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Journal articles on the topic 'Disease propagation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Nagaprasad, S., T. Pushpalatha, and S. Naga Lakshmi. "Heart Disease Prediction Propagation approach." International Journal of Machine Learning and Networked Collaborative Engineering 4, no. 2 (2020): 72–77. http://dx.doi.org/10.30991/ijmlnce.2020v04i02.003.

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2

Brightman, B. K., Q. X. Li, D. J. Trepp, and H. Fan. "Differential disease restriction of Moloney and Friend murine leukemia viruses by the mouse Rmcf gene is governed by the viral long terminal repeat." Journal of Experimental Medicine 174, no. 2 (1991): 389–96. http://dx.doi.org/10.1084/jem.174.2.389.

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Neonatal CxD2 (Rmcfr) and Balb/c (Rmcfs) mice inoculated with Moloney murine leukemia virus (M-MuLV) exhibited approximately equivalent time course and pathology for disease. CxD2 mice showed only slightly reduced presence of Moloney mink cell focus-forming virus (M-MCF) provirus as seen by Southern blot analysis compared to Balb/c mice. This lack of restriction for disease and spread of MCF was in sharp contrast to that seen for CxD2 mice inoculated with Friend murine leukemia virus (F-MuLV), where incidence of disease and propagation of MCFs were severely restricted, as previously reported.

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3

Rajna, Zalán, Heli Mattila, Niko Huotari, et al. "Cardiovascular brain impulses in Alzheimer’s disease." Brain 144, no. 7 (2021): 2214–26. http://dx.doi.org/10.1093/brain/awab144.

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Abstract Accumulation of amyloid-β is a key neuropathological feature in brain of Alzheimer’s disease patients. Alterations in cerebral haemodynamics, such as arterial impulse propagation driving the (peri)vascular CSF flux, predict future Alzheimer’s disease progression. We now present a non-invasive method to quantify the three-dimensional propagation of cardiovascular impulses in human brain using ultrafast 10 Hz magnetic resonance encephalography. This technique revealed spatio-temporal abnormalities in impulse propagation in Alzheimer’s disease. The arrival latency and propagation speed b

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4

Wilson, Spencer, Abdullah Alabdulkarim, and David Goldsman. "Green Simulation of Pandemic Disease Propagation." Symmetry 11, no. 4 (2019): 580. http://dx.doi.org/10.3390/sym11040580.

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This paper is concerned with the efficient stochastic simulation of multiple scenarios of an infectious disease as it propagates through a population. In particular, we propose a simple “green” method to speed up the simulation of disease transmission as we vary the probability of infection of the disease from scenario to scenario. After running a baseline scenario, we incrementally increase the probability of infection, and use the common random numbers variance reduction technique to avoid re-simulating certain events in the new scenario that would not otherwise have changed from the previou

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5

Yates, Darran. "Propagation of disease pathology in PD." Nature Reviews Neurology 5, no. 10 (2009): 522. http://dx.doi.org/10.1038/nrneurol.2009.142.

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6

MAGDOŃ-MAKSYMOWICZ, M. S., A. Z. MAKSYMOWICZ, and J. GOŁDASZ. "SIMULATION OF MAD COW DISEASE PROPAGATION." International Journal of Modern Physics C 17, no. 02 (2006): 213–22. http://dx.doi.org/10.1142/s0129183106008935.

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Computer simulation of dynamic of BSE disease is presented. Both vertical (to baby) and horizontal (to neighbor) mechanisms of the disease spread are considered. The game takes place on a two-dimensional square lattice Nx×Ny = 1000×1000 with initial population randomly distributed on the net. The disease may be introduced either with the initial population or by a spontaneous development of BSE in an item, at a small frequency. Main results show a critical probability of the BSE transmission above which the disease is present in the population. This value is vulnerable to possible spatial clus

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7

Soto, Claudio, and Gabriela P. Saborı́o. "Prions: disease propagation and disease therapy by conformational transmission." Trends in Molecular Medicine 7, no. 3 (2001): 109–14. http://dx.doi.org/10.1016/s1471-4914(01)01931-1.

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8

Luk, Kelvin C., and Virginia M. Y. Lee. "Modeling Lewy pathology propagation in Parkinson's disease." Parkinsonism & Related Disorders 20 (January 2014): S85—S87. http://dx.doi.org/10.1016/s1353-8020(13)70022-1.

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9

Potterat, J. J., R. B. Rothenberg, and S. Q. Muth. "Network structural dynamics and infectious disease propagation." International Journal of STD & AIDS 10, no. 3 (1999): 182–85. http://dx.doi.org/10.1258/0956462991913853.

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10

Qian, Yu, Søren Besenbacher, Thomas Mailund, and Mikkel Heide Schierup. "Identifying disease associated genes by network propagation." BMC Systems Biology 8, Suppl 1 (2014): S6. http://dx.doi.org/10.1186/1752-0509-8-s1-s6.

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11

Wang, Yubo, Gaoxi Xiao, Limsoon Wong, Xiuju Fu, Stefan Ma, and Tee Hiang Cheng. "Effects of fear factors in disease propagation." Journal of Physics A: Mathematical and Theoretical 44, no. 35 (2011): 355101. http://dx.doi.org/10.1088/1751-8113/44/35/355101.

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12

Xu, Zhiting. "Wave propagation in an infectious disease model." Journal of Mathematical Analysis and Applications 449, no. 1 (2017): 853–71. http://dx.doi.org/10.1016/j.jmaa.2016.11.051.

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13

Zhai, Bo, and Zhi Min Gong. "Numerical Simulation Modeling of GPR on Road Disease." Applied Mechanics and Materials 178-181 (May 2012): 1463–68. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1463.

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Numerical simulation of ground penetrating radar is an effective way of analyzing and studying high frequency electromagnetic wave propagation rules in the underground media. In this paper, Finite-Difference Time-Domain (FDTD) method is used for numerical simulation to form radar records reflection profile about road disease such as loose disease. The purpose is to study and sum up the radar wave propagation rules and reflection signal characters in this diseases media, in order to provide reliable theory support for road maintenance and evaluation.

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14

Liu, Renming, Christopher A. Mancuso, Anna Yannakopoulos, Kayla A. Johnson, and Arjun Krishnan. "Supervised learning is an accurate method for network-based gene classification." Bioinformatics 36, no. 11 (2020): 3457–65. http://dx.doi.org/10.1093/bioinformatics/btaa150.

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Abstract Background Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different n

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15

Clarke, Anthony R., Graham S. Jackson, and John Collinge. "The molecular biology of prion propagation." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 356, no. 1406 (2001): 185–95. http://dx.doi.org/10.1098/rstb.2000.0764.

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Prion diseases such as Creutzfeldt–Jakob disease (CJD) in humans and scrapie and bovine spongiform encephalopathy (BSE) in animals are associated with the accumulation in affected brains of a conformational isomer (PrP Sc ) of host–derived prion protein (PrP C ). According to the protein–only hypothesis, PrP Sc is the principal or sole component of transmissible prions. The conformational change known to be central to prion propagation, from a predominantly α–helical fold to one predominantly comprising β structure, can now be reproduced in vitro , and the ability of β–PrP to form fibrillar ag

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16

Rossi, Marcello, Simone Baiardi, and Piero Parchi. "Understanding Prion Strains: Evidence from Studies of the Disease Forms Affecting Humans." Viruses 11, no. 4 (2019): 309. http://dx.doi.org/10.3390/v11040309.

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Prion diseases are a unique group of rare neurodegenerative disorders characterized by tissue deposition of heterogeneous aggregates of abnormally folded protease-resistant prion protein (PrPSc), a broad spectrum of disease phenotypes and a variable efficiency of disease propagation in vivo. The dominant clinicopathological phenotypes of human prion disease include Creutzfeldt–Jakob disease, fatal insomnia, variably protease-sensitive prionopathy, and Gerstmann–Sträussler–Scheinker disease. Prion disease propagation into susceptible hosts led to the isolation and characterization of prion stra

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17

Sarell, Claire J., Emma Quarterman, Daniel C. M. Yip та ін. "Soluble Aβ aggregates can inhibit prion propagation". Open Biology 7, № 11 (2017): 170158. http://dx.doi.org/10.1098/rsob.170158.

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Mammalian prions cause lethal neurodegenerative diseases such as Creutzfeldt–Jakob disease (CJD) and consist of multi-chain assemblies of misfolded cellular prion protein (PrP C ). Ligands that bind to PrP C can inhibit prion propagation and neurotoxicity. Extensive prior work established that certain soluble assemblies of the Alzheimer's disease (AD)-associated amyloid β-protein (Aβ) can tightly bind to PrP C , and that this interaction may be relevant to their toxicity in AD. Here, we investigated whether such soluble Aβ assemblies might, conversely, have an inhibitory effect on prion propag

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18

Barengo, Marika, Isabella Iennaco, and Ezio Venturino. "The consequences of habitat fragmentation on disease propagation." International Journal of Computer Mathematics 91, no. 6 (2013): 1202–23. http://dx.doi.org/10.1080/00207160.2013.829212.

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19

Dagher, Alain, and Yashar Zeighami. "Testing the Protein Propagation Hypothesis of Parkinson Disease." Journal of Experimental Neuroscience 12 (January 2018): 117906951878671. http://dx.doi.org/10.1177/1179069518786715.

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20

Moreno, Julie A., and Glenn C. Telling. "Molecular Mechanisms of Chronic Wasting Disease Prion Propagation." Cold Spring Harbor Perspectives in Medicine 8, no. 6 (2017): a024448. http://dx.doi.org/10.1101/cshperspect.a024448.

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21

GAO, SHUJING, YUMIN DING, and JIANPING XIE. "ROLE OF DISEASE PROPAGATION IN MIGRATORY BIRD POPULATION." International Journal of Biomathematics 05, no. 03 (2012): 1260002. http://dx.doi.org/10.1142/s1793524512600029.

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Chatterjee considered a predator–prey model with avian migration in the migration prey population [S. Chatterjee, Alternative prey source coupled with prey recovery enhance stability between migratory prey and their predator in the presence of disease, Nonlinear Anal. Real World Appl. 11 (2010) 4415–4430]. In this paper, we modify and analyze the model by taking time dependent parameters and the general functional response into consideration. The conditions for the persistence of the system and the extinction of the disease are obtained. The global attractivity of the system is also studied. B

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22

Jackson, Graham S., and John Collinge. "Prion disease – the propagation of infectious protein topologies." Microbes and Infection 2, no. 12 (2000): 1445–49. http://dx.doi.org/10.1016/s1286-4579(00)01299-5.

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23

Picart-Armada, Sergio, Steven J. Barrett, David R. Willé, Alexandre Perera-Lluna, Alex Gutteridge, and Benoit H. Dessailly. "Benchmarking network propagation methods for disease gene identification." PLOS Computational Biology 15, no. 9 (2019): e1007276. http://dx.doi.org/10.1371/journal.pcbi.1007276.

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24

Piqueira, José Roberto C. "Rumor Propagation Model: An Equilibrium Study." Mathematical Problems in Engineering 2010 (2010): 1–7. http://dx.doi.org/10.1155/2010/631357.

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Compartmental epidemiological models have been developed since the 1920s and successfully applied to study the propagation of infectious diseases. Besides, due to their structure, in the 1960s an interesting version of these models was developed to clarify some aspects of rumor propagation, considering that spreading an infectious disease or disseminating information is analogous phenomena. Here, in an analogy with the SIR (Susceptible-Infected-Removed) epidemiological model, the ISS (Ignorant-Spreader-Stifler) rumor spreading model is studied. By using concepts from the Dynamical Systems Theo

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25

Acquatella-Tran Van Ba, Isabelle, Thibaut Imberdis, and Véronique Perrier. "From Prion Diseases to Prion-Like Propagation Mechanisms of Neurodegenerative Diseases." International Journal of Cell Biology 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/975832.

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Prion diseases are fatal neurodegenerative sporadic, inherited, or acquired disorders. In humans, Creutzfeldt-Jakob disease is the most studied prion disease. In animals, the most frequent prion diseases are scrapie in sheep and goat, bovine spongiform encephalopathy in cattle, and the emerging chronic wasting disease in wild and captive deer in North America. The hallmark of prion diseases is the deposition in the brain of PrPSc, an abnormalβ-sheet-rich form of the cellular prion protein (PrPC) (Prusiner 1982). According to the prion hypothesis, PrPSccan trigger the autocatalytic conversion o

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26

Razik, A., A. Grinstein, and J. Katan. "RHIZOCTONIA DISEASE IN PROPAGATION MATERIAL AND FIELD GROWN STRAWBERRY." Acta Horticulturae, no. 265 (December 1989): 579–85. http://dx.doi.org/10.17660/actahortic.1989.265.86.

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27

Martin, C. W. "ROSE ROSETTE DISEASE AND THE IMPACTS ON PROPAGATION©." Acta Horticulturae, no. 1055 (October 2014): 319–21. http://dx.doi.org/10.17660/actahortic.2014.1055.68.

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28

Hansen, Christian, та Jia-Yi Li. "Beyond α-synuclein transfer: pathology propagation in Parkinson's disease". Trends in Molecular Medicine 18, № 5 (2012): 248–55. http://dx.doi.org/10.1016/j.molmed.2012.03.002.

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29

Casciola-Rosen, Livia. "Autoimmune myositis: new concepts for disease initiation and propagation." Current Opinion in Rheumatology 17, no. 6 (2005): 699–700. http://dx.doi.org/10.1097/01.bor.0000179940.14109.50.

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30

Hijazi, Nuha, Yuval Shaked, Hana Rosenmann, Tamir Ben-Hur, and Ruth Gabizon. "Copper binding to PrPC may inhibit prion disease propagation." Brain Research 993, no. 1-2 (2003): 192–200. http://dx.doi.org/10.1016/j.brainres.2003.09.014.

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31

Faragó, István, and Róbert Horváth. "Qualitative properties of some discrete models of disease propagation." Journal of Computational and Applied Mathematics 340 (October 2018): 486–500. http://dx.doi.org/10.1016/j.cam.2017.09.024.

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32

Kordower, Jeffrey H., and Patrik Brundin. "Propagation of host disease to grafted neurons: Accumulating evidence." Experimental Neurology 220, no. 2 (2009): 224–25. http://dx.doi.org/10.1016/j.expneurol.2009.09.016.

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33

Wade, R., L. Igali, and A. Figus. "Skin involvement in Dupuytren’s disease." Journal of Hand Surgery (European Volume) 41, no. 6 (2015): 600–608. http://dx.doi.org/10.1177/1753193415601353.

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Whether the palmar skin has a role in the development, propagation or recurrence of Dupuytren’s disease remains unclear. Clinical assessment for skin involvement is difficult and its correlation with histology uncertain. We prospectively biopsied the palmar skin of consecutive patients undergoing single digit fasciectomy (for primary Dupuytren’s disease without clinically involved skin) and dermofasciectomy (for clinically involved skin or recurrence) in order to investigate this relationship. We found dermal fibromatosis in 22 of 44 patients (50%) undergoing fasciectomy and 41 of 59 patients

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34

Turechek, W. W. "Spatial Distribution of Crown Gall in a Commercial Nursery of Weeping Fig." Plant Health Progress 13, no. 1 (2012): 3. http://dx.doi.org/10.1094/php-2012-1126-01-rs.

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Agrobacterium larrymoorei causes tumors on weeping fig. The association between propagation and pathogen spread in mother trees and daughter branches was studied in a commercial nursery. The mother tree planting was scouted for tumors prior to and after propagation. Branches selected for propagation were tagged to track disease development. The spatial distribution of crown gall in the mother tree planting was characterized with runs, join-count, and spatial autocorrelation analyses. The association of disease in mother trees and daughter branches was characterized with cross-correlation analy

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35

Kaufmann, Dan, Jeremy J. Theriot, Jekaterina Zyuzin, et al. "Heterogeneous incidence and propagation of spreading depolarizations." Journal of Cerebral Blood Flow & Metabolism 37, no. 5 (2016): 1748–62. http://dx.doi.org/10.1177/0271678x16659496.

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Spreading depolarizations are implicated in a diverse set of neurologic diseases. They are unusual forms of nervous system activity in that they propagate very slowly and approximately concentrically, apparently not respecting the anatomic, synaptic, functional, or vascular architecture of the brain. However, there is evidence that spreading depolarizations are not truly concentric, isotropic, or homogeneous, either in space or in time. Here we present evidence from KCl-induced spreading depolarizations, in mouse and rat, in vivo and in vitro, showing the great variability that these depolariz

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36

Caughey, Byron, and Allison Kraus. "Transmissibility versus Pathogenicity of Self-Propagating Protein Aggregates." Viruses 11, no. 11 (2019): 1044. http://dx.doi.org/10.3390/v11111044.

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The prion-like spreading and accumulation of specific protein aggregates appear to be central to the pathogenesis of many human diseases, including Alzheimer’s and Parkinson’s. Accumulating evidence indicates that inoculation of tissue extracts from diseased individuals into suitable experimental animals can in many cases induce the aggregation of the disease-associated protein, as well as related pathological lesions. These findings, together with the history of the prion field, have raised the questions about whether such disease-associated protein aggregates are transmissible between humans

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37

Warner, Fiona J., Harinda Rajapaksha, Nicholas Shackel, and Chandana B. Herath. "ACE2: from protection of liver disease to propagation of COVID-19." Clinical Science 134, no. 23 (2020): 3137–58. http://dx.doi.org/10.1042/cs20201268.

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Abstract Twenty years ago, the discovery of angiotensin-converting enzyme 2 (ACE2) was an important breakthrough dramatically enhancing our understanding of the renin–angiotensin system (RAS). The classical RAS is driven by its key enzyme ACE and is pivotal in the regulation of blood pressure and fluid homeostasis. More recently, it has been recognised that the protective RAS regulated by ACE2 counterbalances many of the deleterious effects of the classical RAS. Studies in murine models demonstrated that manipulating the protective RAS can dramatically alter many diseases including liver disea

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38

Ning, Yi-zi. "Effects of epidemic prevention on the university management." Advances in Social Sciences Research Journal 7, no. 10 (2020): 311–16. http://dx.doi.org/10.14738/assrj.710.9211.

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The information transmission network is different from the physical contact network. It is of great significance to study the spread range of epidemic diseases by distinguishing the topological structure of perceptual information transmission network from that of physical contact disease diffusion network. SIR model is used to describe the transmission process of epidemic, and it is very important to explore the disease diffusion model which integrates perceptual transmission and disease diffusion. Furthermore, with a multi-layer network coupling the diffusion of perceptual information and the

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39

Marciniuk, Kristen, Ryan Taschuk, and Scott Napper. "Evidence for Prion-Like Mechanisms in Several Neurodegenerative Diseases: Potential Implications for Immunotherapy." Clinical and Developmental Immunology 2013 (2013): 1–20. http://dx.doi.org/10.1155/2013/473706.

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Transmissible spongiform encephalopathies (TSEs) are fatal, untreatable neurodegenerative diseases. While the impact of TSEs on human health is relatively minor, these diseases are having a major influence on how we view, and potentially treat, other more common neurodegenerative disorders. Until recently, TSEs encapsulated a distinct category of neurodegenerative disorder, exclusive in their defining characteristic of infectivity. It now appears that similar mechanisms of self-propagation may underlie other proteinopathies such as Alzheimer’s disease, Parkinson’s disease, Amyotrophic lateral

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40

Pasini, C., and M. G. Fantino. "CONTROL OF FUSARIUM DISEASE IN IXIA BY DIPPING PROPAGATION MATERIAL." Acta Horticulturae, no. 266 (March 1990): 407–12. http://dx.doi.org/10.17660/actahortic.1990.266.54.

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41

Wang, Xiang-Sheng, Haiyan Wang, and Jianhong Wu. "Traveling waves of diffusive predator-prey systems: Disease outbreak propagation." Discrete & Continuous Dynamical Systems - A 32, no. 9 (2012): 3303–24. http://dx.doi.org/10.3934/dcds.2012.32.3303.

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42

Kobayashi, Shunsuke, Yuko Saito, Toshiyuki Maki, and Shigeo Murayama. "Cortical propagation of Creutzfeldt–Jakob disease with codon 180 mutation." Clinical Neurology and Neurosurgery 112, no. 6 (2010): 520–23. http://dx.doi.org/10.1016/j.clineuro.2010.03.015.

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43

Tirado-Ramos, Alfredo, and Chris Kelley. "Next Steps in Simulating High-risk Infectious Disease Propagation Networks." Procedia Computer Science 18 (2013): 1421–28. http://dx.doi.org/10.1016/j.procs.2013.05.309.

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44

Duyckaerts, Charles, Florence Clavaguera та Marie-Claude Potier. "The prion-like propagation hypothesis in Alzheimerʼs and Parkinsonʼs disease". Current Opinion in Neurology 32, № 2 (2019): 266–71. http://dx.doi.org/10.1097/wco.0000000000000672.

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45

Andrade-Restrepo, Martin, Paul Lemarre, Laurent Pujo-Menjouet, Leon Matar Tine та Sorin Ionel Ciuperca. "Modeling the spatial propagation of Aβ oligomers in Alzheimer’s Disease". ESAIM: Proceedings and Surveys 67 (2020): 30–45. http://dx.doi.org/10.1051/proc/202067003.

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Recent advances in the study of Alzheimer’s Disease and the role of Aβ amyloid formation have caused the focus of biologists to progressively shift towards the smaller protein assemblies, the oligomers. These appear very early on in the disease progression and they seem to be the most infectious species for the neurons. We suggest a model of spatial propagation of Aβ oligomers in the vicinity of a few neurons, without considering the formation of large ﬁbrils or plaques. We also include a simple representation of the oligomers neurotoxic effect. A numerical study reveals that the oligomers spa

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46

Zheng, Ying-Qiu, Yu Zhang, Yvonne Yau, et al. "Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation." PLOS Biology 17, no. 11 (2019): e3000495. http://dx.doi.org/10.1371/journal.pbio.3000495.

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47

Fast, Shannon M., Marta C. González, James M. Wilson, and Natasha Markuzon. "Modelling the propagation of social response during a disease outbreak." Journal of The Royal Society Interface 12, no. 104 (2015): 20141105. http://dx.doi.org/10.1098/rsif.2014.1105.

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Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived ri

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48

Vanunu, Oron, Oded Magger, Eytan Ruppin, Tomer Shlomi, and Roded Sharan. "Associating Genes and Protein Complexes with Disease via Network Propagation." PLoS Computational Biology 6, no. 1 (2010): e1000641. http://dx.doi.org/10.1371/journal.pcbi.1000641.

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49

Ramirez, Marianela, Marek J. Krasowski, and Judy A. Loo. "Vegetative Propagation of American Beech Resistant to Beech Bark Disease." HortScience 42, no. 2 (2007): 320–24. http://dx.doi.org/10.21273/hortsci.42.2.320.

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The objective of this study was to develop vegetative propagation techniques—using tissue culture and grafting—for American beech (Fagus grandifolia) resistant to beech bark disease. Resterilizing the buds after excising bud scales reduced contamination of in vitro cultures derived from dormant buds. Application of a 7-day dark treatment before transferring shoots to the rooting medium improved rooting success. Plantlets gradually acclimatized to nonsterile growth conditions and set buds but failed to survive the dormant period. The application of 6-benzyladenine enhanced sprouting from roots

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50

Radhimeenakshi, S., and G. M. Nasira. "Prediction of Heart Disease using Neural Network with Back Propagation." International Journal of Data Mining Techniques and Applications 4, no. 1 (2015): 19–22. http://dx.doi.org/10.20894/ijdmta.102.004.001.005.

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