Good Quality Data Research Articles

Results of the 2020 Genomic Proficiency Test for the network of European Union Reference Laboratory for Antimicrobial Resistance assessing whole-genome-sequencing capacities.

The global surveillance and outbreak investigation of antimicrobial resistance (AMR) is amidst a paradigm shift from traditional biology to bioinformatics. This is due to developments in whole-genome-sequencing (WGS) technologies, bioinformatics tools, and reduced costs. The increased use of WGS is accompanied by challenges such as standardization, quality control (QC), and data sharing. Thus, there is global need for inter-laboratory WGS proficiency test (PT) schemes to evaluate laboratories' capacity to produce reliable genomic data. Here, we present the results of the first iteration of the Genomic PT (GPT) organized by the Global Capacity Building Group at the Technical University of Denmark in 2020. Participating laboratories sequenced two isolates and corresponding DNA of Salmonella enterica, Escherichia coli and Campylobacter coli, using WGS methodologies routinely employed at their laboratories. The participants' ability to obtain consistently good-quality WGS data was assessed based on several QC WGS metrics. A total of 21 laboratories from 21 European countries submitted WGS and meta-data. Most delivered high-quality sequence data with only two laboratories identified as overall underperforming. The QC metrics, N50 and number of contigs, were identified as good indicators for high-sequencing quality. We propose QC thresholds for N50 greater than 20 000 and 25 000 for Campylobacter coli and Escherichia coli, respectively, and number of contigs >200 bp greater than 225, 265 and 100 for Salmonella enterica, Escherichia coli and Campylobacter coli, respectively. The GPT2020 results confirm the importance of systematic QC procedures, ensuring the submission of reliable WGS data for surveillance and outbreak investigation to meet the requirements of the paradigm shift in methodology.

Assessing the conversion of electronic medical record data into antibiotic stewardship indicators.

Measuring the appropriateness of antibiotic use is crucial for antibiotic stewardship (ABS) programmes to identify targets for interventions. To assess the technical feasibility of converting electronic medical record (EMR) data into ABS indicators. In this observational feasibility study covering a period of 2 years, the EMRs of patients hospitalized at a large non-university hospital network and receiving at least one dose of a systemic antibiotic were included. ABS indicators measuring steps in the process of antibiotic prescription proposed by the literature were collected and rephrased or defined more specifically to be calculable if needed. Algorithms were programmed in R to convert EMR data into ABS indicators. The indicators were visualized in an interactive dashboard and the plausibility of each output value was assessed. In total, data from 25 337 hospitalizations from 20 723 individual patients were analysed and visualized in an interactive dashboard. Algorithms could be programmed to compute 89% (25/28) of all pre-selected indicators assessing treatment decisions automatically out of EMR data, with good data quality for 46% (13/28) of these indicators. According to the data quality observed, the most important issues were (i) missing or meaningless information on indication (e.g. 'mild infection') and (ii) data processing issues such as insufficiently categorized metadata. The calculation of indicators assessing treatment decisions from EMRs was feasible. However, better data structure and processing within EMR systems are crucial for improving the validity of the results.

BrewER: Entity Resolution On-Demand

The task of entity resolution (ER) aims to detect multiple records describing the same real-world entity in datasets and to consolidate them into a single consistent record. ER plays a fundamental role in guaranteeing good data quality, e.g., as input for data science pipelines. Yet, the traditional approach to ER requires cleaning the entire data before being able to run consistent queries on it; hence, users struggle to tackle common scenarios with limited time or resources (e.g., when the data changes frequently or the user is only interested in a portion of the dataset for the task). We previously introduced BrewER, a framework to evaluate SQL SP queries on dirty data while progressively returning results as if they were issued on cleaned data, according to a priority defined by the user. In this demonstration, we show how BrewER can be exploited to ease the burden of ER, allowing data scientists to save a significant amount of resources for their tasks.

Evaluation of Tuberculosis Surveillance System in North Sumatra Provincial Health Office 2022

Tuberculosis Global Report 2021 TB was being the major death caused by one infection agent. Result of health problem analysis in North Sumatra Provincial Health Office, TB was choosen as the first priority with main determinant low treatment coverage TB. The purpose of this study is to know the implementation of surveillance TB system based on system approach and evaluate based on surveillance attribute in North Sumatra Provincial Health Office 2022. The type of research is descriptive with evaluation design. Subject of the research was surveillance TB system has been implemented in North Sumatra and Deli Serdang Health Office District in 2021 with TB officer in Health Office. Data collected by interview with TB officer using questionarre and supported by document study. Evaluation was doing by system approach and based on surveillance attribute. The data interpreted by narration and table. Results of evaluation system component TB in Health Office in Provincial and District obtained that implementation system of TB was appropriate with surveillance guidelines and TB control guidelines by Ministry of Health. Based on attribute evaluation, from 9 attributes Health Office Provincial and Distrcit got 5 attributes with good results means that TB surveillance program was simple, acceptable, good data quality, sensitive, and stable but the surveillance program was not flexible, low predictive value positive, not timeliness, and not representative. Based on the evaluation results, TB surveillance system in North Sumatra Provincial Health Office and Deli Serdang District Health Office has a good system based on input, process, and output. Based on attribute surveillance, its still need some improvements in terms of increasing predictive value positive by doing screening people before TCM examination and analysis of cases by person place and time so the data would be representative and describe the real condition

Data heterogeneous federated learning algorithm for industrial entity extraction

Entity extraction is an important part to realize digital transformation in the industrial field. Building an entity extraction model in the industrial field requires a lot of data. The parties in industry often cannot share data due to commercial competition and security and privacy issues, thus forming “Data Island”. Federated learning provides a solution to this problem. Federated learning is a distributed machine learning framework that allows each party to train locally and independently using their own private data. The model parameters or gradient information of each party will be aggregated to the central server, thus forming a model jointly trained by all parties. This approach can not only protect the security and privacy of data from all parties, but also fully utilize their data resources. Federated learning can effectively solve the problem of data island, but it still faces some problems and challenges, among which the most typical problem is data heterogeneity. To address the data islanding problem and data heterogeneity problem faced by industrial entity extraction, this paper uses a federated learning framework to solve the data islanding problem and proposes the FedDP algorithm. This algorithm assigns weights based on the data quality performance of each participant. Participants with relatively good data quality performance have higher weights in the aggregation stage, while participants with relatively poor data quality performance have lower weights in the aggregation stage, thus optimizing the performance of federated learning in heterogeneous data scenarios.

Application of StyleGAN Architecture for Generating Venous Leg Ulcer Images.

Leg ulcers caused by impaired venous blood return are the most typical chronic wound form and have a significant negative impact on the lives of people living with these wounds. Thus, it is important to provide early assessment and appropriate treatment of the wounds to promote their healing in the normal trajectory. Gathering quality wound data is an important component of good clinical care, enabling monitoring of healing progress. This data can also be useful to train machine learning algorithms with a view to predicting healing. Unfortunately, a high volume of good-quality data is needed to create datasets of suitable volume from people with wounds. In order to improve the process of gathering venous leg ulcer (VLU) data we propose the generative adversarial network based on StyleGAN architecture to synthesize new images from original samples. We utilized a dataset that was manually collected as part of a longitudinal observational study of VLUs and successfully synthesized new samples. These synthesized samples were validated by two clinicians. In future work, we plan to further process these new samples to train a fully automated neural network for ulcer segmentation.

Fault detection and diagnostics in the context of sparse multimodal data and expert knowledge assistance: Application to hydrogenerators

Deep learning-based methods for industrial fault detection and diagnostics (FDD) depend strictly on good quality and sufficient quantity of condition monitoring data. However, in real-world industrial settings, data collection is usually limited, leading to sparse and insufficient data to train a data-driven model. Therefore, this work proposes a new methodology to address this issue by leveraging multimodal data anddomain knowledge to develop a data-driven solution. Particularly for large, complex machinery, unimodal sensors may not fully capture the health state information. In such cases, multimodal data may provide complementary insights into the machine degradation. However, challenges mentioned above need to be addressed before these data can be useful. The multimodal learning method presented within the methodology can benefit from useful information from different data modalities and from domain expert knowledge, even when these data are of low volume. The performance of the proposed methodology is investigated through a real industrial case study involving energy production systems. The obtained results demonstrate the potential of the proposed methodology in augmenting the FDD accuracy and tackling the sparse data challenge.

A Twin Study of Altered White Matter Heritability in Youth With Autism Spectrum Disorder

White matter alterations are frequently reported in autism spectrum disorder (ASD), yet the etiology is currently unknown. The objective of this investigation was to examine, for the first time, the impact of genetic and environmental factors on white matter microstructure in twins with ASD compared to control twins without ASD. Diffusion-weighted MRIs were obtained from same-sex twin pairs (6-15 years of age) in which at least 1 twin was diagnosed with ASD or neither twin exhibited a history of neurological or psychiatric disorders. Fractional anisotropy (FA) and mean diffusivity (MD) were examined across different white matter tracts in the brain, and statistical and twin modeling were completed to assess the proportion of variation associated with additive genetic (A) and common/shared (C) or unique (E) environmental factors. We also developed a novel Twin-Pair Difference Score analysis method that produces quantitative estimates of the genetic and environmental contributions to shared covariance between different brain and behavioral traits. Good-quality data were available from 84 twin pairs, 50 ASD pairs (32 concordant for ASD [16 monozygotic; 16 dizygotic], 16 discordant for ASD [3 monozygotic; 13 dizygotic], and 2 pairs in which 1 twin had ASD and the other exhibited some subthreshold symptoms [1 monozygotic; 1 dizygotic]) and 34 control pairs (20 monozygotic; 14 dizygotic). Average FA and MD across the brain, respectively, were primarily genetically mediated in both control twins (A= 0.80, 95% CI [0.57, 1.02]; A= 0.80 [0.55, 1.04]) and twins concordant for having ASD (A= 0.71 [0.33, 1.09]; A= 0.84 [0.32,1.36]). However, there were also significant tract-specific differences between groups. For instance, genetic effects on commissural fibers were primarily associated with differences in general cognitive abilities and perhaps some diagnostic differences for ASD because Twin-Pair Difference-Score analysis indicated that genetic factors may have contributed to ∼40% to 50% of the covariation between IQ scores and FA of the corpus callosum. Conversely, the increased impact of environmental factors on some projection and association fibers were primarily associated with differences in symptom severity in twins with ASD; for example, our analyses suggested that unique environmental factors may have contributed to ∼10% to 20% of the covariation between autism-related symptom severity and FA of the cerebellar peduncles and external capsule. White matter alterations in youth with ASD are associated with both genetic contributions and potentially increased vulnerability or responsivity to environmental influences. We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as living with a disability. The author list of this paper includes contributors from the location and/or community where the research was conducted and they participated in the data collection, design, analysis, and/or interpretation of the work.

A Survey of Data Quality Requirements That Matter in ML Development Pipelines

The fitness of the systems in which Machine Learning (ML) is used depends greatly on good-quality data. Specifications on what makes a good-quality dataset have traditionally been defined by the needs of the data users—typically analysts and engineers. Our article critically examines the extent to which established data quality frameworks are applicable to contemporary use cases in ML. Using a review of recent literature at the intersection of ML, data management, and human-computer interaction, we find that the classical “fitness-for-use” view of data quality can benefit from a more stage-specific approach that is sensitive to where in the ML lifecycle the data are encountered. This helps practitioners to plan their data quality tasks in a manner that meets the needs of the stakeholders who will encounter the dataset, whether it be data subjects, software developers or organisations. We therefore propose a new treatment of traditional data quality criteria by structuring them according to two dimensions: (1) the stage of the ML lifecycle where the use case occurs vs. (2) the main categories of data quality that can be pursued (intrinsic, contextual, representational and accessibility). To illustrate how this works in practice, we contribute a temporal mapping of the various data quality requirements that are important at different stages of the ML data pipeline. We also share some implications for data practitioners and organisations that wish to enhance their data management routines in preparation for ML.

Incentive Mechanism Design for Responsible Data Governance: A Large-scale Field Experiment

A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representative, and individuals providing the data should have autonomy over what data is collected. In this article, we consider the setting of collecting personally measured fitness data (physical activity measurements), in which some individuals may not have an incentive to measure and report accurate data. This can significantly degrade the quality of the collected data. On the other hand, high-quality collective data of this nature could be used for reliable scientific insights or to build trustworthy artificial intelligence applications. We conduct a framed field experiment (N = 691) to examine the effect of offering fixed and quality-dependent monetary incentives on the quality of the collected data. We use a peer-based incentive-compatible mechanism for the quality-dependent incentives without spot-checking or surveilling individuals. We find that the incentive-compatible mechanism can elicit good-quality data while providing a good user experience and compensating fairly, although, in the specific study context, the data quality does not necessarily differ under the two incentive schemes. We contribute new design insights from the experiment and discuss directions that future field experiments and applications on explainable and transparent data collection may focus on.

Well-being and cultural identity for Māori: Knowledge of iwi (tribal) affiliations does not strongly relate to health and social service outcomes

Research indicates that experiences in health and social services vary depending on identity. For Indigenous groups, identity and affiliation is complex. This paper explores ethnicity and knowledge of tribal (iwi) affiliations for Māori (the Indigenous peoples of Aotearoa New Zealand), and links this to health and social service outcomes in administrative data, the national Census, and Māori social survey data. While many initiatives have sought to connect Māori to iwi - where such knowledge has been severed by colonization - we find surprisingly few differences in data between those who named tribal affiliations and those who did not, across sole- and mixed-Māori ethnicity groups. Those who did not name an iwi were less likely to live in overcrowded homes, but were less likely to own that home, and more likely to be a smoker. Unsurprisingly, those who did not name tribal affiliations were less likely to find Māori culture as important, although many still did. These groups also had slightly less contact with social networks and support, plus felt lonelier. The results also point to sole-ethnic identification as Māori as a key marker of experiences of inequity and suggest that connections to tribal affiliations are more complicated than a binary of “connected” or “disconnected”. However, in some indicator areas, affiliation differences should be followed up with future work. We argue these results give further weight to the need for good quality data and indicators designed with Māori populations in mind to measure and monitor inequity.

Quality Assessment of Various CHC NAV GNSS Receiver Models

GNSS is utilized in numerous industries and applications to determine a location's (position and time). GNSS technology was quickly used for surveying because it can offer correct latitude, longitude, and height without establishing angles and distances. It's utilized worldwide in mapping and surveying. An ideal GNSS receiver for geodetic and other surveying applications must receive and monitor both code pseudo ranges and carrier phase signals, including the Y-codeless signal. Using geodetic equipment can offer the most accurate location data, but it depends on the instrument's quality. Choosing the proper equipment ensures trustworthy location data. Surveyors may pick cheap equipment caused by financial constraints. Does the data from different GNSS receiver brands have the same quality? By performing static observation on various GNSS receiver from CHC (i90, i83, i80, i73 and i70) key parameters are extracted from the data for data integrity assessment in terms of multipath, cycle slip, signal noise ratio, sky plot and others. CHC Geomatics Office 2 was used to extract the mentioned parameters for quality assessment. The two-day observation lasted from April 23 to April 25, 2022. (GPST). Data availability and data completeness are closely related criteria. For full potential analysis, receivers must be fully operating. No receiver has 100% data completeness or 24-hour data availability. Each receiver's data demonstrates that error varies by parameter. CHC i70 has the least multipath effect and CHC i80 the greatest. Overall, MP1 and MP2 multipath effects were below 0.5. CHC i70 had the lowest cycle slip ratio as it recorded the strongest signal strength while the greatest cycle slip ratio occurred to CHC i80. Each receiver's sky map exhibits the same pattern for both observation days, indicating they tracked the same satellite. Lastly, the average coordinates acquired either on various days or among the receivers indicates a maximum of 0.28m in vector displacement where it is appropriate to claim that each receiver received different coordinates since they were not locating on one place but adjacent within 1 meter radius. From the data analyzed, it is concluded that CHC i83 has best data quality among CHC models while CHC i80 obtained the worst data quality, but this does not indicate that model cannot provide good quality data.

The Feasibility and Data Quality for a Listening Comprehension Task in an Unmoderated Remote Study With Children.

The purpose of this research note was to examine the sample representation, feasibility and completion, and data quality when using an unmoderated remote study (i.e., conducted without direct contact with a researcher) for a listening comprehension task with 4- to 11-year-old children. Thirty-five participants met inclusionary criteria for this study. Sample representation was examined descriptively. Feasibility and completion (i.e., submission of parent questionnaires and more than 50% of task with no missing data) were examined descriptively and compared with differences of proportions tests. Data quality (i.e., missing data for items with interference or not codable) was examined descriptively with multilevel logistic regression models, as well as one-sample proportions tests by listening comprehension task and participant characteristics. Our sample skewed toward predominantly White and toward families with highly educated parents. Overall, most participants completed the task and had quality data (i.e., audibly clear responses that could be coded, few missing responses, and task completion) in this unmoderated format. There were not any statistically significant effects across participant characteristics in terms of rates of completion. Data quality only significantly differed by response type with mouse selection having the least amount of missing data followed by prompted audio-recorded questions and then open-ended audio-recorded questions. The unmoderated remote study approach seems feasible for a listening comprehension task for most children ages 4-11 years old. Future work is needed to determine if these results apply to samples with broader representation. Overall, we found good data quality despite the less controlled environment in remote studies. https://doi.org/10.23641/asha.23114924.

The Maturity Model of Data Quality Management in Banking Industry: PT XYZ Core System Customer Data

PT XYZ, engaged in the financial industry, has a target to become a leading company in Southeast Asia and has been supported by more than 200 million customer data in its core system. This huge amount of data is expected to create business opportunities, build a risk-aware culture, and increase supremacy in the business strategy of PT XYZ. These things can be achieved if the data used is of good quality data. In fact, found anomalies in a large number of customer data. To get recommendations for improving the quality of customer data, it is necessary to assess the quality of customer data. The customer data quality assessment in this study uses the method introduced by Loshin (2011). Loshin’s Data Quality Management Model (DQMM) adopts a capability maturity level model in building its characteristic matrix. Maturity levels obtained are 3.6 (expectations), 3.6 (dimensions), 4.4 (policy), 3.8 (procedures), 4.2 (governance), 3.8 (standardization), 4, 2 (technology), and 3.8 (performance management). Regarding the expectation that senior management can achieve the highest level of data quality, 9 strategic recommendations were produced 9 strategy recommendations were submitted to PT XYZ is the result of mapping between criteria that have not been met with data quality management activity in Data Management Body of Knowledge (DMBOK) version 2.0. Measurement and monitoring of good data quality is the most influential recommendation for PT XYZ.

High-mass Starless Clumps: Dynamical State and Correlation between Physical Parameters

In order to study the initial conditions of massive star formation, we have previously built a sample of 463 high-mass starless clumps (HMSCs) across the inner Galactic plane covered by multiple continuum surveys. Here, we use 13CO(2–1) line data from the SEDIGISM survey, which covers 78° in longitude (−60° < l < 18°, ∣b∣ < 0.°5) with 30″ resolution, to investigate the global dynamical state of these parsec-scale HMSCs (207 sources with good-quality data, mass 102–105 M ⊙, size 0.1–3.6 pc). We find that most HMSCs are highly turbulent with a median Mach number , and 44%–55% of them are gravitationally bound (with virial parameter α vir ≲ 2) if no magnetic fields are present. A median magnetic field strength of 0.33–0.37 mG would be needed to support these bound clumps against collapse, in agreement with previous observations of magnetic fields in regions of massive star formation. Luminosity-to-mass ratio, an important tracer of evolutionary stage, is strongly correlated with dust temperature. Magnetic field strength is also correlated with density. The Larson line width–size scaling does not hold in HMSCs. This study advances our understanding of the global properties of HMSCs, and our high-resolution observations with the Atacama Large Millimeter/submillimeter Array are in progress to study the resolved properties.

Against data individualism: Why a pandemic accord needs to commit to data solidarity

AbstractAn accord to increase global pandemic preparedness is currently negotiated at the World Health Organisation (WHO). One aspect is conspicuously missing from the draft version: data governance. As the COVID‐19 pandemic has shown, the availability of good quality data, and prudent ways of using this data while preventing other forms of data use that is harmful to people and communities, is literally a matter of life and death. It is of utmost importance for the pandemic accord to include a data governance strategy. We argue that data solidarity provides a suitable foundation for such a data governance strategy in the context of pandemics. It includes instruments to facilitate data use in cases where data use is likely to create benefits for many people without entailing high risks for individuals or communities. Data solidarity also calls to refrain from data use in cases where it would pose unacceptable risks. Finally, data solidarity helps to mitigate harm where it occurs. In sum, data solidarity helps to enhance the benefits from digital practices in health crises without playing out collective interests against the rights and needs of individuals.

Using Digital Tools to Study the Health of Adults Born Preterm at a Large Scale: e-Cohort Pilot Study.

Preterm birth is a global health concern. Its adverse consequences may persist throughout the life course, exerting a potentially heavy burden on families, health systems, and societies. In high-income countries, the first children who benefited from improved care are now adults entering middle age. However, there is a clear gap in the knowledge regarding the long-term outcomes of individuals born preterm. This study aimed to assess the feasibility of recruiting and following up an e-cohort of adults born preterm worldwide and provide estimations of participation, characteristics of participants, the acceptability of questions, and the quality of data collected. We implemented a prospective, open, observational, and international e-cohort pilot study (Health of Adult People Born Preterm-an e-Cohort Pilot Study [HAPP-e]). Inclusion criteria were being an adult (aged ≥18 years), born preterm (<37 weeks of gestation), having internet access and an email address, and understanding at least 1 of the available languages. A large, multifaceted, and multilingual communication strategy was established. Between December 2019 and June 2021, inclusion and repeated data collection were performed using a secured web platform. We provided descriptive statistics regarding participation in the e-cohort, namely, the number of persons who registered on the platform, signed the consent form, initiated and completed the baseline questionnaire, and initiated and completed the follow-up questionnaire. We also described the main characteristics of the HAPP-e participants and provided an assessment of the quality of the data and the acceptability of sensitive questions. As of December 31, 2020, a total of 1004 persons had registered on the platform, leading to 527 accounts with a confirmed email and 333 signed consent forms. A total of 333 participants initiated the baseline questionnaire. All participants were invited to follow-up, and 35.7% (119/333) consented to participate, of whom 97.5% (116/119) initiated the follow-up questionnaire. Completion rates were very high both at baseline (296/333, 88.9%) and at follow-up (112/116, 96.6%). This sample of adults born preterm in 34 countries covered a wide range of sociodemographic and health characteristics. The gestational age at birth ranged from 23+6 to 36+6 weeks (median 32, IQR 29-35 weeks). Only 2.1% (7/333) of the participants had previously participated in a cohort of individuals born preterm. Women (252/333, 75.7%) and highly educated participants (235/327, 71.9%) were also overrepresented. Good quality data were collected thanks to validation controls implemented on the web platform. The acceptability of potentially sensitive questions was excellent, as very few participants chose the "I prefer not to say" option when available. Although we identified room for improvement in specific procedures, this pilot study confirmed the great potential for recruiting a large and diverse sample of adults born preterm worldwide, thereby advancing research on adults born preterm.

Identifying and managing data quality requirements: a design science study in the field of automated driving

Good data quality is crucial for any data-driven system’s effective and safe operation. For critical safety systems, the significance of data quality is even higher since incorrect or low-quality data may cause fatal faults. However, there are challenges in identifying and managing data quality. In particular, there is no accepted process to define and continuously test data quality concerning what is necessary for operating the system. This lack is problematic because even safety-critical systems become increasingly dependent on data. Here, we propose a Candidate Framework for Data Quality Assessment and Maintenance (CaFDaQAM) to systematically manage data quality and related requirements based on design science research. The framework is constructed based on an advanced driver assistance system (ADAS) case study. The study is based on empirical data from a literature review, focus groups, and design workshops. The proposed framework consists of four components: a Data Quality Workflow, a List of Data Quality Challenges, a List of Data Quality Attributes, and Solution Candidates. Together, the components act as tools for data quality assessment and maintenance. The candidate framework and its components were validated in a focus group.

Enhancing SARS-CoV-2 Surveillance through Regular Genomic Sequencing in Spain: The RELECOV Network.

Millions of SARS-CoV-2 whole genome sequences have been generated to date. However, good quality data and adequate surveillance systems are required to contribute to meaningful surveillance in public health. In this context, the network of Spanish laboratories for coronavirus (RELECOV) was created with the main goal of promoting actions to speed up the detection, analyses, and evaluation of SARS-CoV-2 at a national level, partially structured and financed by an ECDC-HERA-Incubator action (ECDC/GRANT/2021/024). A SARS-CoV-2 sequencing quality control assessment (QCA) was developed to evaluate the network's technical capacity. QCA full panel results showed a lower hit rate for lineage assignment compared to that obtained for variants. Genomic data comprising 48,578 viral genomes were studied and evaluated to monitor SARS-CoV-2. The developed network actions showed a 36% increase in sharing viral sequences. In addition, analysis of lineage/sublineage-defining mutations to track the virus showed characteristic mutation profiles for the Delta and Omicron variants. Further, phylogenetic analyses strongly correlated with different variant clusters, obtaining a robust reference tree. The RELECOV network has made it possible to improve and enhance the genomic surveillance of SARS-CoV-2 in Spain. It has provided and evaluated genomic tools for viral genome monitoring and characterization that make it possible to increase knowledge efficiently and quickly, promoting the genomic surveillance of SARS-CoV-2 in Spain.

Selected-ion flow-tube mass spectrometry for the identification of volatile spoilage markers for fresh pork packaged under modified atmospheres

Microbial behavior during meat storage leads to the generation of volatile organic compounds (VOCs) and unpleasant off-odors. This study focused on a novel real-time analytical method, selected-ion flow-tube mass spectrometry (SIFT-MS), to monitor VOC quality and identify spoilage indicators for fresh pork stored under different packaging atmospheres (air, 70/0/30, 70/30/0, 5/30/65, 0/30/70 – v/v% O2/CO2/N2) at 4 °C. A comprehensive selection methodology was used to identify compounds with good instrumental data quality as well as a strong relationship with microbial growth and olfactory rejection. Based on the volatolome quantified by SIFT-MS, storage periods and conditions can be discriminated using multivariate statistics. Acetoin (or ethyl acetate) represented a significant pork quality marker for high-O2 conditions, whereas ethanol, 3-methylbutanal and sulfur compounds can indicate the anaerobic storage progress. Considering the applicability in monitoring different VOC profiles, SIFT-MS is expected to be promising in many storage scenarios to improve analytical efficiency and ensure reliability.