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Fishes are key indicators of ecosystem health and are important food sources for Indigenous peoples. Although fishes have been shown to ingest microplastics (MPs; plastic particles <5 mm long) in localities between the polar regions and equator, very few investigations focus on the drivers of microplastic pollution in Arctic fishes. We investigated MPs in 435 stomachs and gastrointestinal (GI) tracts of 7 freshwater fish species from the Canadian Arctic and tested for correlations with habitat, capture location, surrounding human population, and stomach/GI tract mass. Overall, an average 2.22 ± 3.51 MPs per fish was determined following blank and polymer composition normalizations. Northern pike (Esox lucius) contained the greatest average microplastic abundance (3.59 ± 5.09 MPs/fish), the highest percentage of individuals containing MPs (86%), and as a demersal species, contained significantly more MPs than pelagic and benthic fishes. Fishes captured from the Great Slave Lake location contained significantly more total MPs (mean 3.41 ± 4.40 MPs/fish) than fishes from four other studied locations, which may be explained by the greater human population around Great Slave Lake. Fish captured from lakes on the more remote and sparsely populated Cornwallis Island, however, contained significantly greater micro-fragment concentrations, which suggests that proximity to localized anthropogenic activities can influence microplastic concentrations. We found no relationship between stomach/GI tract mass and microplastic abundance. Our study provides a crucial baseline for long-term monitoring of MPs in Arctic fishes, as it assesses the main factors contributing to microplastic ingestion in over 400 freshwater fish and in multiple fish species at 18 capture sites.

Background: Lung cancer therapy resistance is often associated with the redox-regulatory nuclear factor erythroid 2-related factor-2 (NRF2)-Kelch-like ECH-associated protein1 (KEAP1) dysfunction.

Objective: This study investigated the impact of commercially available black tea (BT) phytochemicals (≥80 % theaflavins) from Camellia sinensis in sensitizing doxorubicin (Dox) against nonresponsive lung adenocarcinoma cells by modulation of non-canonical NRF2 regulators.

Methods: The methods included multidrug resistance (MDR) assay, comet assay, cell cycle analysis, zymography, semi-quantitative polymerase chain reaction, western blot and immunocytochemistry (ICC).

Results: BT pretreatment followed by Dox exposure was partially effective in Dox resistance-reversal in A549 cells by increasing drug uptake and downregulating MDR pumps. This combination induced DNA damage and cytotoxicity in A549 cells. It also reduced invasiveness and suppressed the expression of multidrug resistance protein-1, epidermal growth factor receptor, protein kinase B, and B cell lymphoma-2. In absence of wild-type KEAP1, non-KEAP1 regulators were thoroughly investigated by immunolocalization, and immunoblotting. BT restricted non-canonical NRF2 activators, such as p21 and apurinic/apyrimidinic endonuclease1 in A549 and acted oppositely in NCI-H23 cells. Additionally, NRF2-repressors, namely forkhead box O3, p53, glycogen synthase kinase-3β, and retinoid X receptor (RXR), were downregulated in NCI-H23 and upregulated in A549 cells. ICC exhibited that BT modulated the co-localization of NRF2 regulators, such as β-transducin repeat-containing protein and RXR, in A549 and NCI-H23 cells.

Conclusion: Therefore, it might be indicated that BT improved Dox retention and increased the Dox responsiveness in A549 cells. BT-mediated selective suppression of the NRF2, re-stabilized the KEAP-1-independent NRF2 regulators and made the non-responsive A549 cells partially sensitive to Dox.

Background: Existing knee osteoarthritis (KOA) severity classification methods typically rely on a combination of object detection algorithms and classification algorithms. However, this approach not only increases the computational burden and time costs but also reduces the efficiency of real-time diagnosis, which makes it difficult to meet the needs of practical applications. To address the performance limitations of KOA severity recognition models that operate without target detection algorithms, a deep transfer learning approach incorporating a novel activation function (AvRELU) was proposed. The goal was to improve classification performance, particularly for small sample datasets, while optimizing computational resources.

Methods: A dataset consisting of 3300 digital X-ray images of KOA patients was utilized. During model training and evaluation, five-fold cross-validation was used to assess robustness, and the dataset was divided into training, validation, and testing sets through a stratified sampling method at an 8:1:1 ratio. A transfer learning approach utilizing a pretrained Inception-v3 backbone was proposed, where Bayesian optimization automated both the fine-tuning process and downstream classifier construction. To improve the model performance, the activation function AvRELU was introduced in the network layer of the downstream model. Moreover, Kendall's tau-b correlation analysis was employed to evaluate the statistical significance of differences in the predicted Kellgren-Lawrence grades among the different methods.

Results: Using five-fold cross-validation, the proposed method achieved an average test set performance of 95% accuracy, 95% F1-score, and 93% kappa. These results demonstrated the superior performance of the method in KOA severity recognition.

Conclusions: The method proposed here not only significantly improves model performance on small-sample datasets but also maintains the model's lightweight and low-resource characteristics. Moreover, it achieves better performance in KOA severity recognition than most existing methods.

Limiting biological invasions is essential for conserving biodiversity and sustainable future. Preventing human-vectored introductions, especially into ecologically important areas, is recognized as critical; however, quantitative evidence on effective interventions, including those targeting behavioral change, is scarce. Moreover, beyond preventing biological invasion, the impact of the medium through which behavioral interventions are delivered on their effectiveness remains poorly understood in conservation. Here using the case of seed introduction into mountainous regions via footwear, we compared the effectiveness of behavioral interventions through cleaning stations equipped with five types of footwear-cleaning tools with overall participants. Specifically, we adopted the RE-AIM framework and evaluate effectiveness of the tools from three dimensions. While the same behavioral messages and intervention devices were used in all cases, the proportion of seeds removed using the cleaning station and the proportion of visitors who spontaneously used the cleaning station varied substantially across the tools used in the cleaning station. This resulted in significant differences in the proportion of seed interception by visitors' voluntary use of the cleaning station. Notably, cleaning stations equipped with a side-brushed scrubber showed the highest performance, intercepting an estimated 55% of seeds that would otherwise be introduced, even without enforcement. Moreover, the intention to use cleaning tools differed among tools, and was primarily constrained by perceived temporal and mental burdens. Our results highlighted the importance of medium in the conservation behavioral interventions. We also suggest utility of the framework in conservation for evaluating and reporting intervention effectiveness, and identifying priority areas for improvement.

Oily wastewater poses significant ecological hazards, necessitating the development of effective, reusable, and environmentally friendly sorbent materials for efficient oil/water separation. Here, we report the fabrication of a superhydrophobic aerogel composed of gellan gum (GG), konjac glucomannan (KGM), and bamboo fiber (BF), cross-linked with 1,4-butanediol diglycidyl ether (BDGE), and engineered into a hydrophobic cuttlebone-like structure via directional freeze-drying and vapor-phase deposition. The resulting superhydrophobic aerogel (H-GG/KGM/BF) exhibits a porosity of 98.46% and a density of 0.02354 g·cm^-3, with an excellent sorption capacity ranging from 18.08 to 75.66 g·g^-1, and it undergoes complete biodegradation within 3 weeks. It demonstrates superhydrophobicity and oil affinity, attributable to its porous lamellar architecture and S-shaped cross-section pillars, which also confer high compressibility and durability over 30 compression cycles at 30% strain. In addition to oil sorption, the material enables continuous oil/water separation with an efficiency of 98.7% and a flux exceeding 4700 L·m^-2·h^-1, highlighting its potential as a reusable sorbent for environmental remediation.

Selective attention is essential for cognitive and behavioral self-regulation. However, the association between lead exposure and selective attention remains unclear. We examined the association between blood lead levels and selective attention, and evaluated whether this association is influenced by sleep duration. We used data from a prospective cohort of 377 Korean children. Blood lead concentrations and Stroop Color and Word Test (SCWT) scores were repeatedly measured at 6, 8, and 10 years of age. Generalized propensity scores (GPSs) were generated using linear regression models predicting lead levels. Associations between lead levels and SCWT scores were assessed using causal inference approaches, such as linear mixed models adjusted for both GPS and potential confounders, as well as doubly robust estimation models. In models adjusted for both GPS and potential confounders, a doubling of lead levels was associated with lower color [β = -1.46, 95 % confidence interval (CI): -2.63, -0.30] and color-word (β = -1.52, 95 % CI: -3.00, -0.04) test scores. In doubly robust models, these associations persisted for the color (β = -1.35, 95 % CI: -2.36, -0.34) and color-word (β = -1.33, 95 % CI: -2.61, -0.04) test scores. The associations varied by sleep duration, with stronger effects observed among children sleeping ≤ 8 h compared with those sleeping longer. By applying multiple causal inference approaches, this study provides robust evidence that lead exposure impairs selective attention in school-age children. The detrimental associations were amplified among those sleeping ≤ 8 h, suggesting that sufficient sleep may mitigate the neurotoxic effects of lead exposure.

In recent years, nitrous oxide (N₂O) has emerged as a critical target for mitigation owing to its exceptional stability and significant potential to cause global warming. Conventional abatement technologies, including thermal, catalytic, and plasma-based technologies, are currently limited by economic and scalability constraints, hindering their practical deployment. In this study, we developed an electron beam (EB) radiolysis approach to decompose N₂O under ambient conditions. Utilizing an absorbed dose of 50 kGy, we achieved over 90 % decomposition of the 100 ppm N₂O in both N₂ and Ar backgrounds. However, oxygen inhibits radiolytic N₂O decomposition by facilitating the reformation of N₂O through reactions involving N₂- and O₂-derived reactive species. While net N₂O formation was observed from N₂ and O₂ mixtures, the introduction of initial N₂O effectively suppressed this process, demonstrating a distinct kinetic advantage for N₂O decomposition. Therefore, net N₂O decomposition was achieved when N₂O and O₂ concentrations were equivalent. The inhibitory effect of oxygen diminished at higher absorbed doses, indicating that elevated irradiation energies favor direct electron impact decomposition of N₂O over indirect pathways mediated by reactive species. These findings establish EB radiolysis as an efficient and scalable strategy for N₂O conversion. Moreover, they provide a clear scientific basis for addressing the challenges posed by oxygen in practical applications.

The development of bifunctional catalysts that combine photocatalytic pollutant degradation and hydrogen peroxide synthesis provides an effective strategy for dealing with environmental pollution and energy problems. Herein, a S-doped graphitic carbon nitride photocatalyst containing N-vacancies was synthesized by hydrothermal and calcination methods. The introduction of sulfur effectively modulated the valence band position of graphitic carbon nitride, thus enhancing its absorption efficiency of visible light. N vacancies in g-C₃N₄ not only effectively trap localized electrons to promote photogenerated electron-hole (e^--h⁺) separation (forming localized states) but also introduce impurity energy levels in the band structure to enhance the adsorption and activation of reactive oxygen species. PL spectroscopy confirms that S doping and N vacancies generate active sites for charge trapping, suppressing e^--h⁺ recombination. The synthesized SCN-E materials exhibit excellent bifunctional photocatalytic performance: (1) photocatalytic degradation of tetracycline antibiotics-degradation rates of tetracycline hydrochloride (TC), oxytetracycline (OTC), and chlortetracycline (CTC) reach ∼100%, 92%, and 94% within 30 min under visible light irradiation; (2) photocatalytic H₂O₂ synthesis-a yield of 1183.54 μmol L^-1·h^-1 is achieved after 30 min of O₂ bubbling. In addition, the biotoxicity of the catalytically degraded antibiotic solutions to E. coli DH5α was basically eliminated.

Rising carbon emissions have intensified global climate change, creating an urgent need for innovative solutions that generate value while also reducing emissions. Carbon capture, conversion, and utilization (CCCU) is a transformational technique that captures and converts CO₂ from energy and industrial sources into valuable fuels, chemicals, and materials. This review examines the current state of CCCU technologies, highlighting innovative materials including solvents, solid sorbents, and membranes, as well as main CO₂ capture methodologies like pre-combustion, post-combustion, and oxy-fuel combustion. Emerging conversion technologies include photocatalysis, electrocatalysis, and biochemical pathways, with an emphasis on the synthesis of methanol, dimethyl carbonate (DMC), dimethyl ether (DME), urea, and formic acid. The role of nanomaterials and bio-inspired systems in enhancing conversion efficiency is also explored. Industrial case studies and life-cycle assessments demonstrate the economic and environmental viability of CCCU, particularly when paired with renewable energy sources such as green hydrogen. Despite promising progress, CCCU still faces technical, economic, and infrastructural challenges related to energy consumption, scalability, and policy support. Looking to the future, research should focus on creating hybrid systems that can combine capture and conversion in a single process, developing more advanced catalysts, designing flexible modular reactors, and improving efficiency using machine learning. CCCU can be unlocked to its full potential by integrating it into circular economy frameworks and industrial symbiosis models. CCCU promotes decarbonization by transforming CO₂ waste into a valuable resource. This aligns economic growth with environmental responsibility and fosters sustainable development. This review focuses on the commercial viability of CCCU. The conference emphasized the critical importance of technological innovation and strategic implementation in establishing renewable energy as the foundation for a low-carbon, climate-resilient future.