To provide information necessary for a license application for a deep repository for spent nuclear fuel, the Swedish Nuclear Fuel and Waste Management Co. has started site investigations at two sites in Sweden. In this paper, we present a strategy to integrate site-specific ecosystem data into spatially explicit models needed for safety assessment studies and the environmental impact assessment. The site-specific description of ecosystems is developed by building discipline-specific models from primary data and by identifying interactions and stocks and flows of matter among functional units at the sites. The conceptual model is a helpful initial tool for defining properties needed to quantify system processes, which may reveal new interfaces between disciplines, providing a variety of new opportunities to enhance the understanding of the linkages between ecosystem characteristics and the functional properties of landscapes. This type of integrated ecosystem-landscape characterization model has an important role in forming the implementation of a safety assessment for a deep repository.
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http://dx.doi.org/10.1579/0044-7447(2006)35[418:asfdtb]2.0.co;2 | DOI Listing |
Alzheimers Dement
December 2024
Institute for Biomedical Informatics, Philadelphia, PA, USA.
Background: NIAGADS is a national genomics data repository that facilitates access of genotypic and sequencing data to qualified investigators for the study of the genetics of Alzheimer's disease (AD) and related neurological diseases. Collaborations with large consortia and centers such as the Alzheimer's Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the Alzheimer's Disease Sequencing Project (ADSP), and the Genome Center for Alzheimer's Disease (GCAD) allow NIAGADS to lead the effort in managing large AD datasets that can be easily accessed and fully utilized by the research community.
Method: NIAGADS is supported by the National Institute on Aging (NIA) under a cooperative agreement.
Brief Bioinform
November 2024
The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Department of Immunology, School of Basic Medical Sciences, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, Jiangsu Province, China.
Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti-PD-1/PD-L1 outcomes centered on the cGAS-STING pathway in HCC. We identified classical pathways highly correlated with cGAS-STING pathway and constructed transformer survival model preserving raw structure of pathways.
View Article and Find Full Text PDFFront Psychol
December 2024
Natural and Artificial Cognition Laboratory, Department of Humanistic Studies, University of Naples "Federico II", Naples, Italy.
Introduction: Missing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant non-response, dropout, or technical errors during data collection. Traditional methods like mean imputation or regression, commonly used to handle missing data, rely upon assumptions that may not hold on psychological data and can lead to distorted results.
View Article and Find Full Text PDFMaturitas
December 2024
Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Universiteitsweg 99, Utrecht 3508 TB, the Netherlands.
Objective: Given that Parkinson's disease is a progressive disorder, with symptoms that worsen over time, our goal is to enhance the diagnosis of Parkinson's disease by utilizing machine learning techniques and microbiome analysis. The primary objective is to identify specific microbiome signatures that can reproducibly differentiate patients with Parkinson's disease from healthy controls.
Methods: We used four Parkinson-related datasets from the NCBI repository, focusing on stool samples.
Sci Rep
December 2024
Department of Informatics, University of Hamburg, Hamburg, Germany.
Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various methods due to the complexity of the problem space.
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