The present work reports a Density Functional Theory (DFT)-based computational investigation into the question of aromaticity of azaborines (1,2-azaborine, 1,3-azaborine and 1,4-azaborine). The aromaticity of the molecules has been addressed by the method of Nucleus-Independent Chemical Shift (NICS) scan which reveals the presence of diatropic ring current within the rings revealing the trend of aromaticity as: benzene > 1,3-azaborine > 1,2-azaborine ≈ 1,4-azaborine > borazine. The test of aromaticity based on Harmonic Oscillator Model of Aromaticity (HOMA) index shows a trend as: benzene > borazine > 1,3-azaborine ≈ 1,2-azaborine > 1,4-azaborine, whereas the electronic-based aromaticity indices like para-delocalization index (PDI), para-linear response (PLR) index and multi-center delocalization index (MCI) shows a trend as: benzene > 1,3-azaborine ≈ 1,2-azaborine > 1,4-azaborine > borazine. The trend predicted by aromatic fluctuation (FLU) index (benzene > borazine > 1,3-azaborine > 1,2-azaborine > 1,4-azaborine), or the energetic criterion of aromatic stabilization energy (1,3-azaborine > benzene > 1,2-azaborine ≈ 1,4-azaborine > borazine) is found to vary from those of the other criteria. All the aromaticity indices of azaborines are systematically compared with the hydrocarbon (benzene) and fully B-N (borazine) parents. Though the results indicate the aromatic character of the molecules, distinct discrepancies are noted in predicting the trend of aromaticity whereby pointing toward the ambiguity that still persists in calculation of the important qualitative phenomenon of aromaticity in azaborines. A Density Functional Theory-based computational investigation into the question of aromaticity of 1,2-, 1,3- and 1,4-azaborines with reference to magnetic, energetic, geometrical and electronic-based criteria is undertaken. Through systematic comparison with the hydrocarbon (benzene) and fully B-N (borazine) parents the discrepancies among various aromaticity indices are highlighted.
Antimicrobial resistance (AMR) and antibiotic resistance (ABR) represent one of the most pressing global health threats, driven by the complex interplay between human, animal, and environmental factors. The One Health resistome framework recognises that resistance genes circulate continuously across clinical, agricultural, and environmental compartments through horizontal gene transfer, co-selection mechanisms, and anthropogenic contamination. This comprehensive review synthesises current evidence on integrated AMR surveillance, examining how digital technologies are transforming our capacity to monitor, predict, and respond to resistance emergence. Key advances include whole-genome sequencing enabling high-resolution pathogen tracking, metagenomics revealing environmental resistome diversity, machine learning algorithms predicting resistance phenotypes with > 85% accuracy, and point-of-care diagnostics extending sophisticated testing to resource-limited settings. Geographic information systems facilitate spatial hotspot identification, while wastewater-based surveillance provides early warning capabilities, detecting resistance genes before clinical manifestation. Despite technological progress, substantial challenges persist: fragmented data streams across sectors, lack of standardised environmental monitoring methods, limited laboratory capacity in low- and middle-income countries, and chronic underfunding. Emerging technologies, portable nanopore sequencing, CRISPR-based diagnostics, artificial intelligence, and blockchain-enabled data governance promise to address these gaps. Realising comprehensive One Health resistome surveillance requires sustained investment in interoperable digital infrastructure, international standardisation, capacity building, and political commitment to cross-sectoral coordination, prioritising equitable global implementation.
Volvariella areolavolvata sp. nov. and Volvopluteus fibrillobrunneus sp. nov. are introduced and illustrated from West Bengal, eastern India, based on detailed morphological observations and molecular phylogenetic analyses. Morphologically, V. areolavolvata is distinguished by its large-sized basidiomata with a pileus covered by conspicuous silky fibrils, becoming slightly yellowish at the center with age; the presence of a volva with a distinctive, externally areolate cracked surface; oval to ellipsoid basidiospores with a mean value of 7.75 × 4.91 μm; mostly lageniform cheilocystidia, 63–131 × 21–37 μm; and broadly clavate pleurocystidia with a rounded apex, 44–56 × 13–17 µm. Volvopluteus fibrillobrunneus is characterized by a pileus with light brown to greyish-brown fibrils; ellipsoid to elongate basidiospores (mean 9.85 × 6.30 μm); mostly clavate cheilocystidia measuring 34–72 × 15–29 μm; and rostrate pleurocystidia with prominent apical appendages, 53–75 × 14–21 µm. Phylogenetic analyses based on combined nrITS and nrLSU sequence data support the recognition of both taxa as distinct species within Volvariella and Volvopluteus, respectively, forming well-supported clades clearly separated from their closest species. Detailed morphological descriptions, field photographs of the collected basidiomata, comparisons with morphologically similar taxa, and phylogenetic analyses based on the combined nrITS and nrLSU sequence datasets are provided.
Nucleolus and neural progenitor protein (NEPRO) is a nucleolar factor required for 40S ribosomal subunit maturation and is therefore essential for the high translational demand of proliferating cancer cells. Here, we identify a bipartite nuclear localization signal (NLS; aa 74-96) in NEPRO and show that residues in both basic clusters are required for nuclear targeting. A disease-associated mutation within the C-terminal cluster, R94C, abolished NEPRO nuclear localization and markedly reduced binding to importin-α1 in vitro and in cells. Importin-α1-NLS complexes revealed that R94 forms persistent hydrogen bonds, salt bridges, and hydrophobic contacts with importin-α1 residues (A269, W273, P308, T311, P312, N350), explaining its central role in NLS recognition. Guided by these insights, we designed a rational synthetic hexapeptide inhibitor (H2N-AWPTPD-COOH) that is soluble, monodisperse, shows intrinsic fluorescence and is non-amyloidogenic. AWPTPD peptide binds wild-type NEPRO but not the R94C variant, and ab initio modeling shows peptide engagement of the R94 surface. Cellular delivery of the synthetic peptide significantly mislocalized NEPRO to the cytoplasm, reduced polysome abundance, decreased collagen secretion/deposition and clonogenicity, and induced cell-cycle arrest with upregulation of senescence markers: p16INK4A and p21WAF1/CIP1. These results validate R94 as a targetable hotspot in NEPRO's NLS and demonstrate a peptide-based approach to perturb ribosome biogenesis and suppress cancer cell growth.
Flood hazard assessment is a critical component of disaster mitigation studies, particularly within the dynamic and topographically complex Sub-Himalayan landscape. This research applies an integrated modelling framework—encompassing the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and their fuzzy logic extensions (Fuzzy AHP and Fuzzy FR)—to evaluate flood susceptibility in the Bangri–Mujnai Basin. A suite of 11 topographic, hydrological, and climatic parameters was derived from remote sensing datasets and synthesized to produce thematic layers. These layers were then weighted through pairwise comparison and statistical correlation techniques to generate flood hazard maps for each model variant. Results indicate a predominance of moderate flood hazard zones in all models, with high susceptible areas concentrated primarily near the major stream networks—especially around the alluvial fan–floodplain transition at 125 m elevation. Of particular note, the Fuzzy FR model identified the least spatial extent of high hazard zones, underscoring its enhanced analytical precision. Validation using historical flood extent data extracted from Sentinel-1A SAR-C imagery revealed strong concordance between the model outputs and field observations. The analysis also highlights the continued influence of the Titi River’s avulsion, where its former confluence with the Bangri remains a significant driver of flood dynamics. Overall, this study demonstrates the utility of integrating fuzzy logic with conventional modelling approaches to improve flood hazard assessments in highly variable environmental contexts. The findings offer valuable insights for policymakers to inform targeted flood mitigation measures and guide sustainable land use planning in the vulnerable Sub-Himalayan regions.
