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Plant extract-based nanoemulsion for controlling sweet potato pests and weeds
Abstract The sweet potato weevil ( Cylas formicarius ) is the most destructive pest of sweet potato and is often associated with diseases and weeds. The use of plant extracts as pest control agents is a promising, eco-friendly approach, but their effectiveness often declines under field conditions. Therefore, nanoemulsion technology was applied to improve the dispersion and bioefficacy of active compounds. In this study, we aimed to develop plant extract-based nanoemulsions as multifunctional biopesticides capable of controlling insect pests, fungal pathogens, and weeds. Twenty-one plant extracts were screened for their effects adult SPW via contact methods. The most potent extracts were then formulated into nanoemulsions and evaluated for insecticidal efficacy—including contact toxicity, repellency, and oviposition inhibition. In addition to insecticidal effects, antifungal and herbicidal activities were systematically assessed against Fusarium sp. and two common weed species in sweet potato fields, Amaranthus tricolor and Echinochloa crus-galli . The major chemical constituents of key extracts were identified using gas chromatography–mass spectrometry (GC–MS). Among all extracts, the hexane extracts of star anise and long pepper exhibited the highest contact toxicity, with LC50 values ranging from 2.051 to 2.415% at 24 h. When formulated into nanoemulsions (NHEF-1 and NHEH-2), toxicity increased 16- to 18-fold, lowering LC 50 values to 0.114–0.150%, and at above 0.1%, oviposition was inhibited by more than 85%. Both formulations also demonstrated significant antifungal activity against Fusarium sp. (44.6–56.8% growth inhibition) and completely inhibited the germination of A. tricolor (at 0.01%) and E. crus-galli (at 1.0%). GC–MS analysis revealed anethole (84.49%) as the predominant compound in the star anise extract, with long pepper extract containing moderate amounts of several compounds. These findings demonstrate the potential of plant extract-based nanoemulsions as eco-friendly, multifunctional biopesticides for sustainable sweet potato management, integrating enhanced insecticidal, antifungal, and herbicidal effects.
Intramolecular Hydrogen Bonding Enables 5.9% External Quantum Efficiency in Radical-Based Near-Infrared Organic Light-Emitting Diodes with Emission beyond 850 nm
A multi-GPU enabled solver in Kronecker product form for multiphysics problems
Topochemical Synthesis of a Syndiotactic Polymer from a Racemic Monomer
Rapid feed component assessment to enhance livestock productivity and reduce emissions
Biomolecular Phase Boundaries are Described by a Solubility Product That Accounts for Variable Stoichiometry and Soluble Oligomers
Accurate identification of broadly neutralizing antibodies against dengue virus based on deep stacking strategy with multi-perspective features
Abstract Rapid and precise screening for broadly neutralizing antibodies (bNAbs) against all four dengue virus (DENV) serotypes (DENV-1 to DENV-4) is essential to accelerate the development of effective therapeutic antibodies and combat the global burden of dengue fever. However, characterizing bNAbs against DENV through experimental methods remains a significant challenge, as it requires costly laboratory conditions and time-consuming procedures. Therefore, computational methods for in silico identification of bNAbs against DENV based on sequence information are highly desirable and can effectively complement established experimental approaches. Thus, we propose a novel and high-accuracy computational approach, called Deepstack-NAb, for the precise identification of bNAbs against DENV. This method utilizes a stacking ensemble of multiple machine learning (ML) and deep learning (DL) algorithms. First, in Deepstack-NAb, multi-source feature encoding schemes were employed to capture key information from CDR-H3 and epitope data, including conventional feature encodings, natural language processing (NLP)-based feature encodings, and pretrained protein language models (PLMs). Second, all feature descriptors were then fused and optimized using a feature selection technique. Subsequently, well-trained ML and DL methods were constructed to generate multi-perspective features, covering both probabilistic and class information. Finally, the most informative features were selected and employed for optimizing the stacking ensemble model. We conducted comparative experiments to evaluate the performance of Deepstack-NAb against its baseline models and the existing method (i.e., PredNAb). Both cross-validation and independent test results showed that Deepstack-NAb provided a superior performance compared with both its baseline models and PredNAb. On the independent test, Deepstack-NAb achieved an accuracy of 0.905, sensitivity of 0.922, and an MCC of 0.810, representing improvements of 10.34, 13.44, and 20.65%, respectively, over PredNAb. Altogether, the excellent predictive and generalization ability of this new method Deepstack-Nab, highlights its potential as a valuable tool for accurately identifying bNAbs against DENV, significantly reducing false negatives in the process.
Protocell Survival Networks in Spatially Modulated Chemical Gradients
Growth strategy of aerial green algae on building materials in the temperate climate zone and its relevance to substrate biodeterioration
Abstract Aerial green algae colonising building materials in temperate climates form complex biofilms that contribute to the deterioration of man-made structures, reducing their aesthetic value. The present study seeks to address whether algae, already at an early stage of colonisation, can adversely influence building substrates, contributing to biodeterioration beyond mere aesthetic impact. Early-stage colonisers – Chloroidium saccharophilum , Klebsormidium nitens , Bracteacoccus minor , Diplosphaera chodatii , and Stichococcus bacillaris , were isolated from brick and plaster walls, identified using traditional and molecular methods, and grown on experimental brick and plaster substrates. Over six months, algal growth in laboratory and environmental conditions was visualised and monitored by measuring chlorophyll a concentration, fluorescence intensity, and substrate profile height. All taxa were able to grow on both types of substrates at their surface and in the interior part. Most of them formed compact biofilms that filled substrate pores and overgrew the cavities, with some producing membrane-like layers ( K. nitens and S. bacillaris ). Algae with thick cell walls ( C. saccharophilum , D. chodatii , S. bacillaris ) and filamentous K. nitens detached the mineral grains from the brick surface during biofilm separation, as a consequence of prolonged colonisation over time. Except for B. minor , all taxa contributed to the geophysical biodeterioration of brick.
A Strongly Binding Lithium Salt with Stepwise Interphase Formation Mechanism Enables Stable High-Voltage Lithium Metal Batteries
An empirical study of integration models and mechanisms for maternity care in innovative vocational education
Abstract Integrated vocational education is increasingly essential for addressing maternal health disparities in under-resourced regions of China. Persistent workforce shortages and fragmented training models have contributed to inconsistent care delivery, particularly in Western provinces. This study evaluated an integrated model that combines simulation-based learning, structured supervision, and early clinical immersion to enhance training effectiveness. A convergent parallel mixed-methods design assessed the model’s impact on patient satisfaction, student interaction, and perceived maternal complications. Quantitative data were collected from 300 participants, including students, maternity nurses, clinical instructors, and postnatal patients, and analyzed using SPSS and partial least squares structural equation modeling (SmartPLS). Qualitative insights from 14 semi-structured interviews were analyzed thematically using NVivo. The integrated training model significantly predicted higher patient satisfaction (β = 0.863, R² = 0.743), aligning with Kolb’s Experiential Learning Theory. However, no significant relationships were found between student interaction, patient satisfaction, or perceived maternal complications. Critically, the staff training construct demonstrated weak psychometric validity (CR = 0.337; AVE = 0.126), warranting cautious interpretation of its statistical strength. Qualitative findings provided essential context, revealing fragmented implementation, unclear student roles, and institutional barriers such as emergency delays and staffing shortages. These systemic constraints help explain the absence of improvement in clinical outcomes despite enhanced satisfaction ratings. In conclusion, integrated vocational education can enhance perceived care quality, but its clinical impact depends on institutional readiness and supervisory fidelity. This study offers actionable insights for policy reforms under the Healthy China 2030 agenda and supports scalable models for maternal health training in low-resource settings.