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Browsing by Subject "Networks"
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Item Average Consensus in Wireless Sensor Networks with Probabilistic Network Links(2010) Saed, Steve; Li, Lingxi; Kim, Dongsoo S.; King, BrianThis study proposes and evaluates an average consensus scheme for wireless sensor networks. For this purpose, two communication error models, the fading signal error model and approximated fading signal error model, are introduced and incorporated into the proposed decentralized average consensus scheme. Also, a mathematical analysis is introduced to derive the approximated fading signal model from the fading signal model. Finally, differnt simulation scenarios are introduced and their results analyzed to evaluate the performance of the proposed scheme and its effectiveness in meeting the needs of wireless sensor networks.Item Informatics Approaches to Linking Mutations to Biological Pathways, Networks and Clinical Data(2011-07-08) Singh, Arti; Mooney, Sean; Jung, Jeesun; Romero, PedroThe information gained from sequencing of the human genome has begun to transform human biology and genetic medicine. The discovery of functionally important genetic variation lies at the heart of these endeavors, and there has been substantial progress in understanding the common patterns of single-nucleotide polymorphism (SNP) in humans- the most frequent type of variation in humans. Although more than 99% of human DNA sequences are the same across the population, variations in DNA sequence have a major impact on how we humans respond to disease; to environmental entities such as bacteria, viruses, toxins, and chemicals; and drugs and other therapies and thus studying differences between our genomes is vital. This makes SNPs as well other genetic variation data of great value for biomedical research and for developing pharmaceutical products or medical diagnostics. The goal of the project is to link genetic variation data to biological pathways and networks data, and also to clinical data for creating a framework for translational and systems biology studies. The study of the interactions between the components of biological systems and biological pathways has become increasingly important. It is known and accepted by scientists that it as important to study different biological entities as interacting systems, as in isolation. This project has ideas rooted in this thinking aiming at the integration of a genetic variation dataset with biological pathways dataset. Annotating genetic variation data with standardized disease notation is a very difficult yet important endeavor. One of the goals of this research is to identify whether informatics approaches can be applied to automatically annotate genetic variation data with a classification of diseases.Item Temporal stability of the ventral attention network and general cognition along the Alzheimer’s disease spectrum(Elsevier, 2021) Chumin, Evgeny J.; Risacher, Shannon L.; West, John D.; Apostolova, Liana G.; Farlow, Martin R.; McDonald, Brenna C.; Wu, Yu-Chien; Saykin, Andrew J.; Sporns, Olaf; Radiology and Imaging Sciences, School of MedicineUnderstanding the interrelationships of clinical manifestations of Alzheimer’s disease (AD) and functional connectivity (FC) as the disease progresses is necessary for use of FC as a potential neuroimaging biomarker. Degradation of resting-state networks in AD has been observed when FC is estimated over the entire scan, however, the temporal dynamics of these networks are less studied. We implemented a novel approach to investigate the modular structure of static (sFC) and time-varying (tvFC) connectivity along the AD spectrum in a two-sample Discovery/Validation design (n = 80 and 81, respectively). Cortical FC networks were estimated across 4 diagnostic groups (cognitively normal, subjective cognitive decline, mild cognitive impairment, and AD) for whole scan (sFC) and with sliding window correlation (tvFC). Modularity quality (across a range of spatial scales) did not differ in either sFC or tvFC. For tvFC, group differences in temporal stability within and between multiple resting state networks were observed; however, these differences were not consistent between samples. Correlation analyses identified a relationship between global cognition and temporal stability of the ventral attention network, which was reproduced in both samples. While the ventral attention system has been predominantly studied in task-evoked designs, the relationship between its intrinsic dynamics at-rest and general cognition along the AD spectrum highlights its relevance regarding clinical manifestation of the disease.