Bahona College

Tomato is a globally important crop and model for plant-pathogen studies, with bacterial wilt caused by Ralstonia solanacearum posing a major threat. Understanding metabolic changes in resistant and susceptible cultivars during infection is vital. This study investigated the metabolic responses of resistant (Hawaii 7996) and susceptible (Marmande) tomato cultivars under R. solanacearum infection, identified key metabolites and defense pathways, and compared metabolite accumulation using high-resolution mass spectrometry. Liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF-MS) was used to analyze root samples at 6 days post-inoculation. Data processed in MetaboAnalyst 6.0 identified differentially accumulated metabolites through multivariate analysis, heatmaps, and volcano plots. Pathway and chemical superclass enrichment analyses revealed distinct responses between cultivars. The resistant cultivar showed markedly higher abundance of defense-related metabolites in pathways such as glutathione metabolism, tropane/piperidine/pyridine alkaloid biosynthesis, phenylalanine metabolism, terpenoid backbone biosynthesis, and sesquiterpenoid/triterpenoid biosynthesis, indicating a robust defense response. In contrast, the susceptible cultivar exhibited fewer changes in these pathways, suggesting a weaker defense. Baseline differences were also observed, with the resistant cultivar displaying elevated levels of certain defense-associated metabolites even without infection. Overall, the findings clearly demonstrate that resistance in Hawaii 7996 is driven by a strong activation and maintenance of key defense-related metabolic pathways, whereas Marmande fails to initiate comparable metabolic reprogramming under infection. These results provide direct metabolomic evidence linked to bacterial wilt resistance and identify specific metabolites and pathways that may serve as promising biochemical targets for breeding disease-resilient tomato cultivars.

Accurate quantification of plant disease severity is essential for evaluating host-pathogen interactions and assessing the effectiveness of disease management strategies. Traditional visual scoring methods and manual estimation of infected tissue are widely used but are often subjective and prone to observer bias. Digital image analysis offers an objective alternative by enabling automated identification and quantification of symptomatic plant tissues based on color and spatial characteristics. Here, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images. The workflow involves acquisition of standardized leaf images, conversion of RGB images into hue-saturation-value (HSV) color space, segmentation of diseased tissue using defined HSV thresholds, refinement of the segmented mask through morphological operations, and extraction of the whole leaf area. The protocol then calculates the diseased area and total leaf area in pixels and computes the percentage of infected tissue. The method uses MATLAB together with the Image Processing Toolbox and can be implemented using simple scripts. This protocol enables rapid and reproducible quantification of disease severity in plant leaves exhibiting visually distinct symptoms such as necrotic lesions or blight patches. By minimizing observer bias and providing quantitative measurements of infected area, the protocol offers a practical and reproducible approach for plant disease phenotyping and evaluation of disease management strategies across diverse plant-pathogen systems where diseased tissues can be clearly distinguished from healthy tissues under reasonably controlled imaging conditions. Key features • A reproducible MATLAB-based workflow for separating diseased and healthy plant tissue using color-space segmentation. • Applicable to plant diseases where symptomatic tissue contrasts clearly with healthy tissue (necrosis, blight lesions, rot patches). • Requires digital leaf images, MATLAB, and the MATLAB Image Processing Toolbox for image processing and disease quantification. • Enables rapid calculation of diseased leaf area and disease severity using automated pixel-based quantification.

In this article, we present novel correlation coefficients for measuring the relationship between two fuzzy sets. Here we put forward mathematical models to determine the evolutionary relationships between SARS-CoV & SARS-CoV-2 and five other commonly known corona viruses in human which includes MERS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63 and HCoV-HKU1 and two bat corona viruses (RsSHC014 and RaTG13) with special emphasis on their correlation. The work can be considered as a pioneer in the field of application of fuzzy set theory in the study of this zoonsis.

Lemon grass (Cymbopogon citratus, LG-SiO2), horsetail (Equisetum debile, SG-SiO2), and cassava leaves (Manihot esculenta, CL-SiO2) were used to green-synthesize silica (SiO2) nanoparticles (NPs). While LG-SiO2 and SG-SiO2 had strong (101) peaks and a large 22 degrees amorphous peak, XRD of CL-SiO2 verified crystallinity. LG-SiO2 (agglomerated porous), SG-SiO2 (rectangular-cube agglomerates), and CL-SiO2 (flake agglomerates) had different morphologies. The TEM image of LG-SiO2 revealed agglomerated spherical NPs with an average particle size of about 32.17 nm, the smallest among all the samples. Under solar illumination, LG-SiO2 demonstrated superior photocatalytic activity, which is 1.5 times higher than SG-SiO2 and 3.2 times higher than CL-SiO2 in terms of their rate constant values. Concentration-dependent inhibitory zones were observed in antibacterial tests against Staphylococcus aureus and Escherichia coli. Among the samples, LG-SiO2 exhibited the strongest antibacterial response. This work provides an optimal green catalyst for dual applications that have not yet been investigated.