Data Curation Research Articles

Abstract 4117883: Long noncoding RNAs and machine learning to improve cardiovascular outcomes of COVID-19

Introduction/Background: Cardiovascular symptoms appear in a high proportion of patients in the few months following a severe SARS-CoV-2 infection. Non-invasive methods to predict disease severity could help personalizing healthcare and reducing the occurrence of these symptoms. Research Questions/Hypothesis: We hypothesized that blood long noncoding RNAs (lncRNAs) and machine learning (ML) could help predict COVID-19 severity. Goals/Aims: To develop a model based on lncRNAs and ML for predicting COVID-19 severity. Methods/Approach: Expression data of 2906 lncRNAs were obtained by targeted sequencing in plasma samples collected at baseline from four independent cohorts, totaling 564 COVID-19 patients. Patients were aged 18+ and were recruited from 2020 to 2023 in the PrediCOVID cohort (n=162; Luxembourg), the COVID19_OMICS-COVIRNA cohort (n=100, Italy), the TOCOVID cohort (n=233, Spain), and the MiRCOVID cohort (n=69, Germany). The study complied with the Declaration of Helsinki. Cohorts were approved by ethics committees and patients signed an informed consent. Results/Data: After data curation and pre-processing, 463 complete datasets were included in further analysis, representing 101 severe patients (in-hospital death or ICU admission) and 362 stable patients (no hospital admission or hospital admission but not ICU). Feature selection with Boruta, a random forest-based method, identified age and five lncRNAs (LINC01088-201, FGDP-AS1, LINC01088-209, AKAP13, and a novel lncRNA) associated with disease severity, which were used to build predictive models using six ML algorithms. A naïve Bayes model based on age and five lncRNAs predicted disease severity with an AUC of 0.875 [0.868-0.881] and an accuracy of 0.783 [0.775-0.791]. Conclusion: We developed a ML model including age and five lncRNAs predicting COVID-19 severity. This model could help improve patients’ management and cardiovascular outcomes.

Slow and diverse: Upslope expansion of tree species since 1854 in the Bavarian Alps (Germany) detected by citizen science

ABSTRACT We motivated mountain hikers to survey high-altitude occurrences of selected tree and shrub species in the Bavarian Alps in a citizen science portal and compared the observations with historical data collected by the botanist Otto Sendtner at the end of the Little Ice Age. For nine of ten species with sufficient numbers of historical and citizen science observations, currently uppermost outpost occurrences were verified after data curation and subsampling. Tree species of montane (Sorbus aucuparia: 342 m, Abies alba: 324 m, Acer pseudoplatanus: 273 m, Fagus sylvatica: 141 m) and of lower subalpine distribution (Betula pubescens: 278 m, Picea abies: 55 m) climbed significantly, whereas species found at the high subalpine tree line ecotone ascended less (Pinus mugo: 21 m, Larix decidua: 13 m, Alnus alnobetula: 6 m) or even appeared to lose elevation (Pinus cembra: −57 m). We explain the gap between realized and potential upslope shift of ca. 500 m suggested by the thermal elevation gradient by the lack of mature soils at high altitudes and low summit heights in the Bavarian Alps.

T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution Framework

Entity resolution (ER) is the process of determining whether two representations refer to the same real-world entity and plays a crucial role in data curation and data cleaning. Recent studies have introduced the KAER framework, aiming to improve pre-trained language models by augmenting external knowledge. However, identifying and documenting the external knowledge that is being augmented and understanding its contribution to the model's predictions have received little to no attention in the research community. This paper addresses this gap by introducing T-KAER, the Transparency-enhanced Knowledge-Augmented Entity Resolution framework. To enhance transparency, three Transparency-related Questions (T-Qs) have been proposed: T-Q(1): What is the experimental process for matching results based on data inputs? T-Q(2): Which semantic information does KAER augment in the raw data inputs? T-Q(3): Which semantic information of the augmented data inputs influences the predictions? To address the T-Qs, T-KAER is designed to improve transparency by documenting the entity resolution processes in log files. In experiments, a citation dataset is used to demonstrate the transparency components of T-KAER. This demonstration showcases how T-KAER facilitates error analysis from both quantitative and qualitative perspectives, providing evidence on `"what" semantic information is augmented and ``"why" the augmented knowledge influences predictions differently.

Assessing the role of circular economy and green innovation in mitigating carbon emissions in the Visegrad countries

The shift towards a circular economy is an essential measure in achieving sustainable development because it seeks to separate economic expansion from resource use and environmental deterioration. To meet the European Union green deal, waste management, and the net zero emissions targets various countries are developing and adopting prudent strategies. This study investigates the dynamic affiliation between circular economy (CIR), green innovation (INV), renewable energy (REE), economic progress (GDP), and urbanisation (URB) on carbon emissions (CO2) in the Visegrad (V4) countries, comprising the Czech Republic, Hungary, Poland, and Slovakia. Using the CS-ARDL technique and quantile regression, data curation from 1990-2022 was analysed after checking for cross-sectional, unit root, and cointegration. The outcome demonstrates that circular economy, green innovation, and renewable energy had a negative effect on carbon emissions. In addition, GDP and URB had an immaterially positive influence on carbon emissions. Lastly, the quantile regression confirmed that the study provides useful information for policymakers and stakeholders in the Visegrad countries. It emphasised how important it is to take a broad approach to circular economy initiatives, support eco-friendly innovations, carry out renewable energy projects, and manage the urbanisation process well to achieve long-term economic growth and environmental health.

Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study.

It is projected that drug-resistant infections will lead to 10 million deaths annually by 2050 if left unabated. Despite this threat, surveillance data from resource-limited settings are scarce and often lack antimicrobial resistance (AMR)-related clinical outcomes and economic burden. We aim to build an AMR and antimicrobial use (AMU) data warehouse, describe the trends of resistance and antibiotic use, determine the economic burden of AMR in Uganda, and develop a machine learning algorithm to predict AMR-related clinical outcomes. The overall objective of the study is to use data-driven approaches to optimize antibiotic use and combat antimicrobial-resistant infections in Uganda. We aim to (1) build a dynamic AMR and antimicrobial use and consumption (AMUC) data warehouse to support research in AMR and AMUC to inform AMR-related interventions and public health policy, (2) evaluate the trends in AMR and antibiotic use based on annual antibiotic and point prevalence survey data collected at 9 regional referral hospitals over a 5-year period, (3) develop a machine learning model to predict the clinical outcomes of patients with bacterial infectious syndromes due to drug-resistant pathogens, and (4) estimate the annual economic burden of AMR in Uganda using the cost-of-illness approach. We will conduct a study involving data curation, machine learning-based modeling, and cost-of-illness analysis using AMR and AMU data abstracted from procurement, human resources, and clinical records of patients with bacterial infectious syndromes at 9 regional referral hospitals in Uganda collected between 2018 and 2026. We will use data curation procedures, FLAIR (Findable, Linkable, Accessible, Interactable and Repeatable) principles, and role-based access control to build a robust and dynamic AMR and AMU data warehouse. We will also apply machine learning algorithms to model AMR-related clinical outcomes, advanced statistical analysis to study AMR and AMU trends, and cost-of-illness analysis to determine the AMR-related economic burden. The study received funding from the Wellcome Trust through the Centers for Antimicrobial Optimisation Network (CAMO-Net) in April 2023. As of October 28, 2024, we completed data warehouse development, which is now under testing; completed data curation of the historical Fleming Fund surveillance data (2020-2023); and collected retrospective AMR records for 599 patients that contained clinical outcomes and cost-of-illness economic burden data across 9 surveillance sites for objectives 3 and 4, respectively. The data warehouse will promote access to rich and interlinked AMR and AMU data sets to answer AMR program and research questions using a wide evidence base. The AMR-related clinical outcomes model and cost data will facilitate improvement in the clinical management of AMR patients and guide resource allocation to support AMR surveillance and interventions. PRR1-10.2196/58116.

Unveiling the interspecies correlation and sensitivity factor analysis of rat and mouse acute oral toxicity of antimicrobial agents: first QSTR and QTTR Modeling report

Abstract This study aims to identify toxic potential and environmental hazardousness of antimicrobials. In this regard, the available experimental toxicity data with rat and mouse acute oral toxicity have been gathered from ChemID Plus database (n = 202) and subjected to data curation. Upon the data curation 51 and 68 compounds were left for the rat and mouse respectively for the modeling. The quantitative structure toxicity relationship (QSTR) and interspecies correlation analysis by quantitative toxicity-toxicity relationship (QTTR) modeling was approached in this study. The models were developed from 2D descriptors under OECD guidelines by using multiple linear regressions (MLR) with genetic algorithm (GA) for feature selection as a chemometric tool. The developed models were robust (Q2LOO = 0.600–0.679) and predictive enough (Q2Fn = 0.626–0.958, CCCExt = 0.840–0.893). The leverage approach of applicability domain (ad) analysis assures the model’s reliability. The antimicrobials without experimental toxicity values were classified as high, moderate and low toxic based on prediction and ad. The occurrence of the same classification from QSTR and QTTR models revealed the reliability of QTTR models.Finally, the applied “sensitivity factor analysis” typifies the sensitivity of chemicals toward each species. Overall, the first report will be helpful in the toxicity assessment of upcoming antimicrobials in rodents.

Resolving Conflicts in Data Through Curation-informed Weight Distribution Networks

Missing and conflicting data values create problems when integrating datasets from multiple collections. Moreover, when the collections to be integrated are large and continuously updated, it is not feasible to manually resolve these problems. Instead, disagreements and gaps should be resolved in an automated fashion. To achieve good quality integrated datasets automatically we introduce the Curation-informed Weight Distribution Network (CiWDN), a method that suggests which collection is more reliable in providing a data value in question. CiWDN adapts the PageRank algorithm (PR) to assign and distribute weights across data fields present in the different collections. Weight assignment is rooted in data curation best practices as metrics of a collection's reliability. The metrics include: a) data completeness, b) data coincidence, and c) data consistency over time. Final weights used as collection ranks provide the basis to resolve conflicts between different collections contributing a data value for a given field. CiWDN relies on a data dictionary that normalizes fields across collections, and is implemented on a graph database. We demonstrate CiWDN’s capability using the case of ASTRIAGraph, a knowledge system built to increase transparency of activities in Earth’s orbital environment. CiWDN can assess the reliability of data collections that conflict on space object characteristic data fields, which can be used to resolve the differences. This method for computing collections' reliability can be ported to curate other types of large integrated datasets for use in machine learning and other data-driven applications.

The Emergence of New Disciplines and the Transformation of the Library and Information Science Profession

Abstract: Advances in information and communication technologies, digitalization, and the internet have changed the way people process, use, and share information. The transformation of the library profession and the emergence of new disciplines such as information science, knowledge management, and data science have continued to raise questions about professional identity, workplace, interdisciplinarity, and the types of skills and competencies needed for information professionals to compete in a highly technical and knowledge intensive environment. In this paper, we reflect on the historical development in the field and examine emerging trends and their impact on the transformation of the information and knowledge professions. Data analysis of course offerings from top-ranked information science schools (iSchools) has shown strong emphasis on the use of terms such as “information” and “information science.” The use of the term “data” has significantly increased in the past few years due to the addition of new specialization and degree programs in data curation and data science. The decrease in the use of the term “library,” even though the library is still the largest employer of information science students at all levels, could be attributed to the continued trend and desire by academics and practitioners to separate the profession from the place of work.

Pre-trained models for linking process in data washing machine

Entity Resolution (ER) has been investigated for decades in various domains as a fundamental task in data integration and data quality. The emerging volume of heterogeneously structured data and even unstructured data challenges traditional ER methods. This research mainly focuses on the Data Washing Machine (DWM). The DWM was developed in the NSF DART Data Life Cycle and Curation research theme, which helps to detect and correct certain types of data quality errors automatically. It also performs unsupervised entity resolution to identify duplicate records. However, it uses traditional methods that are driven by algorithmic pattern rules such as Levenshtein Edit Distances and Matrix comparators. The goal of this research is to assess the replacement of rule-based methods with machine learning and deep learning methods to improve the effectiveness of the processes using 18 sample datasets. The DWM has different processes to improve data quality, and we are currently focusing on working with the scoring and linking processes. To integrate the machine model into the DWM, different pre-trained models were tested to find the one that helps to produce accurate vectors that can be used to calculate the similarity between the records. After trying different pre-trained models, distilroberta was chosen to get the embeddings, and cosine similarity metrics were later used to get the similarity scores, which helped us assess the machine learning model into DWM and gave us closer results to what the scoring matrix is giving. The model performed well and gave closer results overall, and the reason can be that it helped to pick up the important features and helped at the entity matching process.

A Site-Wise Reliability Analysis of the ABCD Diffusion Fractional Anisotropy and Cortical Thickness: Impact of Scanner Platforms.

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61 ± 0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76 ± 0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, p < 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, p < 0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

Measuring the global scientific literature on big data analytics in the LIS: the blending approach of altmetrics and bibliometrics

Purpose This study aims to measure scientific literature on the emerging research area of “big data” in the field of “library and information science” (LIS). Design/methodology/approach This study used the “bibliometric method” for data curation. Web of Science and altmetric.com were used. Data analysis and visualisation were done using three widely used powerful data analytics software, R-bibliometrix, VOSviewer and Statistical Package for Social Sciences. Findings This study revealed the most preferred venues for publication. Furthermore, this study highlighted an association between the Mendeley readers of publications and citations. Furthermore, it was evident that the overall altimetric attention score (AAS) does not influence the citation score of publications. Other fascinating findings were moderate collaboration patterns overall. Furthermore, the study highlighted that big data (BD) research output and scientific influence in the LIS sector are continually increasing. Practical implications Findings related to BD analytics in LIS techniques can serve as helpful information for researchers, practitioners and policymakers. Originality/value This study contributes to the current knowledge accumulation by its unique manner of blending the two approaches, bibliometrics and altmetrics.

Diversity and structural-functional insights of alpha-solenoid proteins.

Alpha-solenoids are a significant and diverse subset of structured tandem repeat proteins (STRPs) that are important in various domains of life. This review examines their structural and functional diversity and highlights their role in critical cellular processes such as signaling, apoptosis, and transcriptional regulation. Alpha-solenoids can be classified into three geometric folds: low curvature, high curvature, and corkscrew, as well as eight subfolds: ankyrin repeats; Huntingtin, elongation factor 3, protein phosphatase 2A, and target of rapamycin; armadillo repeats; tetratricopeptide repeats; pentatricopeptide repeats; Pumilio repeats; transcription activator-like; and Sel-1 and Sel-1-like repeats. These subfolds represent distinct protein families with unique structural properties and functions, highlighting the versatility of alpha-solenoids. The review also discusses their association with disease, highlighting their potential as therapeutic targets and their role in protein design. Advances in state-of-the-art structure prediction methods provide new opportunities and challenges in the functional characterization and classification of this kind of fold, emphasizing the need for continued development of methods for their identification and proper data curation and deposition in the main databases.

Advanced Practice Nurse-Led Research: Challenges and Approaches to Digital Health Programs' Evaluation Using Big Data.

Advanced practice nurse leaders are in key positions within health systems to provide time and resources for implementation and evaluation of digital health services. As virtual monitoring programs become more embedded within nursing, nurse leaders and educators need to ensure that nurses are prepared to work within interprofessional teams to administer and evaluate them. This article discusses challenges and implementation strategy considerations for data curation and analysis using large datasets from the Medicaid population for research.

Mindat.org – the open access mineralogy database to accelerate data-intensive geoscience research

Abstract The mindat.org website (Mindat) has been operating since October 2000 as a free, crowd-sourced, and expert-curated database particularly focused on mineral species and their occurrences worldwide. The project has transformed from a hobbyist site in the beginning into a resource that has found use in various scientific research projects and educational programs. Together with other open data resources, Mindat has helped accelerate scientific discoveries in many fields, such as mineral evolution, mineral ecology, and the co-evolution of the geosphere and biosphere. Recently, through open data efforts, machine interfaces and software packages have been established to enable flexible data discovery and download from Mindat. We assume the data access and usage will further scale up in the next years. Although Mindat is curated by a team of geoscience and database experts across the world, the crowd-sourced records in Mindat still pose some bias. In this paper we first present an overview of the primary data subjects in Mindat, and then we give extensive details about the characteristics and bias of three most popular data subjects: locality, mineral species, and mineral occurrence. In the discussion we also give an outlook on appropriate data usage and future extension of data records. We hope users can obtain a more comprehensive view of the Mindat database through this paper and thus better plan their data use. We also hope more people will be inspired to contribute to the data curation work to make Mindat a sustained data ecosystem for geoscience research.

Explainable AI for automated respiratory misalignment detection in PET/CT imaging

Purpose.Positron emission tomography (PET) image quality can be affected by artifacts emanating from PET, computed tomography (CT), or artifacts due to misalignment between PET and CT images. Automated detection of misalignment artifacts can be helpful both in data curation and in facilitating clinical workflow. This study aimed to develop an explainable machine learning approach to detect misalignment artifacts in PET/CT imaging.Approach.This study included 1216 PET/CT images. All images were visualized and images with respiratory misalignment artifact (RMA) detected. Using previously trained models, four organs including the lungs, liver, spleen, and heart were delineated on PET and CT images separately. Data were randomly split into cross-validation (80%) and test set (20%), then two segmentations performed on PET and CT images were compared and the comparison metrics used as predictors for a random forest framework in a 10-fold scheme on cross-validation data. The trained models were tested on 20% test set data. The model's performance was calculated in terms of specificity, sensitivity, F1-Score and area under the curve (AUC).Main results.Sensitivity, specificity, and AUC of 0.82, 0.85, and 0.91 were achieved in ten-fold data split. F1_score, sensitivity, specificity, and AUC of 84.5 vs 82.3, 83.9 vs 83.8, 87.7 vs 83.5, and 93.2 vs 90.1 were achieved for cross-validation vs test set, respectively. The liver and lung were the most important organs selected after feature selection.Significance.We developed an automated pipeline to segment four organs from PET and CT images separately and used the match between these segmentations to decide about the presence of misalignment artifact. This methodology may follow the same logic as a reader detecting misalignment through comparing the contours of organs on PET and CT images. The proposed method can be used to clean large datasets or integrated into a clinical scanner to indicate artifactual cases.

The meta-governance of quality registries for public health

Abstract Issue/problem Healthcare-related planning and policy requires evidence, which in turn requires data. Generating sensitive, high-quality data is expensive. Given the constraints of routine or study-related data for quality monitoring, quality registries are commonly used. But when is it adequate, from a public health meta-governance point of view, to establish dedicated quality registries? Description of the problem Many countries have established public patient registries over time. The lack of a meta-governance framework complicates decision-making for establishing and funding dedicated quality registries. Simultaneously, legal advancements like the European Health Data Space may enhance the use of routine data in healthcare policy, potentially transforming both registry and meta-governance practices. Results We identified key meta-governance challenges, such as setting criteria for the creation, modification, or closure of registries, deciding on public funding and choosing between routine and specially collected data. We argue against using absolute thresholds like disease prevalence, advocating for a scorecard approach that evaluates epidemiologic reasoning, policy relevance, healthcare impact and factors like data availability and registry synergies. Lessons The EHDS will enable enhanced secondary data use, necessitating decisions on when to use routine versus specifically collected data. Decision-makers must consider sustainability, ethics, practicability and data linkability, while recognizing risks such as data quality limitations and potential bias from original data collection purposes. Key messages • The necessity for quality registries varies by case, serving specific policy questions. Optimizing funds and avoiding arbitrary decisions require a strategy based on a meta-governance framework. • With the introduction of EHDS, the focus may shift more toward data curation and quality control, making it critical to align registry efforts with the broader health information system.

The state-of-the-art machine learning model for plasma protein binding prediction: Computational modeling with OCHEM and experimental validation

Plasma protein binding (PPB) is closely related to pharmacokinetics, pharmacodynamics and drug toxicity. Existing models for predicting PPB often suffer from low prediction accuracy and poor interpretability, especially for high PPB compounds, and are most often not experimentally validated. Here, we carried out a strict data curation protocol, and applied consensus modeling to obtain a model with a coefficient of determination of 0.90 and 0.91 on the training set and the test set, respectively. This model (available on the OCHEM platform https://ochem.eu/article/29) was further retrospectively validated for a set of 63 poly-fluorinated molecules and prospectively validated for a set of 25 highly diverse compounds, and its performance for both these sets was superior to that of the other previously reported models. Furthermore, we identified the physicochemical and structural characteristics of high and low PPB molecules for further structural optimization. Finally, we provide practical and detailed recommendations for structural optimization to decrease PPB binding of lead compounds.

Validation of diagnostic wearable ECG data from high-risk patients and integration into the atrial fibrillation catheter ablation pathway

Abstract Background Wearables like the Apple Watch (AW) offer non-invasive, long-term monitoring and patient-led, single-lead ECG recordings. Although validated for Atrial Fibrillation (AF) detection in the general population, the accuracy and clinical utility of the single-lead ECG recording feature for the diagnosis of AF has not been evaluated in patients at high risk of atrial arrhythmias.(1) An evaluation of the accuracy of the Wearable-derived labels is important to determine the feasibility of integrating AW-based remote monitoring into clinical pathways. Objective To evaluate the accuracy and data burden from AW-based, patient-led ECG monitoring in patients undergoing AF catheter ablation. Methods Patients referred for first-time AF catheter ablation to the Study Institution between January 2022 and March 2023 were invited to enrol in the AF Follow-up with Apple Watch (AFFU-AW) Trial. Patients randomised to the active arm of the study received 12 months of post-ablation follow-up using a loaned Apple Watch. Participants underwent an education session and were asked to perform daily ECGs; sending through any labelled as AF, inconclusive, or in the context of symptoms. The accuracy of the automated ECG labelling was determined against blinded, independent labelling by two experienced Electrophysiologists. Results 1093 remote transmissions were emailed in for evaluation from 52 patients to the Study team. This was a median of 6 [2, 17] per patient and a receiver burden of 2 [1, 4] ECGs per day. 475 were categorised by the AW as AF with a sensitivity of 0.71 and specificity of 0.96 compared to expert labelling. 342 (31%) were not categorised by the wearable, with an ‘inconclusive’ interpretation (161 [47%]) or ‘high heart rate’ (108 [32%]) as the most cited reasons. 178 (55%) of the uncategorised ECGs were subsequently overread as AF. Accompanying symptoms were reported with 324 (30%) ECGs, most commonly palpitations. Conclusion This is the first reported evaluation of the AF diagnostic feature based on AW ECG recordings as part of a remote patient rhythm surveillance pathway after catheter ablation. The high positive predictive value of ‘Sinus Rhythm’ and ‘AF’ labels and the high specificity of the ‘AF’ label are comparable to diagnostic algorithms in implantable loop recorders.(2) The source code to automate data curation and structured databasing of wearable ECGs has been made available for care providers to integrate the service into device monitoring workflows.(3)

Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high-throughput workflows, helping researchers recognize low-integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck─in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source Python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semiautonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Initially developed for integration with Thermo Orbitrap workflows, Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS.

Empirical estimation of metal powder bed fusion technological improvement rate

AbstractThis study empirically estimates the technological improvement rate (TIR) of metal powder bed fusion (PBF) technology, widely used in aerospace, automotive, and medical industries. PBF's continuous long-term adoption growth is driven by its ability to enhance manufacturing efficiency in terms of time and raw material use, as well as its capability to produce high-quality, high-strength, complex-shaped parts. Measuring the technological development of PBF is crucial as itis enlarging its application domain and is increasingly considered a viable alternative to traditional manufacturing technologies across a broader range of applications. We resorted to the literature to collect information and assess which technical parameters are most relevant to measure the capabilities of PBF. With those, we established an ideal functional performance metric (FPM) capable of comprehensively assessing PBF's technological performance improvement. Considering all available data sources and PBF machines ever made commercially available, a data set of technical parameters was constructed. This was followed by a data curation process focusing on data availability and reliability. The resultant practical FPM was used to estimate the TIR of PBF technology. By employing regression analysis, we estimate a yearly improvement of 26.8%. This empirical rate comes as a more accurate and reliable substitute to the previously indirectly estimated patent-derived rate of 33.3%. Our findings underscore PBF's capability of keeping pace with its growing significance and wider industrial applications. The results of this study provide a key metric for those in the industry and research, confirming the rapid performance growth and establishing a standard for future industrial uses.