Dragana Dudić

Algorithms

NAR–SPEI–NARX Hybrid Forecasting Model for Soil Moisture Index (SMI)

Miloš Todorov; Darjan Karabašević; Predrag M. Tekić; Dragana Dudić; Dejan Viduka

https://www.mdpi.com/1999-4893/19/4/287

This paper introduces a new hybrid forecasting architecture that combines Nonlinear Autoregressive (NAR) models, the proxy Standardized Precipitation-Evapotranspiration Index (SPEI), and a Nonlinear Autoregressive with Exogenous Inputs (NARX) framework for Soil Moisture Index (SMI) prediction. The suggested methodology solves the crucial difficulty of combining future climatic knowledge into soil moisture forecasting by using a cascaded approach. Stage 1 uses univariate NAR models to create multi-step-ahead predictions of precipitation and temperature. Stage 2 converts these forecasts into proxy SPEI values using a physically based water balance computation, and Stage 3 employs a NARX model that uses observed historical SMI and forecast-derived proxy SPEI as exogenous inputs. The framework is assessed using high-frequency observations from 2014 to 2020, with training data through 2019 and validation covering the whole 2020 horizon. The study combining forecast-driven climatic indicators with autoregressive soil moisture dynamics resulted in prediction accuracy (R2 = 0.9888, RMSE = 0.0827, MAE = 0.0567). This study presents a new NAR–SPEI–NARX model for SMI prediction forecasting, based on three-stage modeling, where NAR models forecast precipitation and temperature and then turn them into SPEI-proxy as an exogenous input for NARX.

Soil Moisture Index (SMI); Standardized Precipitation-Evapotranspiration Index (SPEI); Nonlinear Autoregressive model (NAR); Nonlinear Autoregressive with Exogenous Inputs (NARX); time series

M22

Information

A Model for Evaluating WPAN Network Security Testing Methods in Educational Institutions

Ana Bašić; Veljko Aleksić; Dragana Dudić; Rade Rakić; Dejan Viduka

https://www.mdpi.com/2078-2489/17/6/553

The increasing use of wireless personal networks in educational institutions has created significant challenges in ensuring network security and the reliable testing of communication infrastructure. The selection of appropriate software tools for network security testing is a complex decision-making problem due to multiple software quality criteria and operational requirements. This paper proposes a multi-criteria model for evaluating approaches to wireless personal network security testing in educational institutions through the analysis of representative software tools. The evaluation framework is based on the ISO/IEC 25010 software quality criteria: reliability, functional suitability, interoperability, performance efficiency and scalability, compatibility and maintainability. Five widely used tools (Nmap, OpenVAS, Nessus, Wireshark and Wazuh) were analyzed using a structured multi-criteria approach. Criteria weights were determined using the PIPRECIA-S method, while the ranking was verified using the TOPSIS method. The results show that Wazuh achieved the highest overall score (0.3051), followed by Wireshark (0.2315) and Nessus (0.1954), while OpenVAS (0.1443) and Nmap (0.1225) achieved lower ranks. The stability and reliability of the model were confirmed by sensitivity analysis, Pareto analysis, Spearman’s rank correlation and scenario analysis. The model provides a reliable decision-support framework for selecting network security testing approaches in educational and similar organizational environments.

wireless personal area networks; network security; software evaluation; multi-criteria decision-making; educational institutions; cybersecurity; decision support

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Current Issues in Molecular Biology

Genetic Relatedness and Heterotic Grouping in MRIZP Elite Maize Inbred Lines Using SNP Markers from 25k SNP Array and RNA-seq Data

Marko Mladenović; Bojana Banović Đeri; Ana Nikolić; Dragana Dudić; Slaven Prodanović; Sanja Z. Perić; Nikola Grčić

https://www.mdpi.com/1467-3045/48/6/586

Knowledge of population structure and genetic relationship among inbred lines is essential for exploiting heterosis in maize breeding programs. This study evaluated the concordance between 25k Illumina® Infinum Maize SNP Array-derived and RNA-sequencing-derived markers in estimating genetic relatedness and population structure within the Maize Research Institute “Zemun Polje” (MRIZP) breeding program. A panel of 28 elite MRIZP maize inbred lines, along with two public lines, was analyzed. For the RNA-seq data, three alternative SNP datasets were generated based on heterozygous-site handling (ALL, HOM, and FINAL) to assess their impact on downstream genetic inference. The FINAL dataset, in which heterozygous positions were recoded as missing values and re-filtered, was selected as the most balanced dataset for comparative analyses with 25k SNP array data. Despite minimal overlap between RNA-seq and 25k SNP array datasets, distance-based analyses revealed partial concordance in genetic relationship patterns and population structure between platforms. Genetic distances estimated from 25k SNP array markers were consistent with pedigree records and provided more informative insights than pedigree data alone. Population structure inferred from 25k SNP array data showed high concordance with previously defined heterotic groups, correctly assigning 29 out of 30 lines to expected clusters. RNA-seq-derived SNPs showed moderate concordance, indicating complementary but less reliable information for routine heterotic assignment.

maize inbred lines; heterotic groups; population structure; genetic relatedness; 25k SNP array; RNA-seq

M22

JITA – Journal of Information Technology and Applications

Demystification of RNAseq Quality Control

D. Dudić; B. Banović-Đeri; V. Pajić; G. Pavlović-Lažetić

https://jita-au.com/index.php/2024/03/29/demystification-of-rnaseq-quality-control/

The vast amount of currently available transcriptome sequences is comprised of Illumina RNAseq data. Usually, publicly available datasets are provided as raw data and preparing them for the downstream NGS analysis is the first step required. Such preprocessing step, besides the evaluation of the quality of the raw data, includes data filtering, in order to provide high quality results of the downstream analysis. Existing tools for NGS data filtering are either too general or incomplete for the Illumina RNAseq filtering task, which is why a new tool for this endeavor was needed. The paper presents prepRNA, a novel tool intended for Illumina RNAseq data filtering, designed as a comprehensive and user-friendly wrapper tool with the possibility of further upgrading with a quality control option.

RNAseq; data filtering; data preprocessing; NGS data; Illumina

M52

Proceedings of The First International Conference on Sustainable Environment and Technologies “Creating sustainable commUNiTy”

Solar radiation engineering dataset representation: A metadata approach

D. Dudić; I. Zlatanović

https://unt.edu.rs/wp-content/uploads/2024/04/Proceedings1.pdf

In the near future, Web 3.0 will come true and semantic metadata are crucial to enable it. That is why it is important to annotate and standardize existing web material. In 2013, SOLAR was developed as web application software for meteorological data processing, with the main goal of providing the community with easier access to different thermotechnical variables and parameters. The software was upgraded with semantic metadata. The authors created the Solar Radiation Engineering Application Profile as a robust application profile that can be extended to annotate similar meteorological and thermotechnical research. The software upgrade promotes openness and reusability of data produced by SOLAR software.

metadata schema; application profile; dataset representation; meteorological data processing; solar data software

M33

Biologia Serbica Vol. 43 (1) – Special Edition Book of Abstracts Belgrade Bioinformatics Conference 2021

Bioinformatics Analysis of Eukaryotic Positively Oriented Single Stranded RNA Viruses

Bojana Banović-Đeri; Dušan Vidanović; Bojana Tešović; Tatjana Petrović; Dejan Ristić; Ivan Vučurović; Dragana Dudić

https://plantarum.izbis.bg.ac.rs/handle/123456789/978

Positively oriented single stranded RNA viruses persistently affect the health and well-being of all eukaryotes, including plants, animals and humans. Besides high changeability, another major reason for their persistence is their ability to mimic host processes upon entering the host. This research focused on N6-methyladenosine (m6A), the most common and abundant methylation in eukaryotes, confirmed to be present in ssRNA(+) viruses as well. The study searched for patterns in primary sequences and secondary structures of ssRNA(+) associated with m6A methylation sites, relying on experimentally obtained m6A datasets for eukaryotes and eukaryotic ssRNA(+) viruses.

bioinformatics; m6A; methylome pattern; single stranded RNA viruses; ssRNA(+)

M34

Biologia Serbica Vol. 43 (1) – Special Edition Book of Abstracts Belgrade Bioinformatics Conference 2021

Bioinformatics Pipeline for Genotyping and Genotype–Phenotype Association Study in Maize (Zea mays L.)

Milica Mladenović; Nikola Grčić; Dragana Dudić; Ana Nikolić; Milica Božić; Nikola Delić; Bojana Banović-Đeri

https://imagine.imgge.bg.ac.rs/handle/123456789/1872

Multidisciplinary research is commonly used in plant breeding for improving important agronomic traits. High-throughput genotyping technologies and genotype–phenotype association studies depend on bioinformatics analysis for extracting information from gathered data. The material included 46 maize inbred lines used in breeding programs. Phenotyping was performed for thirteen important quantitative agronomic traits in eight environments during 2018 and 2019. RNA-Seq based on Next Generation Sequencing methodology was performed, and a custom-made bioinformatics pipeline included FastQC, Trimmomatic, TopHat, Cufflinks, Cuffmerge, FreeBayes and BCFtools. Genotype–phenotype association analysis was conducted using TASSEL and machine learning software such as WEKA, with results compared and discussed.

maize; bioinformatics; genotyping; RNA-Seq; genotype-phenotype association

M34

Biologia Serbica Vol. 43 (1) – Special Edition Book of Abstracts Belgrade Bioinformatics Conference 2021

Differential Gene Expression Analysis of Heterotic Groups’ Maize Inbred Lines Under Optimal Conditions Led to the Identification of Specific Gene Regulation Under Low Temperature

Milica Božić; Ana Nikolić; Dragana Dudić; Dragana Ignjatović-Micić; Jelena Samardžić; Nikola Delić; Bojana Banović-Đeri

https://imagine.imgge.bg.ac.rs/handle/123456789/1874?locale-attribute=sr

Finding new ways of improving crop quality, yield potential and abiotic stress tolerance is highly important in crop production. Comparative analysis of 46 maize inbred lines belonging to two different genetic backgrounds, Lancaster and Non-Lancaster, was performed by whole transcriptome sequencing and differential gene expression analysis. The analysis revealed 77 differentially expressed genes between the Lancaster and Non-Lancaster groups, 21 of which were statistically supported and annotated as involved in abiotic stress responses. A subset of seven genes was further analyzed under low temperature treatment. Six differentially expressed genes showed different expression regulation depending on cold exposure duration and genetic background, contributing to the understanding of maize cold response and adaptation.

Transcriptomics; NGS; DEGs; maize; cold tolerance

M34

7th Plant Genomics and Gene Editing Congress & 2nd Microbiome for Agriculture: Asia

Genetic-Background Dependent Cold Response in Lancaster vs. Other Heterotic Groups in Maize

Bojana Banović-Đeri; Milica Božić; Dragana Dudić; Ivana Vićić; Milica Milivojević; Dragana Ignjatović-Micić; Jelena Samardžić; Jelena Vančetović; Nikola Delić; Ana Nikolić

https://global-engage.com/product/plant-genomics-2021/

Maize early sowing became one of the main strategies for overcoming maize yield decrease caused by global climate change. The research aimed to contribute to this strategy by deepening knowledge needed for the development of cold tolerant maize hybrids. Comparative total transcriptome analysis of the genetic background of 46 maize inbred lines revealed 77 differentially expressed genes under optimal growing conditions. Cold testing of eight maize inbred lines showed that Lancaster lines were mainly cold sensitive, while Non-Lancaster lines were mainly cold tolerant. Results suggest that differences in genetic background involve, at least, differences in photosynthesis and sulfate assimilation, contributing to different cold response and adaptation to low temperature.

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M34

The Frontiers of Science and Technology in Crop Breeding and Production Conference – Book of Abstracts

Understanding Low-Temperature and Waterlogging Stress Impact on Early Stages of Maize Plant Development

Ana Nikolić; Bojana Banović-Đeri; Dragana Dudić; Milica Božić; Katarina Marković; Nikola Delić; Dragana Ignjatović-Micić

https://rik.mrizp.rs/handle/123456789/1138

Abiotic stress seriously affects and limits maize productivity worldwide. Earlier maize sowing can help avoid high temperatures during silking and pollination, but may expose plants to suboptimal temperatures during early developmental stages. The study aimed to identify tolerant maize lines for planning future hybrids, unravel mechanisms involved in maize response to low temperatures and identify molecular markers for breeding programs. Bioinformatic analysis of SNPs and differentially expressed genes was performed. Cold-induced expression analysis of several genes revealed regulation dependent on the duration of cold stress, indicating their possible role in maize response to low temperatures. Waterlogging stress was also identified as an important issue to be studied in the same context.

maize; abiotic stress; low-temperature tolerance; NGS; DEGs

M34

The Frontiers of Science and Technology in Crop Breeding and Production Conference – Book of Abstracts

Several Genes Involved in Low Temperature Response in Maize Follow Different Expression Patterns at Different Developmental Stages

Milica Božić; Bojana Banović-Đeri; Dragana Dudić; Dragana Ignjatović-Micić; Jelena Vančetović; Nikola Delić; Ana Nikolić

https://rik.mrizp.rs/bitstream/id/6140/ZP75-Conference-Book-of-abstracts.pdf

Earlier sowing is an important strategy for ensuring good yield potential and crop quality under poor environmental conditions caused by climate change. Sowing maize in early spring helps avoid drought and high summer temperatures, but exposes maize plants to suboptimal temperatures during earlier developmental stages. An initial study included whole transcriptome sequencing of 46 maize inbred lines at the V4 stage under optimal temperature conditions. Gene expression analysis revealed 77 differentially expressed genes. Five analyzed genes showed different expression regulation depending on cold exposure duration, and two genes showed regulation depending on both cold exposure duration and genetic background. The results imply that processes underlying maize low temperature response are dependent on developmental stage and genetic background.

maize; gene expression; low temperature; developmental stages; transcriptomics

M34