OPTIMIZATION OF DNA EXTRACTION AND PCR AMPLIFICATION FOR POLLEN DNA BARCODING FROM FLOWERING PLANTS

2026-04-01T11:06:46+08:00

DNA barcoding is a molecular technique that relies on short DNA region for species identification, with its accuracy and reliability is getting enhanced with the advance technologies such as next-generation sequencing (NGS). Pollen identification using DNA barcoding offers a precise and reliable approach that is independent of morphological traits. However, challenges such as the presence of a robust exine layer encapsulating DNA, inhibitors, and contaminants necessitate improved DNA extraction methods from flowering plant pollen. Additionally, the minute quantity of DNA available in a single pollen grain complicates extraction and subsequent analysis. This study aimed to optimise DNA extraction and amplification methods for pollen DNA barcoding and to assess DNA quality using agarose gel electrophoresis. Nine species of flowering plants; Acacia auriculiform, Melastoma malabatricum, Rhodomyrtus tomentosa, Ixora chinensis, Tetracena indica, Mischocarpus sundaicus, Acronychia pedunculata, Malaleuca cajuputi, and Gmelina asiatica were obtained from Taman Rimba Ilmu Tanah BRIS UniSZA Kampus Besut (TRIBE). DNA was extracted from 50 to100 mg of pollen grains and flowers using two different kits. Three plant DNA markers, the rbcL, ITS2 and trnL were used for PCR optimization. DNA extraction from 100 mg of plant tissue (pollen with flower) yielded more distinct bands compared to 50 mg, indicating improved DNA quality with increased sample mass. Among the eight DNA samples obtained through optimised extraction, six demonstrated successful amplification with at least one marker; rbcL, ITS2, or trnL, as evidenced by distinct bands at the expected sizes. While rbcL and ITS2 consistently produced clear and specific bands, amplification with trnL resulted in multiple bands across several samples, suggesting non-specific binding or primer-dimer formation. This non-specific amplification may complicate downstream sequencing and species identification, indicating that trnL may be less suitable for these plant tissues under the current PCR conditions. BLAST analysis of six plant samples amplified with the rbcL marker identified four to the species level: Ixora chinensis, Tetracera indica, Acronychia pedunculata, and Rhodomyrtus tomentosa which based on high sequence identity (≥92.59%) and significant E-values. The remaining two samples were resolved only to the genus level (Acacia sp. and Mischocarpus sp.), indicating limitations in species-level resolution for certain taxa. These findings underscore the importance of DNA extraction quality in achieving efficient PCR amplification and highlight that marker selection, particularly the use of rbcL plays a critical role in accurate species-level identification. Further optimisation of the PCR conditions for the trnL marker is recommended to improve amplification specificity.

FIRE MITIGATION STRATEGIES IN SFERA@UMS BASED ON FIRE PREDICTION USING RANDOM FOREST

2025-12-29T13:52:52+08:00

Forest fires pose significant threats to ecosystems, biodiversity, and community. In a forested area in a university area, conducting early predictions and assessing mitigation strategies are essential for reducing the impact. This study evaluates the effectiveness of the Random Forest (RF) algorithm in predicting forest fire risk within the Sustainable Forest Education and Research Area (SFERA@UMS) and proposes targeted mitigation strategies. The approach integrates machine learning with GIS-based spatial analysis using variables such as elevation, slope, land use classification, and proximity to roads. Results show that the RF model achieved an overall prediction accuracy of 91%, with high-risk zones concentrated in low-elevation areas with steep slopes and near roads. These conditions increase fire susceptibility during higher temperatures and increase of human activity near or in the forest area, which elevate ignition potential. GIS tools were employed to generate a fire risk map, classifying areas into low, moderate, and high-risk categories for better visualization and planning. Furthermore, Unity-based digital twin technology was utilized to simulate fire spread and assess mitigation measures, including firebreaks, hydrant installations, signage, and community-based fire teams. This research demonstrates the potential of fire occurrences and suitable mitigation strategies to increase fire resilience and improve forest fire management.

Borneo Science | The Journal of Science and Technology

OPTIMIZATION OF DNA EXTRACTION AND PCR AMPLIFICATION FOR POLLEN DNA BARCODING FROM FLOWERING PLANTS

FIRE MITIGATION STRATEGIES IN SFERA@UMS BASED ON FIRE PREDICTION USING RANDOM FOREST