1. DiNAR: revealing hidden patterns of plant signalling dynamics using Diferential Network Analysis in RMaja Zagorščak, Andrej Blejec, Živa Ramšak, Marko Petek, Tjaša Stare, Kristina Gruden, 2018, izvirni znanstveni članek Povzetek: 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.
Ključne besede: biological networks, clustering, gene expression, time series, dynamic network analysis, dynamic data visualisation, web application, multi-conditional datasets, background knowledge
Objavljeno v DiRROS: 24.07.2024; Ogledov: 158; Prenosov: 132
 Celotno besedilo (1,63 MB)
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2. Towards the development of a landslide activity map in SloveniaMateja Jemec Auflič, Krištof Oštir, Tanja Grabrijan, Matjaž Ivačič, Tina Peternel, Ela Šegina, 2024, izvirni znanstveni članek Povzetek: To create the landslide activity map, we implemented and tested the procedure to fully utilise the 6-day repeatability of the Sentinel-1 constellation in three pilot areas in Slovenia for the observation period from 2017 to 2021. The interferometric processing of the Sentinel-1 images was carried out with ENVI SARScape, while the interpretation of the persistent scatterers InSAR data was done in three steps. In the first step, a preliminary interpretation of the landslide areas was performed by integrating the PS InSAR data into a GIS environment with information that could be relevant to explain the movement patterns of the PS InSAR points. In the second step, a field validation was performed to check the PS InSAR in the field and record the potential damage to the objects indicating the slope mass movements. In the third step, the deformations were identified, and areas of significant movement were determined, consisting of clusters of at least 3 persistent scatterers (PS) with a maximum spacing of 10 m. The landslide activity map was created based on the landslide areas categorised into four classes based on the geotechnical analyses, yearly velocity data obtained by PS InSAR, and validation of annual velocity data obtained by in-situ and GNSS monitoring and field observation. A total of 21 polygons with different landslide activities were identified in three study areas. The overall methodology will help stakeholders in the early mapping and monitoring of landslides to increase the urban resilience.
Ključne besede: landslides, EO data, sentinel, time series, methodology, Slovenia
Objavljeno v DiRROS: 30.04.2024; Ogledov: 301; Prenosov: 208
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3. Models for forecasting the traffic flow within the city of LjubljanaGašper Petelin, Rok Hribar, Gregor Papa, 2023, izvirni znanstveni članek Povzetek: Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data.
Ključne besede: traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison
Objavljeno v DiRROS: 28.09.2023; Ogledov: 564; Prenosov: 258
Celotno besedilo (5,05 MB)
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4. Modelling seasonal dynamics of secondary growth in RJernej Jevšenak, Jožica Gričar, Sergio Rossi, Peter Prislan, 2022, izvirni znanstveni članek Povzetek: The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.
Ključne besede: artificial neural networks, cambium, generalized additive model, Gompertz function, growing season, intra-annual time series
Objavljeno v DiRROS: 21.07.2022; Ogledov: 669; Prenosov: 426
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5. Ecological time series and integrative taxonomy unveil seasonality and diversity of the toxic diatom Pseudo-nitzschia H. Peragallo in the northern Adriatic SeaTimotej Turk Dermastia, Federica Cerino, David Stanković, Janja Francé, Andreja Ramšak, Magda Tušek-Žnidarič, Alfred Beran, Vanessa Natali, Marina Cabrini, Patricija Mozetič, 2020, izvirni znanstveni članek Povzetek: Pseudo-nitzschia H. Peragallo (1900) is a globally distributed genus of pennate diatoms that are important components of phytoplankton communities worldwide. Some members of the genus produce the neurotoxin domoic acid, so regular monitoring is in place. However, the identification of toxic members in routine samplings remains problematic. In this study, the diversity and seasonal occurrence of Pseudo-nitzschia species were investigated in the Gulf of Trieste, a shallow gulf in the northern Adriatic Sea. We used time series data from 2005 to 2018 to describe the seasonal and inter-annual occurrence of the genus in the area and its contribution to the phytoplankton community. On average, the genus accounted for about 15 % of total diatom abundance and peaked in spring and autumn, with occasional outbreaks during summer and large inter-annual fluctuations. Increased water temperature and decreased salinity positively affected the presence of some members of the genus, while strong effects could be masked by an unsuitable definition of the species complexes used for monitoring purposes. Therefore, combining morphological (TEM) and molecular analyses by sequencing the ITS, 28S and rbcL markers, eight species were identified from 83 isolated monoclonal strains: P. calliantha, P. fraudulenta, P. delicatissima, P. galaxiae, P. mannii, P. multistriata, P. pungens and P. subfraudulenta. A genetic comparison between the isolated strains and other strains in the Mediterranean was carried out and rbcL was inspected as a potential barcode marker in respect to our results. This is the first study in the Gulf of Trieste on Pseudo-nitzschia time series from a long-term ecological research (LTER) site coupled with molecular data. We show that meaningful ecological conclusions can be drawn by applying integrative methodology, as opposed to the approach that only considers species complexes. The results of this work will provide guidance for further monitoring efforts as well as research activities, including population genetics and genomics, associated with seasonal distribution and toxicity profiles.
Ključne besede: Pseudo-nitzschia, morphology, phylogeny, seasonality, time series, Adriatic Sea
Objavljeno v DiRROS: 18.03.2020; Ogledov: 3021; Prenosov: 1001
Celotno besedilo (6,60 MB)
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