Sher-e-Bangla Agricultural University

BACKGROUND:Humped featherback or clown-knife fish, Chitala chitala is an endangered and globally near threatened species. Mitochondrial genome or mitogenome and its bioinformatic analysis play important roles for the effective conservation of threatened fish species. METHODS AND RESULTS:Next generation sequencing decoded the complete circular mitochondrial genome of Chitala chitala which is endowed with typical organizational features of 13 protein coding genes (PCGs), 22 tRNA genes, two genes for ribosomal RNA subunits, and two non-coding regions. The mitochondrial genome of C. chitala displayed 92.87% homology with that of spotted knifefish, C. ornata (AP008923.1), followed by 91.4% homology with Indonesian featherback, C. lopis (AP008922.1). The phylogenetic analysis revealed that the mitogenome of C. chitala shares a close relationship with six other notopterids, which diverged in the Eocene period as a result of Gondwanan fragmentation and vicariant evolution of ancestral Osteoglossiform lineages. By applying bioinformatic tools, we elucidated several potential termination-associated sequence motifs, a tandem repeat array, and a short motif of conserved sequence block in the control region of C. chitala mitogenome. Consensus repeat motif and origin of light strand replication displayed predicted and potential secondary hairpin structures. The AT-GC skewness analysis exhibited a prominent negative GC-skew (-0.338) across all PCGs, revealing an "overrepresentation" of cytosine bases. The PCGs of C. chitala had the highest codon adaptation indices ranging from 0.707 to 0.816 compared to its congeners. Investigation of relative synonymous codon usage patterns revealed that a substantial set of codons in the C. chitala mitogenome were "overrepresented" and "underrepresented" indicating clear existence of codon usage bias. Comparative mutation rate analysis demonstrated that nearly all PCGs, excluding COX2, ND5, and CYTB of C. chitala mitogenome acquired non-synonymous to synonymous substitution rates (dN/dS) less than 0.1 proving the existence of intense purifying selection. CONCLUSION:We reported the first complete, verified, reference mitogenome of C. chitala and revealed distinct codon usage bias and strong purifying selection.

The integration of hyperspectral imaging (HSI) with machine learning enables non-destructive prediction and visualization of food quality. However, multicollinearity and redundant features in spectral data can reduce model accuracy and increase computational time, emphasizing the need for key wavelength selection. In response, this study presents an inventive method combining a genetic algorithm (GA) with explainable artificial intelligence (XAI) to select key wavelengths for predicting apple dry matter content (DMC). A partial least squares regression (PLSR) model using the selected features outperformed recursive feature elimination (RFE) and competitive adaptive reweighted sampling (CARS), achieving a coefficient of determination (R2) of 0.46 and a root mean squared error (RMSE) of 0.70%. The approach was further applied to hyperspectral images to visualize pixelwise DMC distribution, providing spatial insights into fruit composition. Results demonstrate that integrating XAI with evolutionary feature selection offers a noninvasive, transparent, and efficient strategy for assessing and visualizing fruit quality.

This study evaluates seasonal variations in surface water quality and heavy metal contamination in the southern coastal region of Bangladesh, specifically in Amtali and Kalapara. Water samples were collected during summer, monsoon, and winter to assess physicochemical parameters, Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), and heavy metal concentrations (Cd, As, Cu, Pb, and Cr). The updated WQI and IWQI values indicate that seasonal changes influence water quality and irrigation suitability. Monsoon samples exhibited the best water quality with WQI values ranging from 17.88 ± 6.37 in summer to 46.04 ± 16.96 in winter at Amtali, and from 26.45 ± 17.73 in summer to 60.51 ± 31.47 in winter at Kalapara, reflecting shifts from “good” to “unsuitable” conditions. The IWQI, however, remained in the “excellent” to “good” range across all seasons, with mean values of 22.27 ± 19.02 during the monsoon and 41.36 ± 7.59 in winter at Kalapara, indicating suitability for irrigation. Heavy metal concentrations were highest in winter, with Cd levels rising from 1.20 µg/L in summer to 2.15 µg/L in winter, exceeding WHO limits. Arsenic ranged from 0.10 to 0.50 µg/L, well below the WHO threshold of 10 µg/L. Lead (Pb) and Chromium (Cr) also showed elevated concentrations in winter. Principal Component Analysis (PCA) highlighted that rock-water interactions, evaporation, and anthropogenic inputs were significant contributors to seasonal variations in water quality. This study concludes that the monsoon and pre/post-monsoon seasons are the most favorable for crop irrigation in the region, minimizing the risk of heavy metal accumulation and salinity. It provides critical baseline data for sustainable water management and agricultural planning in coastal Bangladesh with relevant Sustainable Development Goals (SDGs).

Auxin, a plant growth regulator responsive to environmental stresses like drought, plays a crucial role in signaling, gene regulation, and plant sustainability. Drought sensitivity is linked to auxin-mediated cellular interactions, and gaining insights at genomic, cellular, and physiological levels can enhance plant resilience. This review discusses drought stress impacts and auxin sensitivity in cellular and nuclear modules. The biosynthetic and catabolic routes of auxin influence influx and metabolic reprogramming at both cellular and subcellular levels. Auxin interaction with other hormones affects rhizosphere sensitivity and systematic plant signaling. Subcellular drought tolerance is maintained by metabolizing reactive oxygen species, ensuring redox homeostasis. The review also covers transcriptomes regulated by auxin-responsive factors and miRNAs that modulate selective transcripts under drought. It emphasizes epigenetic regulation, including methylation, histone modification, and chromatin remodeling. Specific nucleotide residues and their auxin-induced modifications may help recall stress memory to better combat drought. For sustainable development under drought, strategies involving specific rhizobacteria and nanomaterials are discussed. New perspectives on genome stability, particularly with CRISPR/Cas9 for editing auxin-sensitive genes, aim to improve drought tolerance in crop varieties and selectable traits. Conclusively, the present review highlights auxin’s imperative role in plant reprogramming under osmotic stress, making it a key candidate for eco-friendly crop production to ensure food security.

Magnesium (Mg) is a vital macronutrient that underpins multiple processes essential for plant growth, development, and survival. As the central atom in chlorophyll, Mg is indispensable for photosynthesis, the foundation of crop productivity. Beyond light capture, Mg functions as a structural, enzymatic, and regulatory ion, making it a critical mediator of plant tolerance to abiotic stresses. Drought, salinity, extreme temperatures, and nutrient deficiencies continue to limit agricultural yields, yet Mg-mediated pathways can significantly mitigate their effects. By influencing photosynthesis, ion homeostasis, osmotic adjustment, antioxidative defenses, and signal transduction, Mg reinforces multiple layers of plant stress adaptation. This review consolidates current knowledge of Mg’s roles in enhancing plant tolerance to adverse conditions, with particular emphasis on the molecular, physiological, and biochemical mechanisms underlying these roles. By integrating findings across different scales, it advances understanding of Mg-mediated stress adaptation and highlights its potential as a key factor in developing climate-resilient crop production systems. Unlike earlier works that have focused narrowly on Mg nutrition and photosynthesis, this review offers a holistic framework linking molecular insights to agronomic applications. Additionally, it provides future perspectives and research directions to bridge current knowledge gaps and guide innovation in crop breeding, nutrient management, and sustainable production systems.