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The Extramural Leiden University Medical Center Academic Network (ELAN) is a data infrastructure in the Netherlands that combines healthcare data from various sources to improve patient care and health outcomes through research collaborations. The data includes information on 2.6 million individuals residing in South Holland province since 2007, with coded clinical measurements, morbidities, medication use, and social determinants of health. The document also provides data on characteristics of different countries, including population, gender distribution, age, urbanization, disposable household income, education level, and country of origin. The ELAN database contains information from general practitioners, hospitals, mental healthcare organizations, and national and municipal sources, covering medical information, pregnancies, births, and social determinants of health. The data is pseudonymized, linked across sources, and undergoes harmonization for research projects. While there are limitations to the data, researchers can request access for approved collaborative projects. [Extracted from the article]

Digital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care systems. Techniques for generating synthetic data from real datasets are highly advanced and continually evolving. This paper aims to present the INSAFEDARE project's ambition regarding medical devices’ regulation and how real and synthetic data can be used to check if devices are safe and effective. The project will consist of three pillars: a) assurance of new state-of-the-art technologies and approaches (such as synthetic data), which will support the validation methods as part of regulatory decision-making; b) technical and scientific, focusing on data-based safety assurance, as well as discovery, integration and use of datasets, and use of machine learning approaches; and c) delivery to practice, through co-production involving relevant stakeholders, dissemination and sustainability of the project’s outputs. Finally, INSAFEDARE will develop an open syllabus and training certification for health professionals focused on quality assurance. [ABSTRACT FROM AUTHOR]

Previous methods to systematically characterize sequence-intrinsic activity of promoters have been limited by relatively low throughput and the length of the sequences that could be tested. Here we present 'survey of regulatory elements' (SuRE), a method that assays more than 108 DNA fragments, each 0.2-2 kb in size, for their ability to drive transcription autonomously. In SuRE, a plasmid library of random genomic fragments upstream of a 20-bp barcode is constructed, and decoded by paired-end sequencing. This library is used to transfect cells, and barcodes in transcribed RNA are quantified by high-throughput sequencing. When applied to the human genome, we achieve 55-fold genome coverage, allowing us to map autonomous promoter activity genome-wide in K562 cells. By computational modeling we delineate subregions within promoters that are relevant for their activity. We show that antisense promoter transcription is generally dependent on the sense core promoter sequences, and that most enhancers and several families of repetitive elements act as autonomous transcription initiation sites. [ABSTRACT FROM AUTHOR]

To investigate feedback between relativistic jets emanating from active galactic nuclei and the stellar population of the host galaxy, we analyze the long-term evolution of the orbits of the stars formed in the galaxy-scale simulations by Gaibler et al. of jets in massive, gas-rich galaxies at z ∼ 2-3. We find strong, jet-induced differences in the resulting stellar populations of galaxies that host relativistic jets and galaxies that do not, including correlations in stellar locations, velocities, and ages. Jets are found to generate distributions of increased radial and vertical velocities that persist long enough to effectively augment the stellar structure of the host. The jets cause the formation of bow shocks that move out through the disk, generating rings of star formation within the disk. The bow shock often accelerates pockets of gas in which stars form, yielding populations of stars with significant radial and vertical velocities, some of which have large enough velocities to escape the galaxy. These stellar population signatures can serve to identify past jet activity as well as jet-induced star formation. [ABSTRACT FROM AUTHOR]

Data-driven technologies have improved the efficiency, reliability and effectiveness of healthcare services, but come with an increasing demand for data, which is challenging due to privacy-related constraints on sharing data in healthcare contexts. Synthetic data has recently gained popularity as potential solution, but in the flurry of current research it can be hard to oversee its potential. This paper proposes a novel taxonomy of synthetic data in healthcare to navigate the landscape in terms of three main varieties. Data Proportion comprises different ratios of synthetic data in a dataset and associated pros and cons. Data Modality refers to the different data formats amenable to synthesis and format-specific challenges. Data Transformation concerns improving specific aspects of a dataset like its utility or privacy with synthetic data. Our taxonomy aims to help researchers in the healthcare domain interested in synthetic data to grasp what types of datasets, data modalities, and transformations are possible with synthetic data, and where the challenges and overlaps between the varieties lie.