Homogeneous Dataset Research Articles

The red giant branch tip in the SDSS, PS1, JWST, NGRST, and Euclid photometric systems

We used synthetic photometry from Gaia DR3 BP and RP spectra for a large selected sample of stars in the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC) to derive the magnitude of the red giant branch (RGB) tip for these two galaxies in several passbands across a range of widely used optical photometric systems, including those of space missions that have not yet started their operations. The RGB tip is estimated by fitting a well motivated model to the RGB luminosity function (LF) within a fully Bayesian framework, allowing for a proper representation of the uncertainties of all the involved parameters and their correlations. By adopting the best available distance and interstellar extinction estimates, we provide a calibration of the RGB tip as a standard candle for the following passbands: Johnson-Kron-Cousins I (mainly used for validation purposes), Hubble Space Telescope F814W, Sloan Digital Sky Survey i and z, PanSTARRS 1 y, James Webb Space Telescope F090W, Nancy Grace Roman Space Telescope Z087, and Euclid IE, with an accuracy within a few per cent, depending on the case. We used theoretical models to explore the trend of the absolute magnitude of the tip as a function of colour in the different passbands (beyond the range spanned by the LMC and SMC), as well as its dependency on age. These calibrations can be very helpful to obtain state-of-the-art RGB tip distance estimates to stellar systems in a very large range of distances directly from data in the natural photometric system of these surveys and/or missions, without recurring to photometric transformations. We have made the photometric catalogues publicly available for calibrations in additional passbands or for different approaches in the estimate of the tip, as well as for stellar populations and stellar astrophysics studies that may take advantage of large and homogeneous datasets of stars with magnitudes in 22 different passbands.

A survey on feature extraction and learning techniques for link prediction in homogeneous and heterogeneous complex networks

Complex networks are commonly observed in several real-world areas, such as social, biological, and technical systems, where they exhibit complicated patterns of connectedness and organised clusters. These networks have intricate topological characteristics that frequently elude conventional characterization. Link prediction in complex networks, like data flow in telecommunications networks, protein interactions in biological systems, and social media interactions on platforms like Facebook, etc., is an essential element of network analytics and presents fresh research challenges. Consequently, there is a growing emphasis in research on creating new link prediction methods for different network applications. This survey investigates several strategies related to link prediction, ranging from feature extraction based to feature learning based techniques, with a specific focus on their utilisation in dynamic and developing network topologies. Furthermore, this paper emphasises on a wide variety of feature learning techniques that go beyond basic feature extraction and matrix factorization. It includes advanced learning-based algorithms and neural network techniques specifically designed for link prediction. The study also presents evaluation results of different link prediction techniques on homogeneous and heterogeneous network datasets, and provides a thorough examination of existing methods and potential areas for further investigation.

Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization

Neural processes (NPs) are models for meta-learning which output uncertainty estimates. So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning. In particular, applications in scientific research may prove especially challenging due to the potential novelty of meta-testing tasks. Molecular property prediction is one such research area that is characterized by sparse datasets of many functions on a shared molecular space. In this paper, we study the application of graph NPs to molecular property prediction with DOCKSTRING, a diverse dataset of docking scores. Graph NPs show competitive performance in few-shot learning tasks relative to supervised learning baselines common in chemoinformatics, as well as alternative techniques for transfer learning and meta-learning. In order to increase meta-generalization to divergent test functions, we propose fine-tuning strategies that adapt the parameters of NPs. We find that adaptation can substantially increase NPs' regression performance while maintaining good calibration of uncertainty estimates. Finally, we present a Bayesian optimization experiment which showcases the potential advantages of NPs over Gaussian processes in iterative screening. Overall, our results suggest that NPs on molecular graphs hold great potential for molecular property prediction in the low-data setting.Scientific contributionNeural processes are a family of meta-learning algorithms which deal with data scarcity by transferring information across tasks and making probabilistic predictions. We evaluate their performance on regression and optimization molecular tasks using docking scores, finding them to outperform classical single-task and transfer-learning models. We examine the issue of generalization to divergent test tasks, which is a general concern of meta-learning algorithms in science, and propose strategies to alleviate it.

Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona

Understanding coronal structure and dynamics can be facilitated by analyzing green-line emission, which enables the investigation of diverse coronal structures such as coronal loops, streamers, coronal holes, and various eruptions in the solar atmosphere. In this study, we investigated the spatiotemporal behaviors of green-line emissions in both low and high latitudes across nine solar cycles, ranging from Solar Cycle 17 to the current Solar Cycle 25, using the modified homogeneous data set. We employed methodologies such as cross correlation, power spectral density, and wavelet transform techniques for this analysis. We found distinct behaviors in green-line energy across various latitudinal distributions in the solar atmosphere. The trends observed at higher latitudes differ from those at lower latitudes. The emission behaviors show a close association with other solar phenomena like solar flares, sunspots, and coronal mass ejections throughout the solar cycles. The observed variations exhibit harmonic periods. The emission activity is significantly higher in the low latitudes, accounting for over 70% of the emissions, while the higher latitudes contribute less than 30%. The emissions exhibit asymmetric behavior between the northern and southern hemispheres, leading to a 44 yr cycle of solar hemispheric dominance shifts. Various factors, such as Alfvén waves, solar magnetic fields, sunspots, differential rotation, and reconnection events, influence the observed differences in behavior between lower and higher latitudes, suggesting the existence of potential underlying phenomena contributing to deviations in properties, intensity, temporal dynamics, and spatiotemporal lifetime.

Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models

This paper presents a novel mathematical framework for assessing and predicting the resilience of critical coastal infrastructures against wave overtopping hazards and extreme climatic events. A probabilistic sensitivity analysis model is developed to evaluate the relative influence of hydrodynamic, geomorphological, and structural factors contributing to wave overtopping dynamics. Additionally, a stochastic Gaussian process (GP) model is introduced to predict the mean overtopping discharge from coastal defences. Both the sensitivity analysis and the predictive models are validated using a large homogeneous dataset comprising 163 laboratory and field-scale tests. Statistical evaluations demonstrate the superior performance of the GPs in identifying key parameters driving wave overtopping and predicting mean discharge rates, outperforming existing regression-based formulae. The proposed model offers a robust predictive tool for assessing the performance of critical coastal protection infrastructures under various climate scenarios.

Enhancing Graph Neural Networks via Memorized Global Information

Graph neural networks (GNNs) have gained significant attention for their impressive results on different graph-based tasks. The essential mechanism of GNNs is the message-passing framework, whereby node representations are aggregated from local neighborhoods. Recently, Transformer-based GNNs have been introduced to learn the long-range dependencies, enhancing performance. However, their quadratic computational complexity, due to the attention computation, has constrained their applicability on large-scale graphs. To address this issue, we propose MGIGNN ( M emorized G lobal I nformation G raph N eural N etwork), an innovative approach that leverages memorized global information to enhance existing GNNs in both transductive and inductive scenarios. Specifically, MGIGNN captures long-range dependencies by identifying and incorporating global similar nodes, which are defined as nodes exhibiting similar features, structural patterns and label information within a graph. To alleviate the computational overhead associated with computing embeddings for all nodes, we introduce an external memory module to facilitate the retrieval of embeddings and optimize performance on large graphs. To enhance the memory-efficiency, MGIGNN selectively retrieves global similar nodes from a small set of candidate nodes. These candidate nodes are selected from the training nodes based on a sparse node selection distribution with a Dirichlet prior. This selecting approach not only reduces the memory size required but also ensures efficient utilization of computational resources. Through comprehensive experiments conducted on ten widely-used and real-world datasets, including seven homogeneous datasets and three heterogeneous datasets, we demonstrate that our MGIGNN can generally improve the performance of existing GNNs on node classification tasks under both inductive and transductive settings.

Metallicity calibrations based on auroral lines from PHANGS-MUSE data

We present a chemical analysis of selected regions from the PHANGS-MUSE nebular catalogue. Our intent is to empirically re-calibrate strong-line diagnostics of gas-phase metallicity, applicable across a wide range of metallicities within nearby star-forming galaxies. To ensure reliable measurements of auroral line fluxes, we carried out a new spectral fitting procedure whereby only restricted wavelength regions around the emission lines of interest are taken into account: this assures a better fit for the stellar continuum. No prior cuts to nebulae luminosity were applied to limit biases in auroral line detections. Ionic abundances of O$^+$ $, N$^+$, S$^+$, and S$^ $ were estimated by applying the direct method. We integrated the selected PHANGS-MUSE sample with other existing auroral line catalogues, appropriately re-analysed to obtain a homogeneous dataset. This was used to derive strong-line diagnostic calibrations that span from 12+log(O/H) = 7.5 to 8.8. We investigate their dependence on the ionisation parameter and conclude that it is likely the primary cause of the significant scatter observed in these diagnostics. We apply our newly calibrated strong-line diagnostics to the total sample of regions from the PHANGS-MUSE nebular catalogue, and we exploit these indirect metallicity estimates to study the radial metallicity gradient within each of the 19 galaxies of the sample. We compare our results with the literature and find good agreement, validating our procedure and findings. With this paper, we release the full catalogue of auroral and nebular line fluxes for the selected regions from the PHANGS-MUSE nebular catalogue. This is the first catalogue of direct chemical abundance measurements carried out with PHANGS-MUSE data.

From Voxel to Gene: A Scoping Review on MRI Radiogenomics' Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas-The Promise of Virtual Biopsy?

Gliomas, including the most severe form known as glioblastomas, are primary brain tumors arising from glial cells, with significant impact on adults, particularly men aged 45 to 70. Recent advancements in the WHO (World Health Organization) classification now correlate genetic markers with glioma phenotypes, enhancing diagnostic precision and therapeutic strategies. This scoping review aims to evaluate the current state of deep learning (DL) applications in the genetic characterization of adult gliomas, addressing the potential of these technologies for a reliable virtual biopsy. We reviewed 17 studies, analyzing the evolution of DL algorithms from fully convolutional networks to more advanced architectures (ResNet and DenseNet). The methods involved various validation techniques, including k-fold cross-validation and external dataset validation. Our findings highlight significant variability in reported performance, largely due to small, homogeneous datasets and inconsistent validation methods. Despite promising results, particularly in predicting individual genetic traits, the lack of robust external validation limits the generalizability of these models. Future efforts should focus on developing larger, more diverse datasets and integrating multidisciplinary collaboration to enhance model reliability. This review underscores the potential of DL in advancing glioma characterization, paving the way for more precise, non-invasive diagnostic tools. The development of a robust algorithm capable of predicting the somatic genetics of gliomas or glioblastomas could accelerate the diagnostic process and inform therapeutic decisions more quickly, while maintaining the same level of accuracy as the traditional diagnostic pathway, which involves invasive tumor biopsies.

Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care

BackgroundEarly detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence (AI) has proven to be helpful for enhancing melanoma detection. However, there are few prospective studies confirming these promising results. Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion of rare melanoma subtypes, preventing a fair and thorough evaluation of AI and its generalizability, a crucial aspect for its application in the clinical setting.MethodsTherefore, we assessed “All Data are Ext” (ADAE), an established open-source ensemble algorithm for detecting melanomas, by comparing its diagnostic accuracy to that of dermatologists on a prospectively collected, external, heterogeneous test set comprising eight distinct hospitals, four different camera setups, rare melanoma subtypes, and special anatomical sites. We advanced the algorithm with real test-time augmentation (R-TTA, i.e., providing real photographs of lesions taken from multiple angles and averaging the predictions), and evaluated its generalization capabilities.ResultsOverall, the AI shows higher balanced accuracy than dermatologists (0.798, 95% confidence interval (CI) 0.779–0.814 vs. 0.781, 95% CI 0.760–0.802; p = 4.0e−145), obtaining a higher sensitivity (0.921, 95% CI 0.900–0.942 vs. 0.734, 95% CI 0.701–0.770; p = 3.3e−165) at the cost of a lower specificity (0.673, 95% CI 0.641–0.702 vs. 0.828, 95% CI 0.804–0.852; p = 3.3e−165).ConclusionAs the algorithm exhibits a significant performance advantage on our heterogeneous dataset exclusively comprising melanoma-suspicious lesions, AI may offer the potential to support dermatologists, particularly in diagnosing challenging cases.

Comparing LUCAS Soil and national systems: Towards a harmonized European Soil monitoring network

A recent assessment states that 60–70% of soils in Europe are considered degraded. Protecting such valuable resource require knowledge on soil status through monitoring systems. In Europe, different types of monitoring networks currently exist in parallel. Many EU Member states (MS) developed their own national soil information monitoring system (N-SIMS), some being in place for decades. In parallel in 2009, the European Commission extended the periodic Land Use/Land Cover Area Frame Survey (LUCAS) led by EUROSTAT to sample and analyse the main properties of topsoil in EU in order to develop a homogeneous dataset for EU.Both sources of information are needed to support European policies on soil health evaluation. However, a question remains whether the assessment obtained by using soil properties from both monitoring programs (N-SIMS and LUCAS Soil) are comparable, and what could be the limitations of using either one dataset or the other.Conducted in the context of European Joint Programme (EJP) SOIL, this study shows the results of a comparison between N-SIMS and LUCAS Soil programs among 12 different EU member states including BE, DE, DK, EE, ES, FR, DE, HU, IT, NL, PL, SE and SK. The comparison was done on: (i) the sampling strategies including site densities, land cover and soil type distribution; (ii) the statistical distribution of three soil properties (organic carbon, pH and clay content); (iii) two potential indicators of soil quality (i.e. OC/Clay ratio and pH classes). The results underlined substantial differences in soil properties statistical distributions between N-SIMS and LUCAS Soil in many member states, particularly for woodland and grassland soils, affecting the evaluation of soil health using indicators. Such differences might be explained by both the monitoring strategy and sampling or analytical protocols exposing the potential effect of data source on European and national policies. The results demonstrate the need to work towards data harmonization and in the light of the Soil Monitoring Law, to carefully design the future of soil monitoring in Europe taking into account both LUCAS Soil and N-SIMS considering the significant impact of the monitoring strategies and protocols on soil health indicators.

Development of a CNN for Adult Brain Tumour Characterisation: Implications and Future Directions for Transfer Learning.

Brain tumours are the most commonly occurring solid tumours in children, albeit with lower incidence rates compared to adults. However, their inherent heterogeneity, ethical considerations regarding paediatric patients, and difficulty in long-term follow-up make it challenging to gather large homogenous datasets for analysis. This study focuses on the development of a Convolutional Neural Network (CNN) for brain tumour characterisation using the adult BraTS 2020 dataset. We propose to transfer knowledge, from models pre-trained on extensive adult brain tumour datasets to smaller cohort datasets (e.g., paediatric brain tumours) in future studies, by leveraging Transfer Learning (TL). This approach aims to extract relevant features from pre-trained models, addressing the limited availability of annotated paediatric datasets and enhancing tumour characterisation in children. The implications and potential applications of this methodology in paediatric neuro-oncology are discussed.

Full Rotational Dynamics of Plastic Microfibers in Turbulence.

The motion, settling, and dispersion of microplastics in the ocean are determined by their rotational dynamics. We present experiments on elongated, large aspect ratio, and mildly curved plastic fibers slightly longer than the Kolmogorov length scale. Exploiting their uniquely identifiable three-dimensional orientation, we perform original optical Lagrangian investigations and provide a set of homogeneous data on their rotation rates around their longitudinal axis: spinning rate, and transversal axes: tumbling rates, which we explain in the context of the general features of turbulence.

Unrealistic Data Augmentation Improves the Robustness of Deep Learning-Based Classification of Dopamine Transporter SPECT Against Variability Between Sites and Between Cameras.

We propose strongly unrealistic data augmentation to improve the robustness of convolutional neural networks (CNNs) for automatic classification of dopamine transporter SPECT against the variability between sites and between cameras. Methods: A CNN was trained on a homogeneous dataset comprising 1,100 123I-labeled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)nortropane SPECT images using strongly unrealistic data augmentation based on gaussian blurring and additive noise. Strongly unrealistic data augmentation was compared with no augmentation and intensity-based nnU-Net augmentation on 2 independent datasets with lower (n = 645) and considerably higher (n = 640) spatial resolution. Results: The CNN trained with strongly unrealistic augmentation achieved an overall accuracy of 0.989 (95% CI, 0.978-0.996) and 0.975 (95% CI, 0.960-0.986) in the independent test datasets, which was better than that without (0.960, 95% CI, 0.942-0.974; 0.953, 95% CI, 0.934-0.968) and with nnU-Net augmentation (0.972, 95% CI, 0.956-0.983; 0.950, 95% CI, 0.930-0.966) (all McNemar P < 0.001). Conclusion: Strongly unrealistic data augmentation results in better generalization of CNN-based classification of 123I-labeled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)nortropane SPECT images to unseen acquisition settings. We hypothesize that this can be transferred to other nuclear imaging applications.

A Comparative Analysis of Machine Learning Algorithms for Identifying Cultural and Technological Groups in Archaeological Datasets through Clustering Analysis of Homogeneous Data

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions.

Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.

Background: Prostate Cancer (PCa) is asymptomatic at an early stage and often painless, requiring only active surveillance. External Beam Radiotherapy (EBRT) is currently a curative option for localised and locally advanced diseases and a palliative option for metastatic low-volume disease. Although highly effective, especially in a hypofractionation scheme, 17.4% to 39.4% of all patients suffer from cancer recurrence after EBRT. But, radiographic findings also correlate with significant differences in protein expression patterns. In the PCa EBRT workflow, several imaging modalities are available for grading, staging and contouring. Using image data characterisation algorithms (radiomics), one can provide a quantitative analysis of prognostic and predictive treatment outcomes. Methods: This literature review searched for original studies in radiomics for PCa in the context of EBRT. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes 73 new studies and analyses datasets, imaging modality, segmentation technique, feature extraction, selection and model building methods. Results: Magnetic Resonance Imaging (MRI) is the preferred imaging modality for radiomic studies in PCa but Computed Tomography (CT), Positron Emission Tomography (PET) and Ultrasound (US) may offer valuable insights on tumour characterisation and treatment response prediction. Conclusions: Most radiomic studies used small, homogeneous and private datasets lacking external validation and variability. Future research should focus on collaborative efforts to create large, multicentric datasets and develop standardised methodologies, ensuring the full potential of radiomics in clinical practice.

BRIDGING DATA GAPS: A FEDERATED LEARNING APPROACH TO HEAT EMISSION PREDICTION IN LASER POWDER BED FUSION

Abstract Deep learning has impacted defect prediction in Additive Manufacturing (AM), which is important to ensure process stability and part quality. However, its success depends on extensive training, requiring large, homogenous datasets–remaining a challenge for the AM industry, particularly for small- and medium-sized enterprises (SMEs). The unique and varied characteristics of AM parts, along with the limited resources of SMEs, hampers data collection, posing difficulties in the independent training of deep learning models. Addressing these concerns requires enabling knowledge sharing from the similarities in the physics of the AM process and defect formation mechanisms while carefully handling privacy concerns. Federated learning (FL) offers a solution to allow collaborative model training across multiple entities without sharing local data. This paper introduces an FL framework to predict section-wise heat emission during Laser Powder Bed Fusion (LPBF), a vital process signature. It incorporates a customized Long Short-Term Memory (LSTM) model for each client, capturing the dynamic AM process's time series properties without sharing sensitive information. Three advanced FL algorithms are integrated–FedAvg, FedProx, and FedAvgM–to aggregate model weights rather than raw datasets. Experiments demonstrate that the FL framework ensures convergence and maintains prediction performance comparable to individually trained models. This work demonstrates the potential of FL-enabled AM modeling and prediction where SMEs can improve their product quality without compromising data privacy.

Mapping paleostress trajectories by means of the clustering of reduced stress tensors determined from homogeneous and heterogeneous data sets

It is easy to draw stress trajectories to investigate the present stress field by interpolating stress orientations determined at control points. However, challenges arise when we deal with the trajectories of paleostresses, because neighboring control points may have the stress orientations of different tectonic phases. We must choose coeval stresses to draw the trajectories. Recent stress inversion techniques can separate stresses from heterogeneous data from fault, dilational fractures, etc. Natural data sets from those structures are often heterogeneous, and age data are usually not enough to classify the stresses by age. As a result, an unsupervised classification problem of the inversion results must be solved to draw the trajectories. Here, we propose a simple and heuristic procedure for this problem. We assume smooth trajectories during each of the phases. The smoothness makes density-based clustering adoptable to solve the problem. The heterogeneity of data sets allows the additional partition of the clusters. As a worked exercise for this technique, the trajectories of minimum horizontal stress orientations were drawn based on the paleostresses determined from the attitudes of felsic dikes and quartz veins formed in mid Cretaceous orogeny in the North Kitakami Terrain, northern Japan. The orogen-parallel and orogen-perpendicular extensional stress fields delineated by the present technique were probably the manifestations, respectively, of the gravitational collapse of the orogen and of regional extensional tectonics in the Far East.

Deep Neural Networks and Tabular Data: A Survey.

Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous datasets, deep neural networks have repeatedly shown excellent performance and have therefore been widely adopted. However, their adaptation to tabular data for inference or data generation tasks remains highly challenging. To facilitate further progress in the field, this work provides an overview of state-of-the-art deep learning methods for tabular data. We categorize these methods into three groups: data transformations, specialized architectures, and regularization models. For each of these groups, our work offers a comprehensive overview of the main approaches. Moreover, we discuss deep learning approaches for generating tabular data and also provide an overview over strategies for explaining deep models on tabular data. Thus, our first contribution is to address the main research streams and existing methodologies in the mentioned areas while highlighting relevant challenges and open research questions. Our second contribution is to provide an empirical comparison of traditional machine learning methods with 11 deep learning approaches across five popular real-world tabular datasets of different sizes and with different learning objectives. Our results, which we have made publicly available as competitive benchmarks, indicate that algorithms based on gradient-boosted tree ensembles still mostly outperform deep learning models on supervised learning tasks, suggesting that the research progress on competitive deep learning models for tabular data is stagnating. To the best of our knowledge, this is the first in-depth overview of deep learning approaches for tabular data; as such, this work can serve as a valuable starting point to guide researchers and practitioners interested in deep learning with tabular data.

Do We Over - Or Underestimate the Overall Cost Burden of Infectious Diseases in Aging Adults?

Background: Arithmetic average values about disease burden across aging adults are often used in the absence of having reliable access to real life data. Those values however assume group homogeneity in characteristics such as age, sex, disease incidence rates, or costs. The question arises about how much the overall outcome results, like total disease management costs, obtained under those homogeneity assumptions may deviate from real-world population data that may manifest non-homogeneous distributions. Without being able to have easily access to those real-world data and for getting a good approximation of the deviations in outcome results, a calculation method is proposed that should also indicate which factor may have a dominant influence on the cost difference between homogeneous and non-homogeneous results. The method should help focus the research for obtaining more accurate information from real-world data in subsequent steps that better estimate control gain of infectious diseases through new interventions. Methods: The method explores, as the outcome measure to assess, the relative deviation in overall infection management costs measured with homogeneity versus non-homogeneity design in the datasets of aging adults. Population modelling is used with an Extended Sensitivity Analysis Plan (ESAP) that simulates non-homogeneous, but realistic, approximates of age-specific distributional spread in demography, infectious disease, and its severity in people aged &gt; 65 years old over a 1-year period in univariant and multivariant assessments. Disease management costs are adjusted for 3 infection severity levels with increased differences between them using multiplication factors up to 20 times the initial unit cost. Results: The assumed full homogenous dataset systematically overestimates up to 10% the overall disease management cost in aging adults when compared with a group simulated with non-homogeneous, but realistic distributions for age, infection, severity, and cost, mainly due to the difference in the demographic age composition. However, overall costs of a proposed homogeneous condition tend to underestimate the spending of non-homogeneous conditions when the reference case has a partially homogeneous setup instead of a full condition or when the demographic age-change in the non-homogeneous condition evolves towards age-demographic homogeneity (same number of people at each age with increasing age), a likely evolution in the coming 15 to 30 years. Conclusion: Assessing the current cost burden of infectious diseases in aging adults must consider exact age-composition of the demography, the type of infection spread with severity levels in function of age and their cost differences between severity levels to avoid unrealistic cost estimates when assuming unreal homogeneous group conditions that could currently overestimate the real costs.

Radial velocity homogeneous analysis of M dwarfs observed with HARPS. I. Exoplanet detection and candidates

The census of planets around M dwarfs in the solar neighbourhood meets two challenges: detecting the best targets for the future characterisation of planets with ELTs, and studying the statistics of planet occurrence that are crucial to formation scenarios. The radial velocity (RV) method remains the most appropriate for such a census as it is sensitive to the widest ranges of masses and periods. HARPS, mounted on the 3.6 m telescope at La Silla Observatory (ESO, Chile), has been obtaining velocity measurements since 2003, and can therefore be used to analyse a very large and homogeneous dataset. We performed a homogeneous analysis of the RV time series of 200 M dwarfs observed with HARPS from 2003 to 2019 (gathering more than 15000 spectra), with the aim of understanding detectable signals such as stellar and planetary companions and activity signals. The RVs were computed with a template matching method before carrying out the time series analysis. First, we focused on the systematic analysis of the presence of a dominant long-term pattern in the RV time series (linear or quadratic trend and sine function). Then, we analysed higher-frequency perdiodic signals using periodograms of the residual time series and Keplerian function fitting. We found long-term variability in 57 RV time series (28.5&lt;!PCT!&gt;). This led to the revision of the parameters of the massive planet (GJ 9482 b), as well as the detection of four substellar and stellar companions (around GJ 3307, GJ 4001, GJ 4254, and GJ 9588), for which we characterised inclinations and masses by combining RV and astrometry. The periodic analysis allowed us to recover 97&lt;!PCT!&gt; of the planetary systems already published in this sample, but also to propose three new planetary candidates orbiting GJ 300 (7.3M$_ oplus $), GJ 654(5M$_ oplus $), and GJ 739 (39M$_ oplus $), which require additional measurements before they can be confirmed.