Reproducible Data Research Articles

Impact of copper on activity, selectivity, and deactivation in the photocatalytic reduction of CO2 over TiO2

The photocatalytic reduction of CO2 to hydrocarbons may be a promising mechanistic route to reduce greenhouse gas CO2, convert it into useful products, and limit the direct emission of CO2. The photocatalytic reduction of CO2 is a reaction in which photons activate the photocatalyst by generating reduced and oxidized sites, that are re-oxidized and re-reduced by the reactants. The photocatalytic reduction of CO2 can produce various products, including CO, formic acid, formaldehyde, methanol, methane, etc.Here, the activity/selectivity of produced hydrocarbons in the presence of oxygen on various photocatalysts (TiO2, 5 wt%Cu-TiO2, 5 wt%Cu-C-TiO2) was determined.The reaction rate increased with increasing reaction time up to the first half an hour of the reaction but then started to decrease with photocatalysts, indicating photocatalyst deactivation, a rate-limiting step by hydroxyl radicals and adsorption of intermediates on TiO2. Carbon deposition as the origin of photocatalyst deactivation was confirmed using the TGA of the spent photocatalyst. Additionally, absorption of intermediates on spent catalysts were confirmed by FTIR. On the other hand, the conversion increased with time when copper was used as a promoter on a TiO2 compared with TiO2 due to its larger surface area and having more active sites.The photocatalysts were characterized using BET, ICP-OES, XRD, TGA, XPS, Fourier-Transform Infrared Spectroscopy (FTIR), and UV–Vis. The focus of this study was to determine the activity and efficacy of different photocatalysts (TiO2, 5 wt%Cu-TiO2, 5 wt%Cu-C-TiO2) by gas phase measurement, with a particular emphasis on obtaining reproducible data on conversion and selectivity as a function of irradiation time.The copper on TiO2 was found to be more selective towards sodium formate/formic acid with a maximum selectivity of ∼90 % in 4 h and had higher activity (74.3 µmol gcat-1 h-1). A maximum CO with a selectivity of 86 % was found when TiO2 was used in 4 h.Copper transfer electrons on the TiO2 surface enhance CO2 adsorption on the catalytic surface and is the reason for having higher valuable product on Cu-C-TiO2 and Cu-TiO2 compared with TiO2. Carbon in this experiment did not have a role and its effect was negligible.

Packaging code and data for reproducible research: A case study of journey time statistics

The effective and ethical use of data to inform decision-making offers huge value to the public sector, especially when delivered by transparent, reproducible, and robust data processing workflows. One way that governments are unlocking this value is through making their data publicly available, allowing more people and organisations to derive insights. However, open data is not enough in many cases: publicly available datasets need to be accessible in an analysis-ready form from popular data science tools, such as R and Python, for them to realise their full potential. This paper explores ways to maximise the impact of open data with reference to a case study of packaging code to facilitate reproducible analysis. We present the jtstats project, which consists of a main Python package, and a smaller R version, for importing, processing, and visualising large and complex datasets representing journey times, for many transport modes and trip purposes at multiple geographic levels, released by the UK Department for Transport (DfT). jtstats shows how domain specific packages can enable reproducible research within the public sector and beyond, saving duplicated effort and reducing the risks of errors from repeated analyses. We hope that the jtstats project inspires others, particularly those in the public sector, to add value to their data sets by making them more accessible.

Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions

The study of the environmental transition of the aviation sector calls for prospective traffic scenarios. Detailed traffic and emissions inventories are often needed to refine the available analyses and to enable the simulation of regionalised scenarios. In the past studies, these are generally based on commercial, proprietary traffic data, making their dissemination problematic and reducing the reproducibility of the science produced. Open-source alternatives do exist but with limited geographical coverage. This study bridges this gap by presenting an innovative open-source dataset detailing 2019's global air traffic flows and associated CO2 emissions. A comprehensive approach that compiles diverse flight data sources is presented. The remaining data gaps are addressed by constructing a route network through systematic Wikipedia parsing and by estimating the related traffic using socio-economic data. Then, an aircraft performance model to estimate CO2 emissions is implemented. This methodology promises reinforced reproducibility and broader data accessibility in aviation environmental research. Several reference datasets are used to evaluate the accuracy of the open-source dataset. Despite various levels of accuracy for individual routes, major traffic flows are well estimated at the country and continental levels, albeit with room for refinement to ensure consistent data reliability. To facilitate the exploration of the dataset, the AeroSCOPE tool has been developed. To initiate research prospects, use cases of this dataset are proposed, concerning the network potential of electric and hydrogen-powered aircraft and inequalities in air transport.

Automated Scanning Dielectric Microscopy Toolbox for Operando Nanoscale Electrical Characterization of Electrolyte‐Gated Organic Transistors

AbstractElectrolyte‐gated organic transistors (EGOTs) leveraging organic semiconductors' electronic and ionic transport characteristics are the key enablers for many biosensing and bioelectronic applications that can selectively sense, record, and monitor different biological and biochemical processes at the nanoscale and translate them into macroscopic electrical signals. Understanding such transduction mechanisms requires multiscale characterization tools to comprehensively probe local electrical properties and link them with device behavior across various bias points. Here, an automated scanning dielectric microscopy toolbox is demonstrated that performs operando in‐liquid scanning dielectric microscopy measurements on functional EGOTs and carries out extensive data analysis to unravel the evolution of local electrical properties in minute detail. This paper emphasizes critical experimental considerations permitting standardized, accurate, and reproducible data acquisition. The developed approach is validated with EGOTs based on blends of organic small molecule semiconductor and insulating polymer that work as accumulation‐mode field‐effect transistors. Furthermore, the degradation of local electrical characteristics at high gate voltages is probed, which is apparently driven by the destruction of local crystalline order due to undesirable electrochemical swelling of the organic semiconducting material near the source electrode edge. The developed approach paves the way for systematic probing of EGOT‐based technologies for targeted optimization and fundamental understanding.

Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit.

With the ever-expanding toolkit of molecular viewers, the ability to visualize macromolecular structures has never been more accessible. Yet, the idiosyncratic technical intricacies across tools and the integration complexities associated with handling structure annotation data present significant barriers to seamless interoperability and steep learning curves for many users. The necessity for reproducible data visualizations is at the forefront of the current challenges. Recently, we introduced MolViewSpec (homepage: https://molstar.org/mol-view-spec/, GitHub project: https://github.com/molstar/mol-view-spec), a specification approach that defines molecular visualizations, decoupling them from the varying implementation details of different molecular viewers. Through the protocols presented herein, we demonstrate how to use MolViewSpec and its 3D view-building Python library for creating sophisticated, customized 3D views covering all standard molecular visualizations. MolViewSpec supports representations like cartoon and ball-and-stick with coloring, labeling, and applying complex transformations such as superposition to any macromolecular structure file in mmCIF, BinaryCIF, and PDB formats. These examples showcase progress towards reusability and interoperability of molecular 3D visualization in an era when handling molecular structures at scale is a timely and pressing matter in structural bioinformatics as well as research and education across the life sciences. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating a MolViewSpec view using the MolViewSpec Python package Basic Protocol 2: Creating a MolViewSpec view with reference to MolViewSpec annotation files Basic Protocol 3: Creating a MolViewSpec view with labels and other advanced features Support Protocol 1: Computing rotation and translation vectors Support Protocol 2: Creating a MolViewSpec annotation file.

IMG-29. IMPLICATIONS OF IMAGE HARMONIZATION FOR BRAIN TUMOR CLINICAL TRIALS

Abstract BACKGROUND Clinical trials for rare pediatric conditions like optic pathway gliomas associated with NF1 (NF1-OPGs) require numerous sites that utilize different MRI scanners and protocols. These MRIs must be harmonized for reliable comparison using machine learning tools. We propose a deep-learning based MRI harmonization that enables reproducible data analysis across multiple sites and MRI platforms. METHODS One-hundred eighty clinical T1-weighted-MRIs of pediatric NF1-OPG subjects were acquired from three sites [Children’s National Hospital (site A, N=60), Children’s Hospital Colorado (site B, N=60), and Children’s Hospital of Philadelphia (site C, N=60)] using different MR scanners (GE, Phillips, and Siemens, respectively). MRIs from A and B were used to train a branched neural network and C were used for testing. Using unsupervised learning, the neural network identified differences in imaging protocols and harmonized data to a chosen protocol, e.g., that of site A, while preserving the patient anatomy. The harmonization was evaluated using Wasserstein distance (nWD), which measured similarity between two gray scale appearances of MRI intensity histograms. Anatomy preservation metric (AP) measured relative volume changes in gray matter (GM) segmented using Freesurfer, before and after harmonization. For both metrics, 100% means the best possible harmonization was achieved. RESULTS Before harmonization, nWD between grayscale appearance of MRIs from sites A and B was 27.3±3.1%. After harmonization, the similarity became 94.2±2.0% with a GM volume error of 3.11±0.56ml which corresponded to AP of 93.1±4.7%. For the test data, nWD similarity post harmonization improved from 38.2±3.9% to 93.4±2.2% with GM volume error of 4.12±0.47ml which corresponded to AP of 90.3±3.5%. CONCLUSIONS Our approach enables MRI harmonization while preserving anatomy for precise analysis using machine learning tools. It scales to new sites to support multisite clinical trials that utilize imaging outcomes for brain tumors and other rare diseases.

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging.

The body size and composition assessment is commonly included in the routine management of healthy athletes as well as of different types of patients to personalize the training or rehabilitation strategy. The digital anthropometric analyses described in the following protocol can be performed with recently introduced systems. These new tools and approaches have the potential to be widely used in clinical settings because they are very simple to operate and enable the rapid collection of accurate and reproducible data. One system consists of a rotating platform with a weight measurement plate, three infrared cameras, and a tablet built into a tower, while the other system consists of a tablet mounted on a holder. After image capture, the software of both systems generates a de-identified three-dimensional humanoid avatar with associated anthropometric and body composition variables. The measurement procedures are simple: a subject can be tested in a few minutes and a comprehensive report (including the three-dimensional scan and body size, shape, and composition measurements) is automatically generated.

Intra‐ and interlaboratory reproducibility evaluation toward international validation status of the AmpFire assay

AbstractTo meet the screening goal of WHO's 90‐70‐90 strategy aimed at eliminating cervical cancer (CC) by 2030, clinical validation of human papillomavirus (HPV) assays is essential to provide accurate and valid results through fulfilling three criteria of the international validation guidelines (IVGs). Previously, the clinical accuracy of the AmpFire® HPV Screening 16/18/HR assay (AmpFire assay) was reported but reproducibility data are lacking. Here, we aim to evaluate the intra‐ and inter‐laboratory reproducibility of the AmpFire assay. The reproducibility of the isothermal AmpFire assay was assessed using 556 cervical cell samples collected from women attending CC screening and biobanked in a Belgian HPV national reference center. This assay detects HPV16, HPV18, and 12 other high‐risk HPV (hrHPV) types (31/33/35/39/45/51/52/56/58/59/66/68) in aggregate. Lower 95% confidence interval bound around the assay's reproducibility should exceed 87%, with κ ≥ 0.50. Additionally, a literature review of the assay's clinical performance was performed. The AmpFire assay showed an excellent intralaboratory (96.4%, 95% CI:94.5–97.8%, κ = 0.920) and interlaboratory (95.3%, 95% CI:93.2–96.9%, κ = 0.897) reproducibility. One study demonstrated noninferior sensitivity of a prototype AmpFire assay targeting 15 hrHPV types (including HPV53) to detect CIN2+. However, clinical specificity became similar to the comparator after removing HPV53 from analyses. The low‐cost and easy‐to‐use AmpFire assay presents excellent reproducibility and—after removing HPV53 from the targeted types—fulfills also clinical accuracy requirements. Inclusion of HPV53, which is not recognized as carcinogenic, comprises clinical specificity of screening assays.

Reproducible and high sample throughput isomiR next-generation sequencing for cancer diagnosis.

e15013 Background: Next-generation sequencing (NGS) can produce up to 6 Tb of data per run with single-nucleotide accuracy, making it ideal for quantifying isomiRs, which encompass both canonical miRNAs and their variants, for clinical applications. However, NGS has poor reproducibility and low sample throughput in quantifying circulating isomiRs due to significant technical variations and the limitations of the multiplex strategy, as evidenced by the fact that no isomiR NGS technique has been successfully used to diagnose cancer. Methods: To address these challenges, a library construction method including a dual unique-dual-index (DUDI) technology was developed. DUDI uses a pair of Inner UDI (IUDI) and outer UDI (OUDI) to label a sample. Twelve independent batches of isomiR NGS were carried out, including three repeated batches. Each batch included 100 gastric cancer and 100 control plasma samples. Batch effect, correlation coefficient (R), and principal component (PCA) analyses were used to evaluate technical reproducibility. Machine learning binary classification was used to assess biological reproducibility, with each pair of batch data serving interchangeably as both training and testing data. Results: In this multicenter study, over 700G of isomiR data were generated from 402 gastric cancer and 498 control samples, with a maximum error rate of 1 in 7 million isomiRs being assigned to wrong samples. The PCA plot indicates high technical reproducibility across the three repeated batches, shown by the extensive intermingling of data points from each batch and the lack of distinct batch-wise clustering. This observation is reinforced by that the R value for each of 239 isomiRs between the repeated batches are close to 1. While the mutual machine learning validations between the repeated batches yielded ~95% accuracy, indicating high biological reproducibility. The accuracies of the validations between the different batches of different samples range from 70% to 82%. The lower accuracy is as expected, given the high genetic heterogeneity of cancer and the small sample size. Furthermore, the IsomiR differentiated expression profiles from the current NGS study closely match those from prior qPCR studies. Conclusions: The DUDI library construction method can produce reproducible high sample throughput NGS data, yet it is cost-effective and straightforward. The maximum number of samples that can be multiplexed in an NGS project is almost one million, i.e., 976 * 976, as IUDI and OUDI can be any of the 976 designed DUDIs. This number far exceeds the high sample throughput requirements of any NGS application. While the capability to distinguish true biological variations of IsomiRs from technical noise, demonstrated by the high technical and biological reproducibility and concordance with the qPCR data, enables the development of robust machine learning algorithms for cancer diagnostics.

An Automated Robotic Interface for Assays: Facilitating Machine Learning in Drug Discovery by the Automation of Physicochemical Property Assays.

Measuring the physicochemical properties of molecules is an iterative but integral process in the drug development process. A strategy to overcome the challenges in maximizing assay throughput relies on the usage of in silico machine learning (ML) prediction models trained on experimental data. Consequently, the performance of these in silico models are dependent on the quality of the utilized experimental data. To improve the data quality, we have designed and implemented an automated robotic system to prepare and run physicochemical property assays (Automated Robotic Interface for Assays, ARIA) with an increase in sample throughput of 6 to10-fold. Through this process, we overcame major challenges and achieved consistent reproducible assay data compared to semiautomated assay preparation.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Analysis of measurable residual disease by IG/TR gene rearrangements: quality assurance and updated EuroMRD guidelines.

Minimal/measurable residual disease (MRD) diagnostics using real-time quantitative PCR analysis of rearranged immunoglobulin and T-cell receptor gene rearrangements are nowadays implemented in most treatment protocols for patients with acute lymphoblastic leukemia (ALL). Within the EuroMRD Consortium, we aim to provide comparable, high-quality MRD diagnostics, allowing appropriate risk-group classification for patients and inter-protocol comparisons. To this end, we set up a quality assessment scheme, that was gradually optimized and updated over the last 20 years, and that now includes participants from around 70 laboratories worldwide. We here describe the design and analysis of our quality assessment scheme. In addition, we here report revised data interpretation guidelines, based on our newly generated data and extensive discussions between experts. The main novelty is the partial re-definition of the "positive below quantitative range" category by two new categories, "MRD low positive, below quantitative range" and "MRD of uncertain significance". The quality assessment program and revised guidelines will ensure reproducible and accurate MRD data for ALL patients. Within the Consortium, similar programs and guidelines have been introduced for other lymphoid diseases (e.g., B-cell lymphoma), for new technological platforms (e.g., digital droplet PCR or Next-Generation Sequencing), and for other patient-specific MRD PCR-based targets (e.g., fusion genes).

Application of handheld/portable spectroscopic tools to the identification, inner stratigraphy and mapping of archaeological metal artefacts

Field handheld/portable instrumentations, such as in-situ geochemical analyzers, have the potential to assist efficiently targeted geochemical archaeometry campaigns in detecting and quantifying specific elements. Non-destructive portable energy dispersive x-ray fluorescence and micro-destructive handheld laser-induced breakdown spectroscopy (LIBS) instrumentation were utilized to investigate the elemental composition, internal stratigraphy by depth profiling and microscale compositional mapping of five copper and two iron alloy artefacts collected from various ancient graves in the Minervino Murge area, Apulia, Italy. The primary elements identified by both techniques included Cu, Sn and Pb in copper alloys, and Fe with minor amounts of Cu and Pb in iron alloys. Furthermore, the elements Al, Ca, Si, Mg, Na and K, mostly originated from soil contamination, and the trace elements Sb, Ni and Zn were detected. The satisfactory performance of both techniques was assessed by their capacity to provide reproducible elemental composition data. Finally, the depth profile and mapping achieved by LIBS contributed to understanding the metal processing and history of the objects studied, so confirming both techniques to be robust analytical tools in outdoor archaeology and archaeometry campaigns.

Fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation (FLASH): Study protocol for a randomized controlled noninferiority trial

BackgroundArtificial intelligence-based quantitative coronary angiography (AI-QCA) has been developed to provide a more objective and reproducible data about the severity of coronary artery stenosis and the dimensions of the vessel for intervention in real-time, overcoming the limitations of significant inter- and intraobserver variability, and time-consuming nature of on-site QCA, without requiring extra time and effort. Compared with the subjective nature of visually estimated conventional CAG guidance, AI-QCA guidance provides a more practical and standardized angiography-based approach. Although the advantage of intravascular imaging-guided PCI is increasingly recognized, their broader adoption is limited by clinical and economic barriers in many catheterization laboratories. MethodsThe FLASH (fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation) trial is a randomized, investigator-initiated, multicenter, open-label, noninferiority trial comparing the AI-QCA-assisted PCI strategy with optical coherence tomography-guided PCI strategy in patients with significant coronary artery disease. All operators will utilize a novel, standardized AI-QCA software and PCI protocol in the AI-QCA-assisted group. A total of 400 patients will be randomized to either group at a 1:1 ratio. The primary endpoint is the minimal stent area (mm2), determined by the final OCT run after completion of PCI. Clinical follow-up and cost-effectiveness evaluations are planned at 1 month and 6 months for all patients enrolled in the study. ResultsEnrollment of a total of 400 patients from the 13 participating centers in South Korea will be completed in February 2024. Follow-up of the last enrolled patients will be completed in August 2024, and primary results will be available by late 2024. ConclusionThe FLASH is the first clinical trial to evaluate the feasibility of AI-QCA-assisted PCI, and will provide the clinical evidence on AI-QCA assistance in the field of coronary intervention. Clinical trial registrationURL: https://www.clinicaltrials.gov. Unique identifier: NCT05388357.

Quantification and isotopic characterization of benzene polycarboxylic acids (BPCA)-derived black carbon in deep oceanic sediments: Towards assessing pyrogenic inputs from marine sources

Methodologies based on benzene polycarboxylic acids (BPCA) selectively target the polymeric aromatic fraction of black carbon (BC) and are considered adequate to quantify pyrogenic inputs in environmental samples such as soils, lakes, and marine dissolved organic carbon. However, the usefulness of these methodologies to quantify BPCA-derived BC in deep-sea sediments has not been fully evaluated. In this manuscript we describe and validate a procedure to quantify BPCAs in deep oceanic sediments with very low organic carbon content. The resulting analytical procedure has produced reproducible quantitative data for BPCAs over a period of 10 months (coefficient of variation, CV = 6.4 − 6.6%). The stable carbon isotopes (δ13C) of BC_BPCA have been characterized using an LC Isolink™-irMS system with an accuracy better than 0.5‰. The quantitative and isotopic composition of several marine sediments has been characterized to investigate the relative contributions of marine/diagenetic and continental/pyrogenic sources to the BC accumulated in oceanic sediments from different contexts, ranging from upwelling systems to remote oceanic locations. Overall, a significant fraction of the sedimentary BC is of marine origin and should be considered in inventories of pyrogenic materials accumulated in the world oceans. However, the continental/pyrogenic sources can be largely dominant in marine settings with large inputs of pyrogenic materials.

Low-cost automated flat-sheet membrane casting: An open-source, advanced manufacturing approach

Novel membrane materials developed in research labs struggle to gain widespread industrial adoption due in part to insufficient reproducibility and unreliable performance data. Open-source hardware approaches, especially 3D printing, enable the democratization of automation within a laboratory setting, and in the context of membranes, can minimize the inherent variability associated with manual methods of membrane casting. In this study, the native hardware and firmware of an inexpensive, conventional 3D printer was extensively modified for the purpose of flat-sheet membrane casting. Replicate poly (ether-ether ketone) (PEEK) membranes were cast with a thickness coefficient of variation of ∼10 % using the modified device. Cast membranes were used to assess the importance of controlling shear rate by characterizing both intra- and inter-film variability. Statistical differences in pure water permeability were observed across tested shear rates, with distinct morphological changes occurring to the membrane substructure. Overall, the technology developed in this study is shown to be an extremely useful approach for improving the process of developing membranes at the bench-scale.

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

On the value of meta-research for early career researchers: A commentary.

Meta-research, also known as "research on research" is a field of study that investigates the methods, reporting, reproducibility, evaluation, and incentives along the research continuum. Meta-research literacy is imperative to ensure high quality, transparent and reproducible primary data or meta-research products. In this commentary, we propose that early career researchers should be trained in meta-research as a foundation to develop a deeper understanding of the research process and ability to appraise the research literature and design high-quality original studies, irrespective of their chosen field of study. We discuss the importance of meta-research and open science from the perspective of an early career trainee, highlighting essential areas for growth and obstacles one may encounter.

ColiSeq: a multiplex amplicon assay that provides strain level resolution of Escherichia coli directly from clinical specimens.

Escherichia coli is a diverse pathogen, causing a range of disease in humans, from self-limiting diarrhea to urinary tract infections (UTIs). Uropathogenic E. coli (UPEC) is the most frequently observed uropathogen in UTIs, a common disease in high-income countries, incurring billions of dollars yearly in treatment costs. Although E. coli is easily grown and identified in the clinical laboratory, genotyping the pathogen is more complicated, yet critical for reducing the incidence of disease. These goals can be achieved through whole-genome sequencing of E. coli isolates, but this approach is relatively slow and typically requires culturing the pathogen in the laboratory. To genotype E. coli rapidly and inexpensively directly from clinical samples, including but not limited to urine, we developed and validated a multiplex amplicon sequencing assay, called ColiSeq. The assay consists of targets designed for E. coli species confirmation, high resolution genotyping, and mixture deconvolution. To demonstrate its utility, we screened the ColiSeq assay against 230 clinical urine samples collected from a hospital system in Flagstaff, Arizona, USA. A limit of detection analysis demonstrated the ability of ColiSeq to identify E. coli at a concentration of ~2 genomic equivalent (GEs)/mL and to generate high-resolution genotyping at a concentration of 1 × 105 GEs/mL. The results of this study suggest that ColiSeq could be a valuable method to understand the source of UPEC strains and guide infection mitigation efforts. As sequence-based diagnostics become accepted in the clinical laboratory, workflows such as ColiSeq will provide actionable information to improve patient outcomes.IMPORTANCEUrinary tract infections (UTIs), caused primarily by Escherichia coli, create an enormous health care burden in the United States and other high-income countries. The early detection of E. coli from clinical samples, including urine, is important to target therapy and prevent further patient complications. Additionally, understanding the source of E. coli exposure will help with future mitigation efforts. In this study, we developed, tested, and validated an amplicon sequencing assay focused on direct detection of E. coli from urine. The resulting sequence data were demonstrated to provide strain level resolution of the pathogen, not only confirming the presence of E. coli, which can focus treatment efforts, but also providing data needed for source attribution and contact tracing. This assay will generate inexpensive, rapid, and reproducible data that can be deployed by public health agencies to track, diagnose, and potentially mitigate future UTIs caused by E. coli.

Correlating transcription and protein expression profiles of immune biomarkers following lipopolysaccharide exposure in lung epithelial cells.

Universal and early recognition of pathogens occurs through recognition of evolutionarily conserved pathogen associated molecular patterns (PAMPs) by innate immune receptors and the consequent secretion of cytokines and chemokines. The intrinsic complexity of innate immune signaling and associated signal transduction challenges our ability to obtain physiologically relevant, reproducible and accurate data from experimental systems. One of the reasons for the discrepancy in observed data is the choice of measurement strategy. Immune signaling is regulated by the interplay between pathogen-derived molecules with host cells resulting in cellular expression changes. However, these cellular processes are often studied by the independent assessment of either the transcriptome or the proteome. Correlation between transcription and protein analysis is lacking in a variety of studies. In order to methodically evaluate the correlation between transcription and protein expression profiles associated with innate immune signaling, we measured cytokine and chemokine levels following exposure of human cells to the PAMP lipopolysaccharide (LPS) from the Gram-negative pathogen Pseudomonas aeruginosa. Expression of 84 messenger RNA (mRNA) transcripts and 69 proteins, including 35 overlapping targets, were measured in human lung epithelial cells. We evaluated 50 biological replicates to determine reproducibility of outcomes. Following pairwise normalization, 16 mRNA transcripts and 6 proteins were significantly upregulated following LPS exposure, while only five (CCL2, CSF3, CXCL5, CXCL8/IL8, and IL6) were upregulated in both transcriptomic and proteomic analysis. This lack of correlation between transcription and protein expression data may contribute to the discrepancy in the immune profiles reported in various studies. The use of multiomic assessments to achieve a systems-level understanding of immune signaling processes can result in the identification of host biomarker profiles for a variety of infectious diseases and facilitate countermeasure design and development.