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There is an urgent need for vaccines against Neisseria gonorrhoeae , the causative agent of gonorrhea. Vaccination with an outer membrane vesicle–based Neisseria meningitidis vaccine provides some protection from N. gonorrhoeae ; however, the mechanisms underlying this cross-protection are unknown. To address this need, we developed multiplexed bead-based assays for the relative quantification of human and mouse IgG and IgA against N gonorrhoeae antigens. The assays were evaluated for analyte independence, dilutional linearity, specificity, sensitivity, intra- and interassay variability, and robustness to sample storage conditions. The assay was then used to test samples from mice and humans immunized with an N meningitidis outer membrane vesicle vaccine. [ABSTRACT FROM AUTHOR]

Impaired intestinal homeostasis is a major pathological feature of inflammatory bowel diseases (IBD). Mannose and selenium (Se) both demonstrate potential anti-inflammatory and anti-oxidative properties. However, most lectin receptors bind free monosaccharide ligands with relatively low affinity and most Se species induce side effects beyond a very narrow range of dosage. This has contributed to a poorly explored therapies for IBD that combine mannose and Se to target intestinal epithelial cells (IECs) for normalization gut homeostasis. Herein, a facile and safe strategy for ulcerative colitis (UC) treatment was developed using optimized, mannose-functionalized Se nanoparticles (M-SeNPs) encapsulated within a colon-targeted hydrogel delivery system containing alginate (SA) and chitosan (CS). This biocompatible nanosystem was efficiently taken up by IECs and led to increased expression of Se-dependent glutathione peroxidases (GPXs), thereby modulating IECs' immune response. Using a mouse model of DSS-induced colitis, (CS/SA)-embedding M-SeNPs (C/S-MSe) were found to mitigate oxidative stress and inflammation through the inhibition of the NF-kB pathway in the colon. This stabilized mucosal homeostasis of IECs and ameliorated colitis-related symptoms, thereby providing a potential new approach for treatment of IBD. [ABSTRACT FROM AUTHOR]

Abnormal levels of catecholamine (CA) neurotransmitters and their metabolites in biological fluids can lead to various neurological disorders. Herein, a boric acid‐functionalized hypercrosslinked polymer was prepared and utilized as a sorbent for the dispersive solid‐phase extraction of CAs and their metabolites in rat serum. By combination with a high‐performance liquid chromatography‐fluorescence detector, the extraction parameters for the seven target analytes were optimized. Under the optimal extraction condition, the methodology for the quantitative analysis of CAs and their metabolites in rat serum samples was established. The limits of detection and limits of quantification were found to be in the ranges of 0.010–0.015 and 0.033–0.050 ng/mL, respectively. The results demonstrated satisfactory recoveries, with values ranging from 88.02% to 113.27%, accompanied by relative standard deviations within the range of 2.69%–9.59%. In addition, the method showed good anti‐interference ability (matrix effect ranged from 2.64% to 18.07%). The developed method was validated for the determination of CAs and their metabolites in normal and Alzheimer's disease model rats' serum, which proved the promising application of the method for CAs neurotransmitter analysis in biological samples. [ABSTRACT FROM AUTHOR]

Under the dual-carbon target, hydrogen energy, as a zero-carbon secondary energy source, has great scope for replacing fossil feedstocks in the fields of energy, transportation and industry. However, the current research on the competitiveness of hydrogen energy in various fields is not sufficiently addressed. In this paper, we use the LEAP model to predict the future scale of hydrogen use and the two-factor learning curve to predict the trend of hydrogen price change from 2025 to 2050, using Qinghai Province as the research background. At the same time, considering the carbon emission reduction benefits and raw material costs, the competitiveness of hydrogen energy in energy, transportation, industry and other fields in the future is compared. The results show that: 1) The hydrogen load scale in Qinghai Province will grow fast from 2025 to 2030. From 2030 to 2040, it slows under the steady and basic scenarios but remains high under the accelerated one. By 2040, the consumption scales are 1.057 million, 649,000 and 442,000 tons respectively. 2) The price of hydrogen energy will drop rapidly from the current 28 CNY/kg to about 20 CNY/kg in the next 5 years. By 2040, the price of hydrogen energy will be reduced to about 17 CNY/kg. 3) In terms of hydrogen energy competitiveness, when carbon emissions are not taken into account, hydrogen energy is currently competitive in the transportation field. During 2032-2038, it will be competitive in the field of methanol synthesis. By 2040, hydrogen energy will not be competitive in the fields of ammonia synthesis and power/heating. When considering carbon emissions, the competitiveness of hydrogen energy in the transportation field will become greater. The competitive year in the field of methanol synthesis will be 1-2 years ahead. By 2040, it will not be competitive in the field of synthetic ammonia and power/heating, but the gap will be significantly reduced due to the consideration of carbon emissions. [ABSTRACT FROM AUTHOR]

Metric-based meta-learning methods have demonstrated remarkable success in the domain of few-shot image classification. However, their performance is significantly contingent upon the choice of metric and the feature representation for the support classes. Current approaches, which predominantly rely on holistic image features, may inadvertently disregard critical details necessary for novel tasks, a phenomenon known as "supervision collapse". Moreover, relying solely on visual features to characterize support classes can prove to be insufficient, particularly in scenarios involving limited sample sizes. In this paper, we introduce an innovative framework named Patch Matching Metric-based Semantic Interaction Meta-Learning (PatSiML), designed to overcome these challenges. To counteract supervision collapse, we have developed a patch matching metric strategy based on the Transformer architecture to transform input images into a set of distinct patch embeddings. This approach dynamically creates task-specific embeddings, facilitated by a graph convolutional network, to formulate precise matching metrics between the support classes and the query image patches. To enhance the integration of semantic knowledge, we have also integrated a label-assisted channel semantic interaction strategy. This strategy merges word embeddings with patch-level visual features across the channel dimension, utilizing a sophisticated language model to combine semantic understanding with visual information. Our empirical findings across four diverse datasets reveal that the PatSiML method achieves a classification accuracy improvement of 0.65% to 21.15% over existing methodologies, underscoring its robustness and efficacy. [ABSTRACT FROM AUTHOR]

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models' average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%. [ABSTRACT FROM AUTHOR]

A diatomite-modified Fe2O3 (Fe2O3/Dia) catalyst was prepared to catalyze the ozonation degradation of sulfachloropyridazine sodium (SPDZ). The chemical oxygen demand (COD) was used as the index of pollutant degradation. The catalytic ozonation experiment showed that the COD removal rate of SPDZ was 87% under Fe2O3/Dia catalysis, which was much higher than that obtained when using Fe2O3 as the catalyst. The characteristics of the Fe2O3/Dia catalyst were investigated, and the successful synthesis of the Fe2O3/Dia composite catalyst was proved by XRD, XPS, SEM, FTIR, BET and other characterization methods. The catalytic mechanism of degradation by ozone with Fe2O3/Dia was analyzed. According to free-radical trapping experiments and an in situ electron paramagnetic spectrometer characterization analysis, the main oxidizing species in the catalytic Fe2O3/Dia ozone system is ·OH. The intermediates in the degradation process of SPDZ were detected and analyzed in detail by liquid chromatography-coupled mass spectrometry. The degradation mechanism and three degradation paths of SPDZ were proposed. [ABSTRACT FROM AUTHOR]

Integration of functional fillers into liquid metals (LM) induces rheology modification, enabling the free-form shaping of LM at the micrometer scale. However, integrating non-chemically modified low-dimensional materials with LM to form stable and uniform dispersions remain a great challenge. Herein, we propose a solvent-assisted dispersion (SAD) method that utilizes the fragmentation and reintegration of LM in volatile solvents to engulf and disperse fillers. This method successfully integrates MXene uniformly into LM, achieving better internal connectivity than the conventional dry powder mixing (DPM) method. Consequently, the MXene/LM (MLM) coating exhibits high electromagnetic interference (EMI) shielding performance (105 dB at 20 μm, which is 1.6 times that of coatings prepared by DPM). Moreover, the rheological characteristic of MLM render it malleable and facilitates direct printing and adaptation to diverse structures. This study offers a convenient method for assembling LM with low-dimensional materials, paving the way for the development of multifunctional soft devices. This work offers a convenient method (solvent-assisted dispersion) for assembling liquid metals with MXene and other low-dimensional materials, paving a path for the development of multifunctional soft devices. [ABSTRACT FROM AUTHOR]

The cultivation of microalgae from municipal wastewater, while simultaneously removing nutrients from the water column, has the potential to aid biodiesel production and carbon dioxide fixation, thereby alleviating the pressure of energy shortages. In this research, different ratios of sodium bicarbonate and glucose were used to prepare simulated municipal wastewater. The results demonstrated that microalgae were most effectively treated under one-stage direct treatment conditions. During direct culture, the most effective treatment was observed for IAA-3, which exhibited a dry weight of 1.4363 g/L and a lipid content of 25.05% after stimulation with 0.0005 M NaHCO3. In contrast, NaHCO3-2 demonstrated optimal performance during the secondary culture, with a dry weight of 1.6844 g/L and a lipid content of 18.05%. Finally, the economic, social and environmental benefits of direct treatment (IAA-3) and secondary treatment NaHCO3-2 were analyzed. The benefits of direct treatment were found to be USD 0.50989/L, while those of secondary treatment were USD 0.43172/L. For each tonne of municipal wastewater treated, the carbon sequestration benefits of IAA-3 during direct treatment and NaHCO3-2 during secondary treatment were USD 0.45645 and USD 0.85725, respectively. [ABSTRACT FROM AUTHOR]

In the global ecosystem, wetlands are vital carbon sinks, playing a crucial role in absorbing greenhouse gases such as carbon dioxide and mitigating global warming. Accurate estimation of wetland carbon content is essential for research on wetland carbon sinks. However, the carbon cycle of wetlands is complex, and the carbon sinking of wetlands is affected by climate, topography, water level conditions, vegetation types, soil types, and other factors. This has caused significant challenges in the estimation of wetland carbon sinks. In current studies, most research has focused on the impact of individual factors on wetland carbon sinks, often ignoring the interaction between various factors, which further leads to uncertainty in wetland carbon measurements. This paper aims to elucidate the process of the wetland carbon cycle, summarize the factors affecting wetland carbon sinks, and explore the interplay between various factors and their influence on wetland carbon sinks, aiming to provide theoretical support for the study of wetland carbon sinks. Additionally, this paper reviews the advantages and disadvantages of current wetland carbon measurement methods, proposes research directions for combining machine learning methods, identifies existing difficulties in current wetland carbon measurement, and offers suggestions to serve as a reference for future wetland carbon sink estimation and wetland management.Highlights: The potential and significance of wetland carbon sinks are explained. The advantages and disadvantages of current wetland carbon measurement techniques are summarized. The prospective method for integrating machine learning into integrated wetland carbon measurement is proposed. [ABSTRACT FROM AUTHOR]

According to the urgent low latency and the heavy computation tasks demands required for sixth‐generation (6G) wireless networks, the authors introduce the conventional resource allocation algorithms, including the game theory, artificial‐intelligence (AI) methods, and matching theory enabled framework, in which the multi‐access edge computing (MEC) scheme collaborative with the cloud platform to serve the primary users (PUs) and cognitive users (CUs) for next generation multiple access (NGMA). The proposed framework allows both the PUs and CUs to offload their computation tasks in a 6G‐enabled cognitive radio (CR) networks, so called cloud‐assisted CR‐MEC networks. In particular, the fundamentals of this conceived networks based on NGMA are first introduced. Hence, a number of methods based on the resource allocation algorithms are proposed in order to improve the quality of service for the mobile users, and reduce their transmission latency as well as the energy consumptions. Moreover, the motivations, challenges, and representative models for these conventional algorithms are described for integrated‐intelligent communication and computing aided NGMA networks. Furthermore, the open issues and future research directions for this conceived networks are summarised. [ABSTRACT FROM AUTHOR]