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Currently, transdisciplinary data from animal surveillance that are available for One Health approaches to public health are scarce, negatively impacting our ability to anticipate and prepare for future public health threats, particularly those involving zoonotic diseases with pandemic or epidemic potential. In this article, we explore the potential of the common European Data Spaces framework to enhance the availability of animal surveillance data, in order to better address public health threats. We propose building upon and expanding existing initiatives, such as the European Data Spaces for Health, Agriculture, and Green Deal, to design innovative services. These services could enable the integration of different data sources to inform research and policymaking on public health interventions. An overarching layer, populated with data and generating integrative information, could support a One Health approach to research and policymaking for the preparedness and anticipation of zoonotic diseases. Consequently, this approach might foster data sharing from Member States by leveraging existing developments within data spaces in terms of, for example, data security. It could also support researchers and developers in accessing transdisciplinary, stratified, and quality-controlled data for their projects.
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http://dx.doi.org/10.1016/j.dib.2025.111332 | DOI Listing |
Inorg Chem
September 2025
Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States.
A series of six quinary rare-earth sulfides CeEuNaSiS, CeEuKSiS, CeEuRbSiS, CeEuCsSiS, CeEuAgSiS, and CeEuCuSiS were obtained in an alkali iodide flux using the boron-chalcogen mixture (BCM) method. Single crystal X-ray diffraction was used to determine the structures of the high quality single crystals that were grown; their elemental compositions were confirmed by energy-dispersive spectroscopy (EDS). The compounds crystallize in the hexagonal crystal system in the noncentrosymmetric space group 6.
View Article and Find Full Text PDFJ Prof Nurs
September 2025
University of Memphis, Loewenberg College of Nursing, USA.
Background: Nurse practitioner students' progression from observational to more independent clinical activities with minimal preceptor prompting is necessary to prepare students for practice.
Purpose: The purpose of this study was to describe and explain NP and physician preceptors' experiences with preparing nurse practitioner students for their transition to becoming autonomous clinicians during their experiences at clinical sites.
Methods: This hermeneutic phenomenological qualitative study was based on Van Manen's methodology.
Int J Radiat Oncol Biol Phys
September 2025
Radiation Oncology, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143. Electronic address:
Purpose: Accelerating MR acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize. Convolutional neural networks (CNNs)/Transformers-based deep learning (DL) methods emerged as a faster alternative but face challenges in modeling continuous k-space, a problem amplified with non-Cartesian sampling commonly used in accelerated acquisition.
View Article and Find Full Text PDFPrev Vet Med
September 2025
World Organisation for Animal Health (WOAH) Sub-Regional Representation for South East Asia, Bangkok 10400, Thailand.
Foot and mouth disease (FMD) remains endemic in several countries across Southeast Asia, China, and Mongolia (SEACFMD region), posing an ongoing threat to livestock and trade. This study aimed to investigate the epidemiological characteristics and analyze the spatial and temporal distribution of FMD outbreaks reported across the SEACFMD region. FMD outbreak and virus lineage data from 2015 to 2023 were utilized.
View Article and Find Full Text PDFComput Biol Med
September 2025
Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. Electronic address:
Modelling the diffusion-relaxation magnetic resonance (MR) signal obtained from multi-parametric sequences has recently gained immense interest in the community due to new techniques significantly reducing data acquisition time. A preferred approach for examining the diffusion-relaxation MR data is to follow the continuum modelling principle that employs kernels to represent the tissue features, such as the relaxations or diffusion properties. However, constructing reasonable dictionaries with predefined signal components depends on the sampling density of model parameter space, thus leading to a geometrical increase in the number of atoms per extra tissue parameter considered in the model.
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