Disease transmission models often assume homogenous mixing. This assumption, however, has the potential to misrepresent the disease dynamics for populations in which contact patterns are non-random. A disease transmission model with an SEIR structure was used to compare the effect of weighted and unweighted empirical equine contact networks to weighted and unweighted theoretical networks generated using random mixing. Equine influenza was used as a case study. Incidence curves generated with the unweighted empirical networks were similar in epidemic duration (5-8 days) and peak incidence (30.8-46.4%). In contrast, the weighted empirical networks resulted in a more pronounced difference between the networks in terms of the epidemic duration (8-15 days) and the peak incidence (5-25%). The incidence curves for the empirical networks were bimodal, while the incidence curves for the theoretical networks were unimodal. The incorporation of vaccination and isolation in the model caused a decrease in the cumulative incidence for each network, however, this effect was only seen at high levels of vaccination and isolation for the complete network. This study highlights the importance of using empirical networks to describe contact patterns within populations that are unlikely to exhibit random mixing such as equine populations.
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http://dx.doi.org/10.1038/s41598-019-40151-2 | DOI Listing |
Neural Comput
January 2025
Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, U.K.
The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned patterns from their noisy variants. Solutions to this problem may be required to memorize vast numbers of patterns based on limited training data and subsequently recall the patterns in the presence of noise.
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December 2024
Institute of Neurosciences. Department of Biomedicine, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
Large neuroimaging datasets play a crucial role in longitudinal modelling and prediction of neurodegenerative diseases, as they provide the opportunity to study biomarker trajectories over time. Noteworthy, the availability of these large datasets coexists with a paradigm shift in the theoretical understanding of these diseases: while classical studies aimed at defining disease signatures as group patterns obtained with static cross-sectional analyses, novel approaches focus on providing individual predictions in the context of phenotypical and temporal heterogeneity. This scenario is often aggravated by the fact that datasets are not homogeneous and suffer from missing points and noisy data.
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December 2024
Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain. Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
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December 2024
UC Davis Alzheimer's Disease Center, Walnut Creek, CA, USA.
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View Article and Find Full Text PDFAlzheimers Dement
December 2024
Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Structural covariance analyses have identified macrostructural/morphological alterations to MRI-based networks in behavioral variant frontotemporal dementia (bvFTD), but microstructural/neuronal alterations to histology-based networks remain unexplored. We previously found greater neurodegeneration in layers and regions enriched for pyramidal neurons in bvFTD with tau (bvFTD-tau) compared to TDP-43 (bvFTD-TDP) pathology. Therefore, we hypothesized laminar networks of empirically connected pyramidal neurons are weaker in bvFTD-tau versus bvFTD-TDP.
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