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1.
DiNAR: revealing hidden patterns of plant signalling dynamics using Diferential Network Analysis in R
Maja Zagorščak, Andrej Blejec, Živa Ramšak, Marko Petek, Tjaša Stare, Kristina Gruden, 2018, original scientific article

Abstract: Background Progress in high-throughput molecular methods accompanied by more complex experimental designs demands novel data visualisation solutions. To specifically answer the question which parts of the specifical biological system are responding in particular perturbation, integrative approach in which experimental data are superimposed on a prior knowledge network is shown to be advantageous. Results We have developed DiNAR, Differential Network Analysis in R, a user-friendly application with dynamic visualisation that integrates multiple condition high-throughput data and extensive biological prior knowledge. Implemented differential network approach and embedded network analysis allow users to analyse condition-specific responses in the context of topology of interest (e.g. immune signalling network) and extract knowledge concerning patterns of signalling dynamics (i.e. rewiring in network structure between two or more biological conditions). We validated the usability of software on the Arabidopsis thaliana and Solanum tuberosum datasets, but it is set to handle any biological instances. Conclusions DiNAR facilitates detection of network-rewiring events, gene prioritisation for future experimental design and allows capturing dynamics of complex biological system. The fully cross-platform Shiny App is hosted and freely available at https://nib-si.shinyapps.io/DiNAR. The most recent version of the source code is available at https://github.com/NIB-SI/DiNAR/ with a DOI 10.5281/zenodo.1230523 of the archived version in Zenodo.
Keywords: biological networks, clustering, gene expression, time series, dynamic network analysis, dynamic data visualisation, web application, multi-conditional datasets, background knowledge
Published in DiRROS: 24.07.2024; Views: 158; Downloads: 132
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2.
Measurements of bridge dynamic amplification factor using bridge weigh-in-motion data
Jan Kalin, Aleš Žnidarič, Andrej Anžlin, Maja Kreslin, 2022, original scientific article

Abstract: The dynamic component of bridge traffic loading is commonly taken into account with a Dynamic Amplification Factor (DAF)—the ratio between the dynamic and static load effects on a bridge. In the design codes, this factor is generally more conservative than in reality. Recently a new method of cal- culation of this factor had been developed. Data from 15 different bridges have been analysed since then and this paper presents the results of the analyses. The background for Bridge Weigh-in-Motion is given, and the most recent method for DAF calculation is described. The sites from which the data originated are presented, and the selection of data discussed. The results of the analyses are presented and discussed and some examples of DAF calculations are shown. Data from the considered sites have invariably demonstrated a DAF decrease with increasing axle load. This is a significant result, especially for assessment of existing structures, since it is beneficial to use measured structural param- eters to optimise structural analysis.
Keywords: bridge loads, bridge weigh-in-motion, dynamic amplication factor, dynamic analysis, measurement, traffic loading
Published in DiRROS: 14.07.2023; Views: 502; Downloads: 278
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