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Mapping amyloid beta predictors of entorhinal tau in preclinical Alzheimer's disease.
Alzheimers Dement
January 2025
Computational Brain Research and Intervention (C-Brain) Lab, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, USA.
Introduction: Amyloid beta (Aβ) plaques and hyperphosphorylated tau in the entorhinal regions are key Alzheimer's disease (AD) markers, but the spatial Aβ pathways influencing tau pathology remain unclear.
Methods: We applied predictive modeling to identify Aβ standardized uptake value ratio (SUVR) spatial patterns that predict entorhinal tau levels, future hippocampal volume, and Preclinical Alzheimer's Cognitive Composite (PACC) scores at 5-year follow-up. The model was trained on Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 237), incorporating amyloid-PET (positron emission tomography), tau-PET, magnetic resonance imaging (MRI), and cognitive data, and validated on Harvard Aging Brain Study (HABS) (N = 276).
Dongting Lake algal bloom forecasting: Robustness and accuracy analysis of deep learning models.
J Hazard Mater
December 2024
Hunan Engineering Research Center of Water Security Technology and Application, Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, College of Civil Engineering, Hunan University, Changsha 410082, China.
Harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems, prompting efforts to predict their occurrence for swift action by water management agencies. Despite the potential for high-precision forecasting through machine learning, the effectiveness of these models is often compromised by data quality issues, such as incomplete data sets, inaccuracies in historical records, inconsistencies in sampling methods, and the dynamic nature of environmental factors, leading to temporal and spatial variability. This study develops an early warning system for HABs using water quality data from a freshwater lake prone to such blooms.
View Article and Find Full Text PDFPromoted growth with dynamic cellular stoichiometry driven by utilization of in-situ dissolved organic matter: Insights from bloom-forming dinoflagellate Prorocentrum donghaiense.
Mar Environ Res
December 2024
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China; College of Ocean and Earth Sciences, Xiamen University, Xiamen, China. Electronic address:
Mixotrophic dinoflagellates frequently cause harmful algal blooms (HABs) in eutrophic waters that contain diverse dissolved organic matter (DOM), especially intensive mariculture areas. Compared to the extensive investigation of phagotrophy and single organic molecule uptake by causative species, we have limited knowledge about the capability of mixotrophic dinoflagellates to utilize in-situ DOM in mariculture waters and its contribution to HABs. Here we use filtered in-situ mariculture water as the sole medium to examine the physiological response of Prorocentrum donghaiense to the natural mariculture DOM.
View Article and Find Full Text PDF[F]SF51, a novel F-labeled PET radioligand for translocator protein 18kDa (TSPO) in brain, works well in monkeys but fails in humans.
J Cereb Blood Flow Metab
December 2024
Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
[F]SF51 is a novel radioligand for imaging translocator protein 18 kDa (TSPO) that previously displayed excellent imaging properties in nonhuman primates. This study assessed its performance in human brain and its dosimetry. Seven healthy participants underwent brain PET imaging to measure TSPO binding using a two-tissue compartment model (2TCM) to calculate total distribution volume ().
View Article and Find Full Text PDFThe value of information in predicting harmful algal blooms.
J Environ Manage
January 2025
Bielefeld University, Faculty of Business Administration and Economics, Universitätsstraße 25, 33615 Bielefeld, Germany; Helmholtz-Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Im Technologiepark 5, 26129 Oldenburg, Germany; Fraunhofer Center for International Management and Knowledge Economy IMW, Leipziger Straße 70/71, 06108 Halle (Saale), Germany.
Environmental decision-making is inherently subject to uncertainty. However, decisions are often urgent, and whether to take direct action or invest in collecting additional data beforehand is pervasive. To make this trade-off explicit, the value of information (VoI) theory offers a powerful decision analytic tool to quantify the expected benefit of resolving uncertainty in a decision context.
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