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Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as spectroscopy, chromatography, and electrochemical techniques. However, traditional testing methods require the utilization of laboratory equipment, which is large and not suitable for real-time testing in the field. Microfluidic devices can overcome the limitations of traditional testing instruments and have become an efficient and convenient tool for water quality analysis. At the same time, artificial intelligence is an ideal means of recognizing, classifying, and predicting data obtained from microfluidic systems. Microfluidic devices based on artificial intelligence and machine learning are being developed with great significance for the next generation of water quality monitoring systems. This review begins with a brief introduction to the algorithms involved in artificial intelligence and the materials used in the fabrication and detection techniques of microfluidic platforms. Then, the latest research development of combining the two for pollutant detection in water bodies, including heavy metals, pesticides, micro- and nanoplastics, and microalgae, is mainly introduced. Finally, the challenges encountered and the future directions of detection methods based on industrial intelligence and microfluidic chips are discussed. [ABSTRACT FROM AUTHOR]

The clinical heterogeneity of early-stage endometrial cancer (EC) is worthy of further study to identify high-quality prognostic markers and their potential role in aggressive tumor behavior. Mutation of TP53 was considered as an important primary triage in modified molecular typing for EC, it still cannot precisely predict the prognosis of EC. After proteomic analysis of cancer and para-cancerous tissues from 24 early-stage endometrioid EC patients with different survival outcomes, 13 differentially expressed proteins were screen out while 2 proteins enriched in p53 signaling pathway were further identified by single-cell transcriptome (scRNA-seq). Interestingly, tumor necrosis factor type-1 receptor-associated protein (TRAP1) and calmodulin-regulated spectrin-associated protein family member 3 (CAMSAP3) were found to be significantly downregulated in the specific cell cluster. Expectedly, the signature genes of TRAP1low/CAMSAP3low cluster included classical oncogenes. Moreover, close cellular interactions were observed between myeloid cells and the TRAP1low/CAMSAP3low cluster after systematically elucidating their relationship with tumor microenvironment (TME). The expression of TRAP1 and CAMSAP3 was verified by immunohistochemistry. Thus, a novel prediction model combining TRAP1, CAMSAP3 and TP53 was construct by multi-omics. Compared with the area under the curve, it demonstrated a significantly improvemrnt in the diagnostic efficacy in EC patients from TCGA bank. In conclusion, this work improved the current knowledge regarding the prognosis of early-stage EC through proteomics and scRNA-seq. These findings may lead to improvements in precise risk stratification of early-stage EC patients. [ABSTRACT FROM AUTHOR]

Background: With the increasingly serious environmental pollution and natural environment damage, renewable energy such as solar cells have gradually become the key to change this situation. Therefore, the local abnormal diagnosis of the charge and discharge of solar cell capacitors is particularly important. Objective: To extend the life of ultracapacitors by resolving the issue of their low detection rate and enhancing the capacity to recognize fault diagnosis factors. A novel approach to charging and discharging, as well as the diagnosis of local anomalies, is put forth, utilizing switching networks. Methods: By controlling the capacitors of multiple solar cells and supercapacitors to work together, it is possible to compensate for the shortage of photovoltaic power. The performance of fault diagnosis is optimized by combining principal component analysis and binary K-means clustering, which completes the fault diagnosis of capacitors. Results: The experimental results show that the research method can increase the maximum output power of photovoltaic by 32.9% under multi-layer shadows. In the charging state of the training set, the number of abnormal capacitors is 6, and the number of normal capacitors is 12, and both of them are in accordance with the preset value. The number of abnormal capacitors and normal capacitors in the discharge state is the same as that in the charging state, which is also 6 and 12. Conclusion: The research method can effectively address the issue of unbalanced energy storage battery packs and minimize the impact of local shadows on photovoltaic systems. In comparison to fuzzy C-means clustering, this method requires fewer iterations, enables faster fault diagnosis, and produces more accurate clustering results. It can provide technical support for diagnosing local abnormalities in the charging and discharging of solar cell capacitors. [ABSTRACT FROM AUTHOR]

This study aimed to explore the effectiveness of school‐based mindfulness training on learning burnout of Chinese middle school students and the mediating role of psychological resilience between dispositional mindfulness and learning burnout. A cluster randomized controlled trial was conducted with repeated measurement analysis, in which 62 Chinese middle school students in the experimental group received a 10‐week school‐based mindfulness training. In comparison, 63 students in the control group were randomized to receive no intervention. Learning burnout, dispositional mindfulness, and psychological resilience were measured by Adolescent Student Burnout Inventory, Five Facet Mindfulness Questionnaire, and Resilience Scale for Chinese Adolescents, respectively. The results showed: (1) Compared with the experimental group's pre‐test scores and the control group's pretest and posttest scores, the posttest scores of learning burnout in the experimental group significantly decreased. In contrast, the posttest scores of dispositional mindfulness and psychological resilience in the experimental group significantly increased. Meanwhile, the control group gained no significant changes between the pre‐and posttest in learning burnout, dispositional mindfulness, and psychological resilience. (2) Dispositional mindfulness was positively related to psychological resilience, while dispositional mindfulness and psychological resilience were negatively associated with learning burnout. (3) The direct effect of dispositional mindfulness on learning burnout and the mediating effect of psychological resilience between dispositional mindfulness and learning burnout were significant. This study suggested that school‐based mindfulness training distinctly improves dispositional mindfulness and psychological resilience and alleviates learning burnout in Chinese middle school students. Furthermore, psychological resilience plays a mediating role between dispositional mindfulness and learning burnout. Practitioner Points: School‐based mindfulness training is an effective intervention method to alleviate learning burnout of Chinese middle school students.Dispositional mindfulness is a protective factor of learning burnout, and psychological resilience plays a mediating role between dispositional mindfulness and learning burnout.School‐based mindfulness training alleviates learning burnout by improving dispositional mindfulness and psychological resilience. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) are ubiquitous in marine environments, and seawater desalination releases large amounts of concentrated saline water. However, little is known about how MPs alter their adsorption behavior towards other pollutants in high-salinity environments. Meanwhile, there is a lack of knowledge about the combined effect of MPs and high salinity on marine phytoplankton. In this work, the impact of high salinity on the adsorption behaviors of two types of MPs (polyethylene MPs (PE-MPs) and polyvinyl chloride MPs (PVC-MPs)) towards three typical water pollutants (Pb2+, 4-chlorophenol, and levofloxacin) was investigated, and the combined effect of MPs and high salinity on Synechococcus was evaluated. The adsorption of Pb2+ and levofloxacin by PE-MPs and PVC-MPs decreased at high salinity, and the adsorption of 4-chlorophenol by PE-MPs also decreased, however, the adsorption of 4-chlorophenol by PVC-MPs increased. The SOD, CAT activities and GSH content of Synechococcus exposed to MPs were increased by the increasing salinity. When the MPs concentration was low, the increase in salinity decreased the content of chlorophyll-a. Our results demonstrated that high salinity significantly changes the adsorption behavior of common pollutants onto MPs and alters the toxic effect of MPs on Synechococcus. This study provides important information necessary for environmental risk assessments with regard to the combined stress of MPs and high salinity, promoting the sustainable development of desalination industries. [ABSTRACT FROM AUTHOR]

Crack detection at the pixel level across complex scenarios (structural interference and adverse working conditions) is a critical consideration in the maintenance of ballastless track slab (BTS). Although existing deep learning models achieve acceptable accuracy on cracks with a monotonous background, the ground truth with high labor cost is inevitable and their performance in complex scenarios may fall far below their theoretical bounds or even cause "all-black images." A hybrid algorithm based on synthetic data from digital twin model and weakly supervised style transfer is proposed in this paper for addressing the above challenges. The algorithm uses a region-attention strategy to enable the uncontrolled generative adversarial network (GAN) focusing its attention on weak labels containing crack regions, directly obtaining segmentation results with the same style as the ground truth of the crack forest dataset. In addition, a digital twin model that can simulate the real inspection working conditions is established to generate a synthetic crack dataset, enabling the hybrid algorithm to extract the most discriminative features. The results show that the performance of the hybrid algorithm on inspection images across complex scenarios is nearly 25% higher than that of the DeepLabv3+ network, while the time cost consumed is only 0.5% of the latter. The deployment of the region attention strategy also enables the hybrid algorithm to achieve a mean intersection of union (MIoU) of 79.38%, which is nearly twice as much as that of GAN. It not only eliminates the oversegmentation caused by structures such as rails and fastener systems but also overcomes "all-black images." In addition, synthetic data can greatly enlarge the range, type, and number of discriminative crack features compared with data augmentation based on limited real data, thus enhancing the performance of the hybrid algorithm for uncertain inspection data. Particularly, the fully trained hybrid algorithm based on the synthetic dataset shows good adaptability and generalization to adverse working conditions such as uneven lighting, noise, and blur. [ABSTRACT FROM AUTHOR]

In this study, we report on how to design efficient catalysts for glucose oxidation via the transitional metal doping of nanohybrids of polyoxometalates (POMs) and metal-organic frameworks (MOFs). ZIF-67, a cobalt-based MOF, as well as phosphomolybdic acid (PMo), were used as precursors for the fabrication of pyrolyzed PMo@ZIF-67 (T-PMo@ZIF-67). A different amount of Ni2+ was doped into PMo@ZIF-67 to produce NixCoy@T-PMo@ZIF-67. Among them, Ni2Co2@T-PMo@ZIF-67 had the best performance. The power density of the fuel cell that used Ni2Co2@T-PMo@ZIF-67 as an anode catalyst was 3.76 times that of the cell that used active carbon as an anode catalyst. SEM and EDS mapping results indicate that Ni2Co2@T-PMo@ZIF-67 has a spherical structure and rough surface, and elements such as cobalt, nickel, and molybdenum are evenly distributed. XRD characterization indicates that Co3O4, CoMoO4, CoNiO4, and MoNiO4 co-exist in the composites. It is supposed that Co2+, Mo6+, and Ni2+ in the composites may have synergistic effects on the catalytic oxidation of glucose. [ABSTRACT FROM AUTHOR]

Glucose oxidation reaction is kinetically sluggish and demands a highly efficient catalyst. Herein, we report a direct glucose alkaline fuel cell (DGAFC) using physically mixed Ni2Co nanoparticles and reduced graphene oxide (Ni2Co-rGO) supported on nickel foam as an anode to promote glucose oxidation. The DGAFC with the Ni2Co-rGO anode delivered a remarkably enhanced peak power density of 40.44 W/m2 at room temperature, which was 74.91% higher than the control (23.12 W/m2). Different techniques including XPS, SEM, linear sweep voltammetry, and electrochemical impedance spectroscopy were used to explore the physiochemical and electrochemical properties of the catalyst. Our results demonstrate that Ni2Co-rGO exhibits an excellent activity toward glucose oxidation in alkaline medium, leading to a drastic improvement in the anode performance. There should be a synergistic effect between rGO and Ni2Co. In the Ni2Co-rGO nanocomposite, rGO not only acts as a support for the Ni2Co nanoparticles and also promotes the electron transfer during the glucose oxidation process. This study can promote the development of bi-transition metal-based catalysts for DGAFCs. [ABSTRACT FROM AUTHOR]

The pyrolysis of metal-organic frameworks (MOFs) is a popular strategy for the synthesis of nanoporous structures. Polymetallic oxides (POMs) are a class of polyhedral structural compounds with unique physicochemical properties. Little effort has been paid to evaluate MOF-POM hybrid-derived materials for peroxomonosulfate (PMS) activation. In this study, a cobalt-based MOF, ZIF-67, together with three types of POMs (phosphomolybdic acid, silicotungstic acid, and phosphotungstic acid), were used as precursors for the synthesis of PMS activation catalyst via pyrolysis. Three T-POMs@ZIF-67 nanohybrids (T-PMo@ZIF-67, T-SiW@ZIF-67, and T-PW@ZIF-67) were obtained by pyrolyzing the prepared precursors at 500 °C. Furthermore, the prepared T-POMs@ZIF-67 nanomaterials were evaluated for the catalytic activation of PMS in the degradation of levofloxacin (LEV). The results showed that the LEV degradation rate could reach 91.46% within 30 min under the optimized conditions when T-PW@ZIF-67 was used as the PMS activation catalyst. The catalytic efficiency of the catalyst decreased by only 9.63% after five cycles, indicating that the material has good stability. This work demonstrates the great potential of POMs@MOF derivatives for application in the field of wastewater treatment. [ABSTRACT FROM AUTHOR]

Freshwater scarcity and microplastics (MPs) pollution are two concerning and intertwined global challenges. In this work, we propose a "one stone kills two birds" strategy by employing an interfacial solar evaporation platform (ISEP) combined with a MPs adsorbent. This strategy aims to produce clean water and simultaneously enhance MPs removal. Unlike traditional predecessors, our ISEP generates condensed water free from MPs contamination. Additionally, the photothermally driven interfacial separation process significantly improves the MPs removal performance. We observed a removal ratio increase of up to 5.5 times compared to previously reported MPs adsorbents. Thus, our rationally-designed ISEP holds promising potential to not only mitigate the existing water scarcity issue but also remediate MPs pollution in natural water environments., (© 2024. The Author(s).)

Microplastics (MPs) have been widely found in the environment and have exerted non-negligible impacts on the environment and human health. Extensive research has shown that MPs can act as carriers for viruses and interacts with them in various ways. Whether MPs influence the persistence, transmission and infectivity of virus has attracted global concern in the context of increasing MPs contamination. This review paper provides an overview of the current state of knowledge regarding the interactions between MPs and viruses in aquatic environments. Latest progress and research trends in this field are summarized based on literature analysis. Additionally, we discuss the potential risks posed by microplastic-associated viruses to human health and the environmental safety, highlighting that MPs can affect viral transmission and infectivity through various pathways. Finally, we underscores the need for further research to address key knowledge gaps, such as elucidating synergistic effects between MPs and viruses, understanding interactions under real environmental conditions, and exploring the role of biofilms in virus-MPs interactions. This review aims to contribute to a deeper understanding on the transmission of viruses in the context of increasing MPs pollution in water, and promote actions to reduce the potential risks., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Wang, Zheng, Wang, Hou, He, Wang, Zhang, Chen and Liu.)

The clinical heterogeneity of early-stage endometrial cancer (EC) is worthy of further study to identify high-quality prognostic markers and their potential role in aggressive tumor behavior. Mutation of TP53 was considered as an important primary triage in modified molecular typing for EC, it still cannot precisely predict the prognosis of EC. After proteomic analysis of cancer and para-cancerous tissues from 24 early-stage endometrioid EC patients with different survival outcomes, 13 differentially expressed proteins were screen out while 2 proteins enriched in p53 signaling pathway were further identified by single-cell transcriptome (scRNA-seq). Interestingly, tumor necrosis factor type-1 receptor-associated protein (TRAP1) and calmodulin-regulated spectrin-associated protein family member 3 (CAMSAP3) were found to be significantly downregulated in the specific cell cluster. Expectedly, the signature genes of TRAP1 low /CAMSAP3 low cluster included classical oncogenes. Moreover, close cellular interactions were observed between myeloid cells and the TRAP1 low /CAMSAP3 low cluster after systematically elucidating their relationship with tumor microenvironment (TME). The expression of TRAP1 and CAMSAP3 was verified by immunohistochemistry. Thus, a novel prediction model combining TRAP1, CAMSAP3 and TP53 was construct by multi-omics. Compared with the area under the curve, it demonstrated a significantly improvemrnt in the diagnostic efficacy in EC patients from TCGA bank. In conclusion, this work improved the current knowledge regarding the prognosis of early-stage EC through proteomics and scRNA-seq. These findings may lead to improvements in precise risk stratification of early-stage EC patients., (© 2024. The Author(s).)