Fragmentation Data Research Articles

11.A. Scientific session: Overcoming Real-World Challenges in Setting up Health Data Linkages for Public Health Policy

Abstract Background Linking available administrative data has gained momentum in the last years to enhance public health research and public health formulation. Linkage of diverse data sources can be leveraged to understand social-determinants of health, the cost-effectiveness of an intervention, and to evaluate impact of policies. For instance, data linkage has been proven to be a powerful approach to periodically analyze real-world progress of the COVID-19 pandemic. Today, we are witnessing an unparalleled increase in electronic health data stemming from citizens who interact with services such as healthcare providers, employers, sick funds, and other governmental structures. This enormous amount of data opens many innovative possibilities in epidemiological research. The main advantages of the secondary use of data are the following: 1) it is enriching for new collected data (e.g. survey study), 2) it provides comprehensive information of the target population, and 3) it is cheaper than new data collection. Data linkage between data sources is, however, often a herculean task, and it comes with considerable challenges such as: 1) juridical complexity related to intra-national and cross-border data governance procedures, 2) fragmented data integration, 3) data delivery delays. Addressing these challenges is essential to unlocking the true potential of data linkages for timely responses to emerging public health questions, thereby contributing to evidence-based policy and care and transforming our health research into a One Health approach. Objectives The workshop’s main aim is to present the researcher’s lessons-learned and approaches in applying data linkage from an European, and a country-level perspective through case-studies while creating a space for the public to share their own view. In this workshop we aim to 1) provide a helicopter overview of current data linkages approaches in Europe, 2) illustrate this with case studies from an international, and a country-level perspective, and 3) emphasize the challenges and disadvantages of those data linkages, while sharing potential solutions. Workshop outline The workshop is divided into a knowledge translation, and an interactive part. In the first part of the workshop, three speakers will give a presentation of each 10 minutes with a Q&A of 5 minutes between each presentation where data linkage will be introduced, and three case studies on how data linkage can be used will be presented. The first presentation will introduce approaches to data linkages in Europe, and will delve into how AI can be used during routine data linkage. The second presentation will show a case-study on how an international data linkages can be done in an European federated manner. The third presentation will further illustrate how nationally data can be linked to investigate health inequalities. In the second part of the workshop, the audience will be invited to interact with the speakers and share their experiences. Key messages • A comprehensive overview of different current data linkages approaches are provided, setting the stage for discussions on methods and best practices. • Administrative data linkages are a powerful tool to answer important public health challenges, but important challenges related to data governance need to be addressed to realize the full potential.

High-resolution modeling and projection of heat-related mortality in Germany under climate change

Abstract Heat is a leading cause of premature deaths during summer months. Understanding the relationship between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Traditional statistical methods have been repeatedly applied to analyze heat-related mortality. However, spatially and temporally high-resolution analyses are challenging due to fragmented data archives across different agglomeration levels, especially for mortality data. We propose a neural network-based model to estimate heat-related mortality. This approach allows for high temporal and spatial resolution estimations, such as regional heat risk during specific heatwaves. Using Germany as a case study, we calculated heat-related excess mortality rates at the district level and visualized the dynamics of local health risks within a heatwave. Overall, we estimated a total of 48,000 heat-related deaths in Germany over the last decade (2014-2023), with most occurring during specific heatwave events. For example, in 2023, the heatwave from July 7-14 contributed approximately 28% (1100 deaths) to the total of about 3,900 heat-related deaths for the entire year. This estimation is consistent with previously published reports from the Robert Koch Institute (RKI), considering the resolution differences in the input data. In addition, we combined our model with shared socio-economic pathways (SSPs) of future climate change, assuming a static population and baseline mortality rate. The results suggest that heat-related risk in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat. Our approach is a valuable tool for the monitoring of regional heat-related risks as well as scenario-based risks projections for the future. The results of the model can help develop climate-driven public health strategies, aiding in the identification of local risks during heatwaves and in long-term resilience planning. Key messages • High-resolution modeling of heat-related mortality helps us to develop targeted adaptation and prevention plans. • The majority of heat-related mortality occurs during heatwaves in summer.

High-resolution modeling and projection of heat-related mortality in Germany under climate change.

Heat has become a leading cause of preventable deaths during summer. Understanding the link between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Yet, data analyses are challenging due to often fragmented data archives over different agglomeration levels. Using Germany as a case study, we develop a multi-scale machine learning model to estimate heat-related mortality with variable temporal and spatial resolution. This approach allows us to estimate heat-related mortality at different scales, such as regional heat risk during a specific heatwave, annual and nationwide heat risk, or future heat risk under climate changescenarios. We estimate a total of 48,000 heat-related deaths in Germany during the last decade (2014-2023), and the majority of heat-related deaths occur during specific heatwave events. Aggregating our results over larger regions, we reach good agreement with previously published reports from Robert Koch Institute (RKI). In 2023, the heatwave of July 7-14 contributes approximately 1100 cases (28%) to a total of approximately 3900 heat-related deaths for the whole year. Combining our model with shared socio-economic pathways (SSPs) of future climate change provides evidence that heat-related mortality in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat under static sociodemographic developments assumptions. Our approach is a valuable tool for climate-driven public health strategies, aiding in the identification of local risks during heatwaves and long-term resilience planning.

Conceptualizing the Antecedents and Individual Impact of Business Intelligence in the Public Sector: The Technology-Organisation-Authoritative Framework

Business intelligence (BI) refers to a technological tool and process that transforms large and fragmented data into well-informed graphical insights to aid managerial decision-making. Recognizing the significant benefits of BI in leveraging extensive amounts of data, governments have been progressively investing in BI tools to improve efficiency in public offices. Nevertheless, despite a significant body of knowledge related to BI adoptions and their advantages, there is a dearth of theoretical understanding of how the effective use of the system affects employees' work performance, particularly among civil servants who have distinctive work natures compared to businesses. Most studies have also ignored external and institutional factors that influence individual usage, whilst studies on the effective use of BI tools remain limited. The paper thus proposed a new conceptual framework for examining the factors that influence the effective use of BI and its impact on individual job performance. The suggested propositions could provide a theoretical contribution by integrating technological, organizational and authoritative dimensions that are novel and unique to the public sector. It would also contribute to practical understandings for public managers and policymakers in ensuring the investment made on BI is worthwhile. Ultimately, the paper seeks to bridge the gap in BI studies related to public organizations.

Integrating Semantic Web, Artificial Intelligence, and Knowledge Management for Enhanced Accounting in SMEs

This study explores the integration of Semantic Web (SW), Artificial Intelligence (AI), and Knowledge Management (KM) to improve accounting practices in small and medium-sized enterprises (SMEs). Recognizing the challenges SMEs face in managing accounting knowledge due to insufficient expertise and fragmented data sources, this research proposes a conceptual model that utilizes SW technologies, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), to create a unified ontology for diverse accounting information. By leveraging AI techniques, including machine learning and natural language processing, the model aims to enhance knowledge retrieval, ontology enrichment, and case-based reasoning, facilitating more efficient and accurate accounting knowledge management. The findings highlight the potential of this integrated approach to support online training, decision-making, and overall competitiveness of SMEs, addressing the limitations of traditional accounting practices. The study concludes with recommendations for further research to refine the integration of SW, AI, and KM in accounting.

Gaining Insight: Data Needs for Firearm Injury Prevention Researchers

ABSTRACTBetween 1996 and 2018, there was little federally funded research on gun violence, even though it remains a perennial issue, resulting in considerable loss of life. Advocacy and academic groups piece together data to conduct their research, but the U.S. firearms data infrastructure is limited. A current National Institute of Health (NIH)‐funded coordinating center led by researchers at the University of Michigan aims to help confront that problem. This Quality Improvement (QI) study seeks to uncover the information needs of researchers in firearm injury prevention and community‐based interventions through interviews with public health, medical, and criminal justice researchers. Interviews reveal problems associated with existing databases and infrastructures, as well as what researchers need to support their research. We find fragmented data resources and a lag in public health data availability create obstacles to obtaining many datasets. The researchers also describe problems and opportunities for data harmonization across intervention‐focused studies.

Experimental and numerical investigation on rock fracturing and fragmentation under coupled static pressure and blasting

The blasting technique is widely adopted to break rock in civil and mining engineering, and its operation is generally subjected to static pressure due to tectonic and gravitational stresses. In the present study, the rock fracturing and fragmentation under coupled static pressure (uniaxial pressure and confining pressure) and blasting are experimentally and numerically investigated. First, 11 blast tests with 100-mm cubic red sandstone samples are carried out based on a biaxial loading system. Then, the blast-produced rock fragmentation in blast testing is numerically modeled using finite element method with LS-DYNA, and the explosion pressure attenuation and fracture evolution in rock samples are numerically reproduced. The simulated rock fragmentation data are obtained by image processing in ImageJ, and the fragment size distribution of red sandstone under combined static stress and blasting is characterized using Weibull distribution. Accordingly, the effects of uniaxial pressure and confining pressure on blast-created rock fragmentation are quantitatively compared and analyzed, and the corresponding mechanisms are discussed and revealed. The current findings indicate that the static pressure plays a role in increasing blast-induced compressive stress and reducing tensile stress in both radial and hoop directions and thus results in a decrease in the length and number of fractures propagating vertically to the direction of applied pressure, further leading to the creation of coarser fragmentation. Blasting under confining pressure produces larger fragments than that under uniaxial pressure. The average fragment size increases quickly at first and then increases slowly with the increase of static stress, which can be well characterized using a logarithm equation. Based on current findings, increasing the free surface is crucial in practical blasting to improve rock fragmentation performance under blasting.

Malnutrition in tribal people living with tuberculosis in India: A systematic review & meta-analysis.

Background & objectives Tuberculosis (TB) remains a persistent public health challenge in India. The country also has high proportions of malnutrition in different areas, making the population susceptible to TB. Among vulnerable populations, such as tribal communities, TB and malnutrition pose significant threats. Tribal populations exhibit disproportionately higher TB prevalence rates compared to non-tribal groups. Also, malnutrition prevalence among tribal communities far exceeds national averages in India. Amidst fragmented data, a systematic review of nutritional status among tribal populations with TB in India is imperative to inform comprehensive intervention strategies. Methods Cross-sectional, case-control, cohort and interventional studies reporting the nutritional status of active TB affected individuals among tribal populations in India were specifically included if terms such as "malnutrition" or "low BMI" were used. This systematic review followed PRISMA guidelines and was registered with PROSPERO (CRD42020168328). The data was assessed using a random-effects model and standardized mean with 95% confidence interval (CI). The I2 statistics indicated heterogeneity. Publication bias was evaluated using funnel plots. Results Out of 124 studies, 20 were selected for initial screening, 15 were excluded due to a lack of appropriate data, and five were included for the final analysis. The pooled prevalence estimates of malnutrition among Indian tribes living with TB was 514 per 1000 people, with 95% CI: 232-791. The sensitivity analysis showed that no individual study influenced the estimation of pooled prevalence. Interpretation & conclusions In the background of India, contributing considerably to the burden of TB-related morbidity and mortality alongside undernutrition, reviews have underscored undernutrition as the predominant risk factor for TB, affecting over 50 per cent of the population in some States. This situation is exacerbated among the tribal communities in India due to the dual burdens of undernutrition and TB being more pronounced. Our systematic review consolidates evidence from five studies, revealing the prevalence of malnutrition among tribal TB patients. Conducting well-planned implementation research to address this dual burden among the underprivileged population would help achieve the ambitious goal of a TB-free India 2025.

Digital Twin Framework for Aircraft Lifecycle Management Based on Data-Driven Models

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle.

OPTIMIZING ULTRASONIC gDNA FRAGMENTATION FOR NGS SEQUENCING

The fragmentation of genomic DNA (gDNA) is a crucial step in many Illumina library preparation protocols, including the TSO 500 assay, aimed at achieving fragment sizes ranging from 90 to 250 base pairs (bp) for next-generation sequencing (NGS). However, challenges arise in practice due to variations in fragmentation times, which are influenced by different extraction methods and sample sources. To optimize ultrasonic gDNA fragmentation across various extraction methods for NGS sequencing applications. Shearing of gDNA was performed using the Covaris S220 system, employing microtubes containing a total volume of 130 microliters of diluted Tris EDTA (pH 8.0) and 100 ng of gDNA, following the manufacturer's protocol. gDNA samples were extracted using three different kits: AllPrep DNA/RNA FFPE (Qiagen) from FFPE blocks, ReliaPrep FFPE gDNA Miniprep system (Promega) from glass slides, and Gentra Puregene (Qiagen) from cell lines. The shearing times varied depending on the extraction method and sample source. gDNA extracted using the AllPrep DNA/RNA FFPE kit from FFPE blocks required shearing times ranging from 300 to 400 seconds. In contrast, gDNA extracted using the ReliaPrep FFPE gDNA Miniprep system from glass slides exhibited shearing times of approximately 480 to 500 seconds. Lastly, gDNA extracted using the Gentra Puregene kit from cell lines showed shearing times ranging from 150 to 180 seconds. Our study demonstrates the influence of gDNA extraction methods and sample sources on ultrasonic fragmentation efficiency for NGS sequencing. The observed differences in shearing times highlight the need for method optimization to achieve consistent and optimal fragment sizes across diverse sample types. These findings contribute to the development of standardized protocols for gDNA fragmentation, facilitating reliable and reproducible NGS data generation in various research and clinical settings. Further investigations into the underlying factors affecting fragmentation efficiency are warranted to advance our understanding and improve NGS library preparation methodologies.

Association between depression symptom severity and total cost of care: Findings from a large, 2-year, claims-based, retrospective population health study

BackgroundDespite the substantial impact of depression on individuals and healthcare utilization, little is known about the specific relationship between depression severity and total cost of care (TCC). This study evaluates the association between depression symptom severity and TCC and how changes in severity affect TCC. MethodsThe analysis was conducted using insurance claims data and data from electronic health records between January 1, 2019 and December 31, 2020. Inclusion criteria comprised insured individuals with coverage during 2019 or 2020, aged one year or older, and identified as having depression in at least one year of the study. Depression symptom severity was assessed using the screening Identification and Stratification (IDS) framework and data available to the research team. The main outcome was TCC per member per month (PMPM) evaluated across the two-year period. ResultsAcross 2019 and 2020, 744,854 members met inclusion criteria. A total of 369,460 members were studied across both years. Greater depression symptom severity was associated with higher TCC across both years. Unchanged severity was associated with limited change in TCC from 2019 to 2020. Decrease in depression symptoms was associated with an average $41 reduction in PMPM spend, whereas increase in depression symptom severity was associated with an average $608 increase. LimitationsLimitations include fragmented data, retrospective design that limits causality, and the IDS framework design. ConclusionChanges in depression symptom severity were significantly associated with changes in TCC. Findings reveal financial and clinical opportunities associated with early identification and targeted management of depression.

Remaining useful life prediction of lithium-ion batteries based on autoregression with exogenous variables model

The gradual decrease capacity serves as a pivotal health indicator, reflecting the condition of lithium-ion batteries. Accurate forecasting of capacity can ascertain the remaining lifespan of these batteries at any given cycle, which is crucial for managing batteries in electric vehicles. This paper proposes an Autoregression with Exogenous Variables (AREV) model, which continually updates itself through a sliding window, offering predictions of battery state of health and remaining useful life, which extends battery prognostics at a fixed operating condition to different operating conditions. In addition, unlike most models that require multiple battery data of the same type for training, the proposed model only requires the use of fragmented data of the target battery with length around 30-50 cycles for capacity prediction and determines battery life based on battery failure thresholds. The above two points enable this model to be updated online without the need for any offline training. Finally, four different types of battery dataset , with different chemical substances and different charge and discharge conditions (especially dataset that follows random walk discharging profile to stimulate the real power consumption process) , are applied to verify the effectiveness and robustness of proposed RUL prediction approach. It shows that the proposed model can accurately predicting future capacity values. Timely warning signals can be issued before the end of life of battery, thereby ensuring the safe driving of electric vehicles and timely battery replacement.

Unlocking inherent values of manufacturing metadata through digital characteristics (DC) alignment

Data form the backbone of manufacturing sciences, initiating a revolutionary transformation in our understanding of manufacturing processes by unravelling complex scientific patterns embedded within them. Digital characteristics (DC) is defined as a strategic framework mapping the manufacturing metadata and integrates essential information across the entire spectrum spanning from the design, manufacturing, and application phases of manufactured products. By carrying these inherent distinctive features, DC serves as the ‘DNA’ for every manufacturing process. Through enormous experimental and simulation efforts, a digital characteristics space (DCS) was established to provide access to the up-to-date and information-rich DC repository containing over 140 manufacturing processes. In digital manufacturing, sensing networks play a pivotal role in metadata acquisition, contributing nearly 2000 petabytes of metadata annually. However, an overwhelming majority-nearly 100 %-of the data collected through sensing networks can be categorised as ‘fragmental data’, encompassing only a few (e.g., 1–2) essential pieces of information. Moreover, the current absence of efficient metadata identification methods presents an emerging and critical need to enable industry to unlock the full potential of manufacturing metadata. To this end, the authors of the present paper developed a physics-based alignment filter, considering DCS as an alignment reference similar to the ‘GenBank’. Specifically, the origins of naturally unattributed fragmental data were identified with an overall probability exceeding 82 % with a minimum length of 10 data points. The probability increased to 99 % when aligning the fragmental data with length of 100 data points. This was realised by comparing the thermo-mechanical DC of fragmental data with their counterparts stored in the DCS. Subsequently, we analysed the distinct DC of this identified manufacturing process to facilitate digitally-enhanced research. This study introduces a pioneering methodology developed to extract latent values embedded in manufacturing metadata derived from unattributed fragmental data. By revolutionising insights into advanced manufacturing sciences, our work provides an enabling approach for identifying and leveraging fragmental data sourced from sensing networks. This empowers the exploration of manufacturing metadata, promising transformative implications for the field.

Developing a Sustainable Shared Child's Health Record in Low Resources Setting, Lao PDR.

Lao PDR's Ministry of Health has problem with fragmented data, lack of standardization, and inability to interoperate hinder child health data collection. Electronic health information exchange (HIE) could improve data quality and shared child health records (SCHR). In this study we developed the SCHR by interoperating with existing healthcare systems. The FHIR IPS standards were customized based on data elements. 14 experts from the technical working group (TWG) had determined the minimum dataset for data transactions and system testing was analyzed on the HAPI FHIR server. Used FHIR IPS standards for interoperability within Client Registry (CR-DHIS2), Electronic Immunization Record (EIR-DHIS2) and Electronic Medical Record (EMR) works well for data transactions. The initial result of the SCHR could be able to provide a real target child and to monitor full vaccination. However, prior to the actual deployment of the system, it is necessary to validate it and guarantee the confidentiality and safety of the data.

Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility.

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Predicting 28Si projectile fragmentation cross sections with Bayesian neural network method

Abstract This study utilizes the Bayesian neural network (BNN) method in machine learning to learn and predict the cross-section data of 28Si projectile fragmentation for different targets at different energies, and to quantify the uncertainty. The detailed modeling process of BNN is presented, and its prediction results are compared with Cummings, Nilsen, EPAX2, EPAX3, and FRACS models, as well as experimental measurement values. The results reveal that the BNN method achieves the smallest root-mean-square error (RMSE) and the highest agreement with the experimental values compared to other models. Only the BNN method and FRACS model show a significant odd-even staggering effect, but BNN method is more closer to the experimental value. Furthermore, the BNN method is the only model capable of reproducing data features with low cross-section values at Z = 9, and the average ratio of the BNN's predicted to experimental values is close to 1.0. These results demonstrate that the BNN method can accurately reproduce and predict the fragment production cross sections of the 28Si projectile fragmentation, as well as its ability to capture key data characteristics.

Assessing the integration of refugee health data into national health information systems in Jordan, Lebanon, and Uganda

BackgroundWith the increasing number of protracted refugee crises globally, it is essential to ensure strong national health information systems (HIS) in displacement settings that include refugee-sensitive data and disaggregation by refugee status. This multi-country study aims to assess the degree of integration of refugee health data into national HIS in Jordan, Lebanon, and Uganda and identify the strengths and weaknesses of their national HIS in terms of collecting and reporting on refugee-related health indicators.MethodsThe study employs a comparative country analysis approach using a three-phase framework. The first phase involved reviewing 4120 indicators compiled from global health organizations, followed by a multi-stage refinement process, resulting in 45 indicators distributed across five themes. The second phase consisted of selecting relevant criteria from the literature, including data sources, annual reporting, disaggregation by refugee status, refugee population adjustments, accuracy, and consistency. The third phase involved assessing data availability and quality of the selected indicators against these criteria.ResultsOur analysis uncovered significant challenges in assessing the health status of refugees in Jordan, Lebanon, and Uganda, primarily stemming from limitations in the available health data and indicators. Specifically, we identified significant issues including incomplete local data collection with reliance on international data sources, fragmented data collection from various entities leading to discrepancies, and a lack of distinction between refugees and host populations in most indicators. These limitations hinder accurate comparisons and analyses. In light of these findings, a set of actionable recommendations was proposed to guide policymakers in the three countries to improve the integration of refugee health data into their national HIS ultimately enhancing refugees’ well-being and access to healthcare services.ConclusionThe current status of refugee-related health data in Jordan, Lebanon, and Uganda indicates the need for improved data collection and reporting practices, disaggregation by refugee status and better integration of refugee health data into national HIS to capture the health status and needs of refugees in host countries. Key improvement strategies include establishing a centralized authority for consistent and efficient data management, fostering transparent and inclusive data governance, and strengthening workforce capacity to manage refugee health data effectively.

Childhood Tumors around the Knee Revisited: Predilection Sites for Most Entities Confirmed.

Background: The diagnostic work-up of musculoskeletal tumors is a multifactorial process. During the early phase, differential diagnoses are made using basic radiological imaging. In this phase, part of the decision making is based on the patient's age, as well as the incidence and predilection sites of different entities. Unfortunately, this information is based on older and fragmented data. In this study, we retrospectively evaluated all soft-tissue and bone tumors around the knee in children treated at our tertiary center in the last 20 years, with the aim of verifying the data used today. Methods: In this retrospective study, the databank of our tertiary center was used to give an overview of treated tumors around the knee in children. Results: We were able to include 224 children with bone and soft-tissue tumors around the knee. The cohort consisted of 184 bone tumors, of which 144 were benign and 40 malignant. The 40 soft-tissue tumors comprised 30 benign and 10 malignant masses. The most common lesions were osteochondromas (88) in the bone and tenosynovial giant-cell tumors (12) in the soft tissue. Conclusions: With this original work, we were able to verify and supplement earlier studies, as well as deepen our insight into these very rare diseases.

Exploring Ion Mobility Mass Spectrometry Data File Conversions to Leverage Existing Tools and Enable New Workflows.

Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Postacquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: (1) mapping the IM axis to the LC axis for IM-MS data, (2) converting the drift time vs m/z space to CCS/z vs m/z space, and (3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. These new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.

Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study.

The integrity and reliability of clinical research outcomes rely heavily on access to vast amounts of data. However, the fragmented distribution of these data across multiple institutions, along with ethical and regulatory barriers, presents significant challenges to accessing relevant data. While federated learning offers a promising solution to leverage insights from fragmented data sets, its adoption faces hurdles due to implementation complexities, scalability issues, and inclusivity challenges. This paper introduces Federated Learning for Everyone (FL4E), an accessible framework facilitating multistakeholder collaboration in clinical research. It focuses on simplifying federated learning through an innovative ecosystem-based approach. The "degree of federation" is a fundamental concept of FL4E, allowing for flexible integration of federated and centralized learning models. This feature provides a customizable solution by enabling users to choose the level of data decentralization based on specific health care settings or project needs, making federated learning more adaptable and efficient. By using an ecosystem-based collaborative learning strategy, FL4E encourages a comprehensive platform for managing real-world data, enhancing collaboration and knowledge sharing among its stakeholders. Evaluating FL4E's effectiveness using real-world health care data sets has highlighted its ecosystem-oriented and inclusive design. By applying hybrid models to 2 distinct analytical tasks-classification and survival analysis-within real-world settings, we have effectively measured the "degree of federation" across various contexts. These evaluations show that FL4E's hybrid models not only match the performance of fully federated models but also avoid the substantial overhead usually linked with these models. Achieving this balance greatly enhances collaborative initiatives and broadens the scope of analytical possibilities within the ecosystem. FL4E represents a significant step forward in collaborative clinical research by merging the benefits of centralized and federated learning. Its modular ecosystem-based design and the "degree of federation" feature make it an inclusive, customizable framework suitable for a wide array of clinical research scenarios, promising to revolutionize the field through improved collaboration and data use. Detailed implementation and analyses are available on the associated GitHub repository.