Accurate dementia risk prediction is challenging, and may be facilitated by better use of imaging and genetic data, including their complex interactions. We explored using deep survival neural networks to integrate these multi-modal, high-dimensional data. We included 3521 Rotterdam Study participants, 6340 magnetic resonance imaging (MRI) scans, with follow-up clinical diagnosis for dementia, and used 504 samples from Alzheimer's Disease Neuroimaging Initiative (ADNI) as an external validation. Genetic data included APOE-ε4 status and 76 additional SNPs. We developed models combining Convolutional Neural Networks (CNN) and Cox Proportional Hazards (CPH) models and provided post-hoc explanations. Our models outperformed CPH models including age, sex, and genetic inputs in both Rotterdam Study and ADNI by C-index of 0.88/0.63 V.S. 0.85/0.58, p-value of 0.02/0.002. Although their performance did not surpass CPH models also included MRI markers (0.89/0.66), additional predictability was obtained in age-stratified prediction in ADNI. Incorporating CNN image features in CPH models further increased performance to highest C-index of 0.90/0.69. Age and image had the highest importance in prediction, with age, image and genetic features showing the strongest interactions. Our approach indicates that imaging and genetic data can be feasibly integrated for dementia risk prediction, with informative extraction, reliable explanations and potential predictive gains.

Background: Cognitive impairment is common in patients with heart failure (HF) and impacts patients' life. Sex differences in HF-characteristics are well-established. We hypothesized that women and men with HF also differ in cognitive functioning and that this may be related to sex differences in HF-characteristics and vascular brain injury. Methods: In the Heart-Brain Connection Study, 162 clinically stable HF patients (mean age 69.7 +/- 10.0, 33 % women) underwent neuropsychological assessments and brain-MRI. Test results were standardized into z-scores for memory, language, attention/speed, executive functioning, and global cognition. Using linear models adjusted for age and education, we calculated sex differences (women-to-men: W-M Delta) in cognitive functioning and examined effects of HF- and vascular brain injury-characteristics on these differences. Results: Men more often had an ischemic cause of HF and lower NYHA-classes, whereas women more often had preserved left ventricular ejection fractions (LVEF). Women had a higher volume of white matter hyperintensities (WMHs) whereas non-lacunar infarcts and microbleeds were more prevalent in men. Women performed better on global cognition than men (W-M Delta in z-score 0.20, 95 %CI 0.03-0.37), predominantly on memory (0.40, 0.02-0.78). These differences were associated with ischemic HF-etiology, as adjustment attenuated these sex differences. After adjustment for non-lacunar infarcts, global cognition difference persisted, but the difference in memory functioning attenuated. Adjustments for NYHA-class, LVEF, WMHs, and microbleeds did not change the results. Conclusion: Women and men with HF differ in cognitive functioning, predominantly in memory functioning, these differences were related to some sex differences in HF-characteristics and vascular brain injury, but not to all.

Coronary artery calcium (CAC) scores are a crucial biomarker identifying asymptomatic individuals at high risk for cardiovascular disease. CAC scores using gated coronary computed tomography (CT) have been assessed by semi-automatic methods requiring human experts’ contribution. We present an artificial intelligence (AI)-based automatic CAC scoring system employing image classification models after whole heart segmentation. The proposed method uses a deep learning (DL) model to detect and discard non-coronary artery calcification among all candidate calcium lesions. We developed and evaluated the DL model using a set of 435 scans publicly available for research. The model was trained on 348 scans and tested on 87 scans. The proposed method was validated internally on the test set and externally on an independent set of 1453 scans from a prospective cohort study. Computed scores by our method were compared to the manual reference standard. In our internal validation, computed scores strongly correlated with the reference scores; Pearson’s r = 0.953 (95 ρ = 0.991 (95 κ = 0.949 (95 ρ = 0.917 (95 κ = 0.774 (95

Deep learning methods based on Convolutional Neural Networks (CNNs) have shown large potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data. However, these methods have yet to be widely adopted in clinical practice, possibly due to the limited interpretability of deep learning models. The Explainable Boosting Machine (EBM) is a glass-box model but cannot learn features directly from input imaging data. In this study, we propose a novel interpretable model that combines CNNs and EBMs for the diagnosis and prediction of AD. We develop an innovative training strategy that alternatingly trains the CNN component as a feature extractor and the EBM component as the output block to form an end-to-end model. The model takes imaging data as input and provides both predictions and interpretable feature importance measures. We validated the proposed model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and the Health-RI Parelsnoer Neurode- generative Diseases Biobank (PND) as an external testing set. The proposed model achieved an area-under-the-curve (AUC) of 0.956 for AD and control classification, and 0.694 for the prediction of conversion of mild cognitive impairment (MCI) to AD on the ADNI cohort. The proposed model is a glass- box model that achieves a comparable performance with other state-of-the-art black-box models. Our code is available at: https://anonymous.4open.science/r/GL-ICNN.

While foundation models (FMs) offer strong potential for AI-based dementia diagnosis, their integration into federated learning (FL) systems remains underexplored. In this benchmarking study, we systematically evaluate the impact of key design choices: classification head architecture, fine-tuning strategy, and aggregation method, on the performance and efficiency of federated FM tuning using brain MRI data. Using a large multi-cohort dataset, we find that the architecture of the classification head substantially influences performance, freezing the FM encoder achieves comparable results to full fine-tuning, and advanced aggregation methods outperform standard federated averaging. Our results offer practical insights for deploying FMs in decentralized clinical settings and highlight trade-offs that should guide future method development.

Aggregation of cohort data increases precision for studying neurodegenerative disease pathways, but efforts to combine data and expertise are often hampered by infrastructural, ethical and legal considerations. We aimed to unite various cohort studies in the Netherlands to enhance research infrastructure and facilitate research on dementia etiology and its public health implications. The Netherlands Consortium of Dementia Cohorts (NCDC) includes participants with initially no established cognitive impairment from 9 Dutch cohorts: the Amsterdam Dementia Cohort (ADC), Doetinchem Cohort Study (DCS), European Medical Information Framework for Alzheimer’s Disease (EMIF-AD), Longitudinal Aging Study Amsterdam (LASA), the Leiden Longevity Study (LLS), The Maastricht Study, the Memolife substudy of the Lifelines cohort, Rotterdam Study and Second Manifestations of ARTerial disease-Magnetic Resonance (SMART-MR) study. The objectives of NCDC are to improve data infrastructure and access to cohorts related to aging and dementia, investigate the role of Alzheimer’s disease and vascular pathology in the development of dementia and estimate the public health impact of established dementia risk factors by assessing their relative contribution to the population burden of dementia. We increased the findability, accessibility, interoperability and reusability (FAIR) status of the cohorts through harmonization of data across cohorts, implementation of medical imaging repositories for scan management, implementation of the Personal Health Train infrastructure and provision of meta-data in existing cohort catalogues. We established the ethical and legal frameworks required for federated and pooled analyses and performed the first remote federated data analyses using the Personal Health Train infrastructure. To determine biomarkers of Alzheimer’s disease, endothelial dysfunction and inflammation, 2554 plasma samples were analyzed centrally. Federated, pooled, and coordinated meta-analyses have led to multiple publications in the context of NCDC. The combination of population-based and clinical cohorts, the coordinated assessment of plasma markers in previously collected samples and implementation and use of the Personal Health Train infrastructure for federated analysis are both feasible and promising for future collaborative efforts.

BackgroundCarotid occlusive disease is a risk factor for cognitive decline. A possible underlying etiology is that hemodynamic impairment results in decreased cerebral perfusion, exacerbated amyloid-β accumulation (Aβ) and poorer cognitive performance.ObjectiveWe aimed to determine whether patients with unilateral internal carotid artery (ICA) occlusion have less cerebral perfusion and more Aβ in the ipsilateral than in the contralateral hemisphere, and whether perfusion and Aβ are associated with cognitive functioning.MethodsWe included 20 patients (age 67.2 ± 7.0 years, 8 females, MMSE 29 [27-29]) with unilateral ICA occlusion, which underwent neuropsychological assessment and dynamic 18F-Florbetaben positron emission tomography (PET). Global and regional relative perfusion (R1) and binding potential (BPND) were obtained from the PET-images using a simplified reference tissue model. We performed Wilcoxon signed-rank tests to examine differences between hemispheres within subjects and linear regression to investigate associations with cognitive functioning.ResultsMedian global R1 was 0.911 (0.883-0.950) and global BPND was 0.172 (0.129-0.187). R1 was lower in the hemisphere ipsilateral to the ICA occlusion than in the contralateral hemisphere (0.899 [0.876-0.921] versus 0.935 [0.889-0.970]). BPND did not differ significantly between hemispheres (ipsilateral 0.172 [0.124-0.181] versus contralateral 0.168 [0.137-0.191]). Neither cerebral perfusion nor Aβ burden were associated with cognitive functioning.ConclusionsPatients with unilateral ICA occlusion did not have more Aβ in the ipsilateral hemisphere than in the contralateral hemisphere despite ipsilateral hypoperfusion. Perfusion and Aβ were unrelated to cognitive functioning. This indicates that cognitive impairment in patients with ICA occlusion is not due to exacerbated Aβ accumulation.

Artificial intelligence (AI) is a powerful technology with the potential to disrupt cancer detection, diagnosis and treatment. However, the development of new AI algorithms requires access to large and complex real-world datasets. Although such datasets are constantly being generated, access to them is limited by data fragmentation across numerous repositories and sites, heterogeneity, lack of annotations, and potential privacy issues. The European Cancer Imaging Initiative is a flagship of Europe's Beating Cancer Plan, aiming to unlock the power of AI for cancer patients, clinicians, and researchers by establishing a federated European infrastructure for cancer images through the EU-funded EUropean Federation for CAncer IMages (EUCAIM) project. This infrastructure, called Cancer Image Europe, builds on the AI for Health Imaging network (AI4HI), established European Research Infrastructures (Euro-BioImaging, BBMRI-ERIC, EATRIS, ECRIN, and ELIXIR), and numerous related partners providing access to research tools, images, and related clinical, pathology and molecular data. The infrastructure targets clinicians, researchers, and innovators by providing the means to develop and validate data-intensive AI-based and other IT-enabled clinical decision-making systems supporting precision medicine. Common data models, including a linking hyperontology, quality standards, compliance with the FAIR (Findability, Accessibility, Interoperability and Reusability) principles, data annotation, curation and anonymization services are provided to ensure data quality and interoperability, consistency and privacy. In summer 2024, the EUCAIM project released the first prototype of an EU-wide infrastructure, with a comprehensive dashboard integrating applications for dataset discovery, federated search, data access request, metadata harvesting, annotation, secure processing environments and federated processing. CRITICAL RELEVANCE STATEMENT: EUCAIM's federated infrastructure for cancer image data advances medical research and related AI development in Europe. It addresses the current fragmentation and heterogeneity of data repositories is legally compliant, and facilitates collaboration among clinicians, researchers, and innovators. KEY POINTS: AI solutions to advance cancer care rely on large, high-quality real-world datasets. EUCAIM's federated infrastructure for cancer image data empowers cancer research in Europe. It provides access to research tools, images, and related clinical, pathology and molecular data.

Background:Approximately one-third of patients with symptomatic severe aortic valve stenosis scheduled for transcatheter aortic valve implantation (TAVI) have some degree of cognitive impairment. The effect of TAVI on cardiac output, cerebral blood flow (CBF), and cognitive functioning has not been systematically studied. Methods:CAPITA (NCT05481008) is a prospective longitudinal study assessing cerebral and cognitive outcomes in patients that underwent TAVI between August 2020 and October 2022. At baseline (<24 h before TAVI) and three-month follow-up, patients underwent echocardiography, brain magnetic resonance imaging (MRI), and multidomain neuropsychological assessment. Primary outcome measures were change in CBF (Δml/100 g/min on arterial spin labelling MRI) and change in global cognitive functioning (Δz-scores). Secondary outcomes included cardiac output (L/min), and white matter hyperintensities (mL, number). Differences were tested with paired t-test and associations were tested with linear mixed models. Findings:A total of 148 patients (80.5 ± 5.7 years, 43% female) underwent TAVI. Three months after TAVI, cardiac output increased from 5.9 ± 1.4 L/min to 6.3 ± 1.4 L/min (mean difference 0.37, 95% CI 0.12-0.62, p = 0.004). CBF increased from 52.2 ± 14.5 mL/100 g/min to 55.9 ± 17.7 mL/100 g/min (mean difference 3.8, 95% CI 1.15-6.36, p = 0.005). Global cognitive functioning also increased from 0.02 ± 0.52 to 0.15 ± 0.49 (mean difference 0.13, 95% CI 0.06-0.20, p < 0.001) with most prominent increase in patients with worst baseline cognitive functioning. Patients with cognitive decline (22%), had a higher volume of new in white matter hyperintensities than patients with stable or improved cognition (78%): 1.26 ± 2.96, vs 0.29 ± 0.45, vs 0.31 ± 0.91 mL (p = 0.06). Interpretation:In patients with severe symptomatic aortic valve stenosis undergoing TAVI, cardiac output, CBF, and cognitive functioning improved after three months. Funding:The Heart-Brain Connection crossroad consortium of the Dutch Cardiovascular Alliance. The Netherlands CardioVascular Research Initiative: Dutch Heart Foundation (CVON 2018-28 & 2012-06 Heart Brain Connection).

Aerobic exercise may improve cerebral perfusion and may thereby attenuate, or delay, cognitive decline. Excersion-VCI aimed to evaluate the effect of aerobic exercise on cerebral perfusion in patients with vascular cognitive impairment (VCI).This Randomized Controlled Trial included non-demented adults ≥50 years diagnosed with VCI. Patients were randomly assigned either home-based aerobic interval training (exercise group) or information sessions (control group). Primary outcome was change in Arterial Spin Labelling MRI grey matter cerebral perfusion from baseline to 14-week follow-up. Per-protocol analysis was performed in patients who completed the follow-up. Secondary outcomes were VO2max and cognitive function. Exploratory outcomes were depression and apathy, White Matter Hyperintensities, cerebral volumes, and blood biomarkers.Fifty-eight VCI patients (mean age 67.0 ± 6.7 years) were allocated to the exercise (n = 28) or control group (n = 30). Intention-to-treat analyses showed that change in grey matter cerebral perfusion in the exercise group did not differ from the control group (p = 0.38), nor were there group differences in change in VO2max (p = 0.17). The exercise group showed an increase in triglycerides compared to the control group (p = 0.04). No group differences were found for other outcomes. Per protocol analyses showed improvement in VO2max in the exercise group compared to the control group (p = 0.04).An aerobic exercise program in VCI patients improved cardiorespiratory fitness in those who adhered to the protocol, but did not show significant effects on grey matter cerebral perfusion or other outcomes. The intervention duration of 14-weeks may have been too short to measure changes in perfusion or improvements in cognitive function.

This study addresses the challenges of confounding effects and interpretability in artificial-intelligence-based medical image analysis. Whereas existing literature often resolves confounding by removing confounder-related information from latent representations, this strategy risks affecting image reconstruction quality in generative models, thus limiting their applicability in feature visualization. To tackle this, we propose a different strategy that retains confounder-related information in latent representations while finding an alternative confounder-free representation of the image data. Our approach views the latent space of an autoencoder as a vector space, where imaging-related variables, such as the learning target (t) and confounder (c), have a vector capturing their variability. The confounding problem is addressed by searching a confounder-free vector which is orthogonal to the confounder-related vector but maximally collinear to the target-related vector. To achieve this, we introduce a novel correlation-based loss that not only performs vector searching in the latent space, but also encourages the encoder to generate latent representations linearly correlated with the variables. Subsequently, we interpret the confounder-free representation by sampling and reconstructing images along the confounder-free vector. The efficacy and flexibility of our proposed method are demonstrated across three applications, accommodating multiple confounders and utilizing diverse image modalities. Results affirm the method's effectiveness in reducing confounder influences, preventing wrong or misleading associations, and offering a unique visual interpretation for in-depth investigations by clinical and epidemiological researchers. The code is released in the following GitLab repository: https://gitlab.com/radiology/compopbio/ai_based_association_analysis.

Most low-mass stars form in stellar clusters that also contain massive stars, which are sources of far-ultraviolet (FUV) radiation. Theoretical models predict that this FUV radiation produces photodissociation regions (PDRs) on the surfaces of protoplanetary disks around low-mass stars, which affects planet formation within the disks. We report James Webb Space Telescope and Atacama Large Millimeter Array observations of a FUV-irradiated protoplanetary disk in the Orion Nebula. Emission lines are detected from the PDR; modeling their kinematics and excitation allowed us to constrain the physical conditions within the gas. We quantified the mass-loss rate induced by the FUV irradiation and found that it is sufficient to remove gas from the disk in less than a million years. This is rapid enough to affect giant planet formation in the disk.

Context.Mid-infrared emission features are important probes of the properties of ionized gas and hot or warm molecular gas, which are difficult to probe at other wavelengths. The Orion Bar photodissociation region (PDR) is a bright, nearby, and frequently studied target containing large amounts of gas under these conditions. Under the “PDRs4All” Early Release Science Program for JWST, a part of the Orion Bar was observed with MIRI integral field unit (IFU) spectroscopy, and these high-sensitivity IR spectroscopic images of very high angular resolution (0.2″) provide a rich observational inventory of the mid-infrared (MIR) emission lines, while resolving the HIIregion, the ionization front, and multiple dissociation fronts.Aims.We list, identify, and measure the most prominent gas emission lines in the Orion Bar using the new MIRI IFU data. An initial analysis summarizes the physical conditions of the gas and demonstrates the potential of these new data and future IFU observations with JWST.Methods.The MIRI IFU mosaic spatially resolves the substructure of the PDR, its footprint cutting perpendicularly across the ionization front and three dissociation fronts. We performed an up-to-date data reduction, and extracted five spectra that represent the ionized, atomic, and molecular gas layers. We identified the observed lines through a comparison with theoretical line lists derived from atomic data and simulated PDR models. The identified species and transitions are summarized in the main table of this work, with measurements of the line intensities and central wavelengths.Results.We identified around 100 lines and report an additional 18 lines that remain unidentified. The majority consists of HIrecombination lines arising from the ionized gas layer bordering the PDR. The HIline ratios are well matched by emissivity coefficients from H recombination theory, but deviate by up to 10% because of contamination by HeIlines. We report the observed emission lines of various ionization stages of Ne, P, S, Cl, Ar, Fe, and Ni. We show how the NeIII/NeII, SIV/SIII, and ArIII/ArIIratios trace the conditions in the ionized layer bordering the PDR, while FeIII/FeIIand NiIII/NiIIexhibit a different behavior, as there are significant contributions to FeIIand NiIIfrom the neutral PDR gas. We observe the pure-rotational H2lines in the vibrational ground state from 0–0S(1) to 0–0S(8), and in the first vibrationally excited state from 1–1S(5) to 1–1 S(9). We derive H2excitation diagrams, and for the three observed dissociation fronts, the rotational excitation can be approximated with one thermal (~700 K) component representative of an average gas temperature, and one nonthermal component (~2700 K) probing the effect of UV pumping. We compare these results to an existing model of the Orion Bar PDR, and find that the predicted excitation matches the data qualitatively, while adjustments to the parameters of the PDR model are required to reproduce the intensity of the 0–0 S (6) to S (8) lines.

BACKGROUND:Cerebral small vessel disease (SVD) is manifested on magnetic resonance imaging (MRI) by white matter hyperintensities, lacunes, microbleeds, and atrophy. While these manifestations can be part of normal aging, a high burden has been associated with cognitive impairment and vascular events. Distinguishing between normal versus abnormal SVD lesion burden in clinical practice remains complex. Our objective is to establish age- and sex-specific normative data for MRI manifestations of SVD, to support clinical assessment in individual patients. METHODS:We used 11 465 MRI scans from 5402 participants of the Rotterdam Study, the Netherlands, an ongoing prospective population-based cohort since 1990, to develop percentile curves for white matter hyperintensities and brain parenchymal fraction and probability curves for the prevalence and count of lacunes and microbleeds, across ages 45 to 100 years, stratified by sex. RESULTS:Participants were primarily White (≈97%), with a mean age at first scan of 64.7 (range, 45.7-97.9) years, and 55.7% being female participants. For all SVD MRI manifestations, the curves demonstrated nonlinear trends, with accelerating burden with advancing age (eg, doubling of white matter hyperintensity fraction every 10 years). Regarding brain parenchymal fraction, a decline was seen earlier in male participants (≈45 years) than female participants (≈60 years) and was more pronounced in male participants over time. Female participants had slightly higher white matter hyperintensity fractions compared with male participants across all ages. Lacunes and microbleeds were more frequently found in male participants than in female participants, and microbleeds were more prevalent than lacunes. CONCLUSIONS:We provide comprehensive normative data for different MRI manifestations of SVD, presented as percentile and probability curves by age, stratified by sex. This can aid clinicians to actually quantify the SVD burden on an individual patient's MRI scans and detect patterns of abnormality.

Water is a key ingredient for the emergence of life as we know it. Yet, its destruction and reformation in space remains unprobed in warm gas. Here, we detect the hydroxyl radical (OH) emission from a planet-forming disk exposed to external far-ultraviolet (FUV) radiation with the James Webb Space Telescope. The observations are confronted with the results of quantum dynamical calculations. The highly excited OH infrared rotational lines are the tell-tale signs of H2O destruction by FUV. The OH infrared ro-vibrational lines are attributed to chemical excitation via the key reaction O+H=OH+H which seeds the formation of water in the gas-phase. We infer that the equivalent of the Earth ocean's worth of water is destroyed per month and replenished. These results show that under warm and irradiated conditions water is destroyed and efficiently reformed via gas-phase reactions. This process, assisted by diffusive transport, could reduce the HDO/H2O ratio in the warm regions of planet-forming disks.

Introduction: Vascular cognitive impairment (VCI) is heterogeneous in brain atrophy patterns, clinical symptoms, and possibly in the factors affecting resilience to symptoms. The objective of this study was to investigate the heterogeneity of VCI by estimating different atrophy-driven subtypes and use those for identifying resilience factors in each subtype. Methods: We used cross-sectional data from the Trace-VCI cohort comprising of memory-clinic patients with vascular brain injury on MRI (n=361 all-cause dementia, n=190 MCI, and n=188 SCD). White matter hyper-intensities (WMH) were segmented, and SLF toolbox was used to refill them on the MRIs for accurate parcellation. Freesurfer volumes were used for identifying subtypes with non-negative matrix factorization. Subtype-specific pseudo-timelines of progression were estimated using a previously validated discriminative event-based model. Severity of atrophy (SA) in patients was estimated using cross-validation based on their position on the pseudo-timeline. Using linear-regression, cognition and disability (MMSE, Global deterioration scale, disability assessment for dementia) were modelled to be dependent on SA and its interactions with genetic factors, vascular markers and risk-factors, and co-pathology independently (variables in Figure-3). Resilience factors were identified by testing if the model with the interaction explains the symptoms significantly more than the one without. Results: The algorithm identified three atrophy-based VCI subtypes: Frontal, Subcortical/Temporal, and Parietal subtype. Their prevalence, vascular and clinical presentations are summarized in Figure-1. The pseudo-timelines of atrophy are shown in Figure-2. SA's interaction with education positively influenced cognition in all subtypes, but negatively influenced disability in subcortical/temporal subtype. In frontal and parietal subtypes, APOE ε4, AD co-pathology resulted in more SA without worsening symptoms. SA's interaction with smoking and microbleeds negatively influenced disability. In the subcortical/temporal subtype, SA's interaction with WMH, lacunes, infarcts negatively impacted disability. In parietal subtype, men have more cognitive resilience than women. SA's interaction with hypercholesterolemia, and smoking were significantly negative for cognition. Lacunes were associated with more SA without affecting cognition. Figure-3 summarizes the interactions for all subtypes. Discussion: We identified three atrophy-based VCI subtypes where the risk-factors have different influence on atrophy and symptoms highlighting differences in resilience. These results could aid in prognosis and in personalizing patients’ intervention strategy.

BackgroundEstablishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner.MethodsIn this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms.ResultsWe successfully applied our ETL tool and observed a complete coverage of the cohorts’ data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data.ConclusionIn this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.

One of the main problems in astrochemistry is determining the amount of sulfur in volatiles and refractories in the interstellar medium. The detection of the main sulfur reservoirs (icy H$_2$S and atomic gas) has been challenging, and estimates are based on the reliability of models to account for the abundances of species containing less than 1% of the total sulfur. The high sensitivity of the James Webb Space Telescope provides an unprecedented opportunity to estimate the sulfur abundance through the observation of the [S I] 25.249 $\mu$m line. We used the [S III] 18.7 $\mu$m, [S IV] 10.5 $\mu$m, and [S l] 25.249 $\mu$m lines to estimate the amount of sulfur in the ionized and molecular gas along the Orion Bar. For the theoretical part, we used an upgraded version of the Meudon photodissociation region (PDR) code to model the observations. New inelastic collision rates of neutral atomic sulfur with ortho- and para- molecular hydrogen were calculated to predict the line intensities. The [S III] 18.7 $\mu$m and [S IV] 10.5 $\mu$m lines are detected over the imaged region with a shallow increase (by a factor of 4) toward the HII region. We estimate a moderate sulfur depletion, by a factor of $\sim$2, in the ionized gas. The corrugated interface between the molecular and atomic phases gives rise to several edge-on dissociation fronts we refer to as DF1, DF2, and DF3. The [S l] 25.249 $\mu$m line is only detected toward DF2 and DF3, the dissociation fronts located farthest from the HII region. The detailed modeling of DF3 using the Meudon PDR code shows that the emission of the [S l] 25.249 $\mu$m line is coming from warm ($>$ 40 K) molecular gas located at A$_{\rm V}$ $\sim$ 1$-$5 mag from the ionization front. Moreover, the intensity of the [S l] 25.249 $\mu$m line is only accounted for if we assume the presence of undepleted sulfur.

Mid-infrared emission features probe the properties of ionized gas, and hot or warm molecular gas. The Orion Bar is a frequently studied photodissociation region (PDR) containing large amounts of gas under these conditions, and was observed with the MIRI IFU aboard JWST as part of the "PDRs4All" program. The resulting IR spectroscopic images of high angular resolution (0.2") reveal a rich observational inventory of mid-IR emission lines, and spatially resolve the substructure of the PDR, with a mosaic cutting perpendicularly across the ionization front and three dissociation fronts. We extracted five spectra that represent the ionized, atomic, and molecular gas layers, and measured the most prominent gas emission lines. An initial analysis summarizes the physical conditions of the gas and the potential of these data. We identified around 100 lines, report an additional 18 lines that remain unidentified, and measured the line intensities and central wavelengths. The H I recombination lines originating from the ionized gas layer bordering the PDR, have intensity ratios that are well matched by emissivity coefficients from H recombination theory, but deviate up to 10% due contamination by He I lines. We report the observed emission lines of various ionization stages of Ne, P, S, Cl, Ar, Fe, and Ni, and show how certain line ratios vary between the five regions. We observe the pure-rotational H$_2$ lines in the vibrational ground state from 0-0 S(1) to 0-0 S(8), and in the first vibrationally excited state from 1-1 S(5) to 1-1 S(9). We derive H$_2$ excitation diagrams, and approximate the excitation with one thermal (~700 K) component representative of an average gas temperature, and one non-thermal component (~2700 K) probing the effect of UV pumping. We compare these results to an existing model for the Orion Bar PDR and highlight the differences with the observations.