MethodsThis retrospective study included 95 patients with confirmed unilateral hydronephrosis in an unenhanced low-dose CT of the abdomen. Data were split into training (n = 67) and test (n = 28) cohorts. Both kidneys for each case were included in further analyses, whereas the kidney without hydronephrosis was used as control. Using the training cohort, we developed a pipeline consisting of a deep-learning model for automatic segmentation (a Convolutional Neural Network based on nnU-Net architecture) of the kidney's parenchyma and a radiomics classifier to detect hydronephrosis. The models were assessed using standard classification metrics, such as area under the ROC curve (AUC), sensitivity and specificity, as well as semantic segmentation metrics, including Dice coefficient and Jaccard index. ResultsUsing manual segmentation of the kidney’s parenchyma, hydronephrosis can be detected with an AUC of 0.84, a sensitivity of 75% and a specificity of 82%, a PPV of 81% and a NPV of 77%. Automatic kidney segmentation achieved a mean Dice score of 0.87 and 0.91 for the right and left kidney, respectively. Additionally, automatic segmentation achieved an AUC of 0.83, a sensitivity of 86%, specificity of 64%, PPV of 71%, and NPV of 82%. ConclusionOur proposed radiomics signature using automatic kidney's parenchyma segmentation allows for accurate hydronephrosis detection on unenhanced low-dose CT scans of the abdomen independently of widened renal pelvis. This method could be used in clinical routine to highlight hydronephrosis to radiologists as well as clinicians, especially in patients with concurrent parapelvic cysts and might reduce time and costs associated with diagnosing hydronephrosis.

Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features From Surgical Pathology Reports

BackgroundWe aim to use Natural Language Processing to automate the extraction and classification of thyroid cancer risk factors from pathology reports. MethodsWe analyzed 1410 surgical pathology reports from adult papillary thyroid cancer patients from 2010 to 2019. Structured and nonstructured reports were used to create a consensus-based ground truth dictionary and categorized them into modified recurrence risk levels. Nonstructured reports were narrative, while structured reports followed standardized formats. We developed ThyroPath, a rule-based Natural Language Processing pipeline, to extract and classify thyroid cancer features into risk categories. Training involved 225 reports (150 structured, 75 unstructured), with testing on 170 reports (120 structured, 50 unstructured) for evaluation. The pipeline's performance was assessed using both strict and lenient criteria for accuracy, precision, recall, and F1-score; a metric that combines precision and recall evaluation. ResultsIn extraction tasks, ThyroPath achieved overall strict F-1 scores of 93% for structured reports and 90% for unstructured reports, covering 18 thyroid cancer pathology features. In classification tasks, ThyroPath-extracted information demonstrated an overall accuracy of 93% in categorizing reports based on their corresponding guideline-based risk of recurrence: 76.9% for high-risk, 86.8% for intermediate risk, and 100% for both low and very low-risk cases. However, ThyroPath achieved 100% accuracy across all risk categories with human extracted pathology information. ConclusionsThyroPath shows promise in automating the extraction and risk recurrence classification of thyroid pathology reports at large scale. It offers a solution to laborious manual reviews and advancing virtual registries. However, it requires further validation before implementation.

Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.

Adversarial training has attracted much attention in enhancing the visual realism of images, but its efficacy in clinical imaging has not yet been explored. This work investigated adversarial training in a clinical context, by training 206 networks on the OASIS-1 dataset for improving low-resolution and low signal-to-noise ratio (SNR) magnetic resonance images. Each network corresponded to a different combination of perceptual and adversarial loss weights and distinct learning rate values. For each perceptual loss weighting, we identified its corresponding adversarial loss weighting that minimized structural disparity. Each optimally weighted adversarial loss yielded an average SSIM reduction of 1.5%. We further introduced a set of new metrics to assess other clinically relevant image features: Gradient Error (GE) to measure structural disparities; Sharpness to compute edge clarity; and Edge-Contrast Error (ECE) to quantify any distortion of the pixel distribution around edges. Including adversarial loss increased structural enhancement in visual inspection, which correlated with statistically consistent GE reductions (p-value << 0.05). This also resulted in increased Sharpness; however, the level of statistical significance was dependent on the perceptual loss weighting. Additionally, adversarial loss yielded ECE reductions for smaller perceptual loss weightings, while showing non-significant increases (p-value >> 0.05) when these weightings were higher, demonstrating that the increased Sharpness does not adversely distort the pixel distribution around the edges in the image. These studies clearly suggest that adversarial training significantly improves the performance of an MRI enhancement pipeline, and highlights the need for systematic studies of hyperparameter optimization and investigation of alternative image quality metrics.

Correction: The performance of transmission pipelines on february 6th, 2023 Kahramanmaras earthquake: a series of case studies

Correction: The performance of transmission pipelines on february 6th, 2023 Kahramanmaras earthquake: a series of case studies

Benchmarking short and long read polishing tools for nanopore assemblies: achieving near-perfect genomes for outbreak isolates

BackgroundOxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks.ResultsWe tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct.ConclusionsShort reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers.