Aspergillus flavus is an agriculturally important and aflatoxigenic fungus, underscoring the need for alternative antifungal strategies. Cysteine-rich antifungal proteins (AFPs) are promising bioactive molecules, yet their recombinant production and genetic determinants of fungal tolerance remain insufficiently characterized. Here, we investigated AnAFP, an antifungal protein from Aspergillus niger, and evaluated its activity against A. flavus. Bioinformatic analyses predicted an N-terminal signal peptide, a putative intrinsically disordered region, and a mature cysteine-rich domain structurally related to known fungal AFPs. Guided by these features, the predicted mature region of AnAFP was expressed in Escherichia coli and purified through Ni-NTA affinity chromatography, tag cleavage, cation-exchange chromatography, and size-exclusion chromatography. Purified AnAFP inhibited A. flavus growth, and comparison with PgAFP and AfAFP confirmed antifungal activity at micromolar concentrations. To identify genes associated with AFP tolerance, Δado1, Δdef1, and Δadk1 mutants were generated by homologous recombination. All three mutants showed increased sensitivity to AnAFP, PgAFP, and AfAFP relative to the wild-type strain, suggesting that ado1, def1, and adk1 may contribute to AFP tolerance. Deletion of these genes also affected colony growth, conidiation, sclerotial formation, stress responses, and aflatoxin production. These findings establish a recombinant production strategy for AnAFP and provide preliminary evidence linking ado1, def1, and adk1 to AFP sensitivity and fungal physiology more broadly in this pathogenic and aflatoxigenic species.
To clarify the underlying molecular mechanisms of lactononadecapeptide (LNDP), we examined its effects on the gene expression of memory-related neuroplasticity and cholinergic signaling in human induced pluripotent stem cell (hiPSC)-derived neurons, alongside a safety evaluation using PC12 cells. LNDP showed no cytotoxicity and significantly upregulated the expression of genes crucial for neuroplasticity (BDNF, TrkB, NGF) and cholinergic signaling (ChAT, CHRM1, NMDAR NR1) in hiPSC-derived neurons. These findings suggest that LNDP potentially modulates transcriptional pathways related to neural health, supporting its potential value as a functional food ingredient for cognitive decline.
Understanding how residents experience living conditions is essential for reducing inequality and advancing more sustainable cities. This study assessed multidimensional urban deprivation in border settlements forming the Lagos–Ogun conurbation, with the aim of generating evidence to improve living conditions and service delivery in the study area. Systematic sampling was used to select eligible respondents (n = 325). Residents rated the importance of, and satisfaction with, key settlement attributes, from which the Residents’ Importance Attached Index (RIAI) and Residents’ Satisfaction Derived Index (RSDI) were computed. Facility conditions were assessed using a Facility Condition Index (FCI). Findings show pronounced gaps between what residents consider important and what they experience in practice across physical, social, and economic domains, indicating multidimensional deprivation in both states’ border settlements. Facility condition ratings further indicate that many basic services and public facilities are in poor condition, reinforcing deprivation. The paper recommends a coordinated Lagos–Ogun border service strategy that prioritizes rehabilitation and maintenance of critical infrastructure, strengthens development control and service accountability across jurisdictions, and leverages well-regulated public–private partnerships to expand and sustain service provision.
Marine renewable energy (MRE) is a vital component of emerging energy systems, playing a key role in the low-carbon transition and enhancing energy self-sufficiency in coastal regions. The Zhoushan Archipelago possesses favorable conditions for wind, wave, tidal-current, and solar energy, providing a resource foundation for multi-energy complementary systems. However, due to resource intermittency, spatiotemporal heterogeneity, and marine-use constraints, single MRE sources cannot independently ensure the long-term stable power supply required for isolated island grids. This study develops a comprehensive decision-making framework for wind-wave-tidal-solar integration by combining logical veto screening, the Analytic Hierarchy Process (AHP), and capacity allocation optimization. First, a multi-level evaluation system is established across resource, natural-engineering, socio-economic, and environmental dimensions, utilizing exclusionary factors—such as nature reserves, cultural heritage, and existing marine engineering—as preliminary veto criteria. Second, a “four-energy complementarity–synergy index” is introduced to characterize temporal availability, ensuring that site selection accounts for the contribution of multi-energy combinations to supply stability. Third, AHP is applied to determine weights and rank candidate sites, while a minimum-variance model optimizes capacity ratios for preferred locations. Furthermore, the TOPSIS method is introduced as an alternative multi-criteria decision-making approach for comparative analysis, to test the sensitivity of the ranking results to the choice of evaluation method. Based on the shortlisted priority candidate sites, a minimum variance capacity allocation model is established to analyse the synergy relationships between different energy types. Results indicate that multi-criteria evaluation effectively reveals suitability differences that single-resource metrics miss. Additionally, optimized capacity allocation significantly reduces combined-output fluctuations and enhances supply stability. The proposed framework is structured, verifiable, and adaptable, providing a methodological reference for the siting and preliminary capacity planning of multi-energy offshore power stations.
This research study describes a machine learning (ML)-driven model for producing smart structural materials via additive manufacturing (AM) by extrusion. A 3D concrete printing system was used to make cementitious composites that were reinforced with carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs). Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models were used to undergo supervised learning on an experimental dataset consisting of 320 specimens to predict compressive strength, electrical conductivity, and print quality as dependent on process parameters and material composition. The highest R2 of compressive strength prediction of SVM was 0.946, whereas RF had the highest R2 of 0.987, which was used to predict electrical conductivity. Optimization of parameters guided by ML had a 61.8% enhancement of compressive strength and 30.5 times increase in electrical conductivity in comparison to non-optimized baselines. Nanomaterial networks were also found to be conductive, allowing individual networks to detect their strain levels through changes in current at a strain of 0.1%, which facilitates real-time structural health sensing. The artificial system showed a 31% decrease in CO2 emissions and a 58.8% decrease in material wastage compared with the usual way of building, proving to be a valid route towards intelligent and sustainable infrastructure.
