Moon Il Kim

Sorghum anthracnose, caused by Colletotrichum sublineola , is a rain-splash–dispersed disease for which canopy structure and tissue-specific susceptibility can shape observed severity. Here, we reanalyzed previously published sorghum–johnsongrass datasets to quantify associations between leaf-angle architecture, excised leaf susceptibility, and greenhouse disease outcomes. Basal leaf-angle profiles measured at the eight-leaf stage were available for 28 genotypes; among these, 14 genotypes had greenhouse anthracnose severity measurements for C. sublineola isolate FSP35. Across the 14 genotypes with greenhouse severity measurements, a quadratic model using canopy “droop” slope (angle versus leaf position) accounted for 87.0% of between-genotype variation in greenhouse severity ( R ² = 0.870), supporting substantial architecture–disease association within this dataset. In excised leaf assays (FSP35), midrib tissues generally exhibited equal or lower severe infection probabilities than lamina tissues, and pooled ordinal analysis supported a modest overall tissue effect, although excision-related physiological effects cannot be excluded. As an exploratory extension, we summarized quantitative reverse transcription PCR responses from a limited marker panel to estimate tissue-level defense-response fractions in four genotypes inoculated with isolate FSP53. We then compared these defense fractions against normalized tissue-level severe infection distributions using Jensen–Shannon divergence. Because the infection and response data were generated from different isolates and assay conditions, we interpret this comparison strictly as a descriptive alignment metric rather than a causal measure. Together, these results identify canopy leaf-angle descriptors and leaf blade–midrib susceptibility contrasts as quantitative traits that can be prioritized for hypothesis-driven validation and disease screening under field-relevant conditions.

Rapid, non-destructive assessment of Escherichia coli contamination levels on food-contact surfaces is important for food safety monitoring and sanitation verification. This study developed a deep-learning framework integrating UV-C fluorescence imaging and cascaded denoising to classify predefined E. coli concentration levels on four food-contact surfaces. A SafetySpect CSI-D+ handheld system acquired fluorescence images at four concentration levels: Control (0), 10⁶, 10⁷, and 10⁸ CFU/mL, on stainless steel, white rubber, white plastic, and black plastic. Five image-denoising operators (wavelet thresholding, NLM, unsharp masking, Wiener filtering, and RL-TV) were evaluated in sequential and parallel configurations. Eight deep-learning classifiers, including five YOLO11-cls variants, EfficientNetB7, ConvNeXtBase, and ViT, were benchmarked using consistent evaluation protocols. Classification performance varied with surface type and denoising strategy. The sequential Wavelet → Wiener → RL-TV cascade achieved the highest observed mean test accuracy (94.18%), with surface-specific accuracies of 97.57%, 97.57%, 94.44%, and 87.15% for stainless steel, white rubber, black plastic, and white plastic, respectively. On the most challenging surface (white plastic), the cascade improved accuracy by 11.46 percentage points over the best single operator. YOLO11-based models generally outperformed ViT, although the highest-performing architecture varied by surface. Most misclassifications occurred primarily between 10⁶ and 10⁷ CFU/mL, while the 10⁸ CFU/mL class was consistently classified accurately. Overall, the ordered cascaded denoising can improve robust E. coli concentration classification across diverse food-contact surfaces, providing a promising foundation for rapid, non-destructive food safety inspection. The results also highlight the importance of surface-specific preprocessing and deep learning architectures for robust performance across diverse materials.

Bacterial biofilms are protected by an extracellular polymeric substance (EPS) matrix primarily composed of extracellular DNA, proteins, and polysaccharides, rendering them highly resistant to conventional antimicrobial treatments. Although natural hydrolases efficiently degrade individual EPS components, their narrow substrate scope, poor stability, and high cost restrict effective biofilm eradication. Here, we report a catalytic microenvironment engineering strategy that enables broad‐spectrum hydrolase nanozymes through arginine‐directed modulation of Zr‐based metal–organic frameworks (ZrMOFs). Systematic amino acid‐directed defect engineering afforded a series of amino acid‐coordinated ZrMOFs, among which arginine‐coordinated ZrMOF (Arg‐ZrMOF) exhibited the highest hydrolase‐like catalytic efficiency ( k _cat /K _m = 1437.5 M ⁻¹ s ⁻¹ ). The enhanced catalytic performance originated from the engineered catalytic microenvironment, characterized by increased surface hydrophilicity, enhanced formation of nucleophilic Zr–OH species, and strengthened electrostatic interactions with negatively charged substrates. Arg‐ZrMOF efficiently hydrolyzed phosphodiester, peptide, and glycosidic bonds, enabling concerted degradation of extracellular DNA, proteins, and polysaccharides within the EPS matrix. This expanded hydrolytic substrate scope enabled superior inhibition and eradication of Escherichia coli and Staphylococcus aureus biofilms, outperforming the benchmark enzyme DNase I while retaining over 80% catalytic activity after 60 reuse cycles and excellent storage stability for more than 100 days. Furthermore, a spray‐on Arg‐ZrMOF formulation enabled effective biofilm removal from stainless‐steel, plastic, and glass surfaces, demonstrating its practical applicability for surface biofilm control. This work establishes catalytic microenvironment engineering as a versatile strategy for constructing hydrolase nanozymes capable of broad biomacromolecular degradation and practical biofilm eradication.

A water-soluble PEI-Vanillin schiff base fluorescent sensor enables selective, trace-level Cu ²⁺ detection through fluorescence quenching and visible colour change, offering a simple, sustainable and portable platform for environmental monitoring.

Jiwoo Shin, Seo Ju Kim, and Moon Il Kim. Korean Society for Biotechnology and Bioengineering Journal 2026;41:217-28. https://doi.org/10.7841/ksbbj.2026.41.3.217

MXenes, an emerging class of two-dimensional (2D) transition metal carbonitrides, nitrides, and carbides, are gaining significant attention due to their unique layered structures and versatile surface chemistries. These characteristics provide them with exceptional properties, making them promising for a wide array of applications. Research indicates that combining MXenes with polymers can further enhance their functional capabilities. This comprehensive overview primarily focuses on MXene-based polymer nanocomposites (PNCs), which are crucial in various research endeavors. The integration of MXene’s properties with the insulating, soft, chain-like conformations of polymers leads to the advancement of materials with synergistic properties better than those of individual components. In addition, this study highlights the fabrication techniques for MXene-based PNCs, such as in situ polymerization, melt blending, and solution casting, emphasizing their effective integration. Besides, it discusses recent advancements in these MXene-based PNCs, focusing on their structural, electrical, mechanical, and thermal properties, with particular attention to developing the multifunctionality essential for advanced electronic devices. Furthermore, the diverse applications of MXene-based PNCs, encompassing energy storage (including dielectric capacitors, supercapacitors (SCs), and batteries), sensors (including strain, gas, and humidity sensors), and flame retardants, have been demonstrated. Ultimately, this paper summarizes the key challenges and future perspectives in the fields of energy storage and sensors. It offers valuable insights to guide future research, aiming to enhance the performance and functionality of MXene-based PNCs for the development of next-generation smart and sustainable technologies.

Cacao swollen shoot disease (CSSD) remains a major viral threat to cacao production in West Africa and is associated with a genetically diverse complex of badnaviruses. We combined alignment-free whole-genome distances with circular sliding-window mosaic barcodes to characterize global divergence and local compositional modularity across 48 full-length CSSD-associated badnavirus genomes, hereafter termed the cacao swollen shoot virus (CSSV) complex. Whole-genome tetranucleotide cosine distance clusters matched published species assignments exactly (adjusted Rand index = 1.00; normalized mutual information = 1.00; silhouette = 0.703) and were strongly concordant with an alignment-based distance derived from open reading frame 3 (ORF3) proteins (Spearman ρ ≈ 0.951; permutation p ≈ 0.0002). A three-component mosaic-complexity score summarized low dominant-label purity, barcode entropy normalized by log₂(K), and circular switch rate; ORF–mosaic agreement was retained as a separate diagnostic and did not contribute to the ranking. Threshold sensitivity showed that barcode switchpoints were enriched within ±100 and ±200 bp of predicted ORF boundaries, but not within ±400 bp. The ten highest-scoring genomes showed slightly higher mean local nucleotide entropy but lower entropy variance and dispersion than the remaining genomes, indicating more uniformly distributed compositional complexity rather than isolated local spikes. These analyses provide a transparent framework for post-sequencing classification, comparison, and prioritization of complete viral genomes. The barcode and score outputs are exploratory summaries and are not direct field diagnostic assays or nucleotide-resolution recombination tests.