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This study advances concrete workability prediction by integrating ensemble learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), and gradient boosted regression trees (GBRTs), and XGBoost showing superior accuracy. Using Shapley additive explanations (SHAPs), it identifies key factors such as the S/log(W) ratio and aggregate components affecting concrete performance. Theoretically, this enhances the predictive analytics framework in material science, while practically, it improves construction efficiency by optimizing material use and reducing physical testing. The development of a user‐friendly graphical user interface (GUI) makes these models accessible for industry application. Addressing gaps in model accuracy and interpretability, the research contributes to sustainable construction practices and underscores the need for future expansions in data diversity and computational optimization in the construction industry. [ABSTRACT FROM AUTHOR]

Glutathione (GSH)-switched fluorescent assays have received attention due to their rapid signal changes of fluorescent nanoprobes. Small polydopamine nanodots (PDs) exhibit remarkable photoluminescent properties. In this work, fluorescent PDs were synthesized through oxidation using dopamine as precursor. The prepared PDs had rich surface functionalities and exhibited good optical properties with maximum emission using 330 nm excitation. The fluorescence was quenched by Ag+ through the interaction with catechols in the PDs. After the addition of GSH, Ag was bound with sulfhydryl groups in GSH and the fluorescence of the PDs was restored. Therefore, a fluorescence turn on-off-on strategy was constructed for Ag+ and GSH using the synthesized PDs as the probe. The PDs showed good performance for Ag+ from 0.01 to 1 μM, with a limit of detection (LOD) equal to 2.82 nM. The system also exhibited a linear relationship for GSH from 0.01 to 0.5 μM with a LOD equal to 1.93 nM. GSH was determined in human serum to demonstrate a practical application of the assay. This work expands the application of PDs in biological analysis. [ABSTRACT FROM AUTHOR]

Affinity assays allow direct detection of DNA methylation events without requiring a special sequence. However, the signal amplification of these methods heavily depends on nanocatalysts and bioenzymes, making them suffer from low sensitivity. In this work, alkaline phosphatase (ALP)-assisted chemical redox cycling was employed to amplify the sensitivity of fluorescence affinity assays for DNA methylation detection using Ru@SiO2@MnO2 nanocomposites as fluorescent probes. In the ALP-assisted chemical redox cycling reaction system, ALP hydrolyzed 2-phosphate- L -ascorbic acid trisodium salt (AAP) to produce AA, which could reduce MnO2 nanosheets to form Mn2+, making the fluorescence recovery of Ru@SiO2 nanoparticles possible. Meanwhile, AA was oxidized to dehydroascorbic acid (DHA), which was re-reduced by tris(2-carboxyethyl) phosphine (TCEP) to trigger a redox cycling reaction. The constantly generated AA could etch large amounts of MnO2 nanosheets and greatly recover Ru@SiO2 fluorescence, amplifying the signal of the fluorescence assay. Employing the proposed ALP-assisted chemical redox cycling signal amplification strategy, a sensitive affinity assay for DNA methylation detection was achieved using ALP encapsulated liposomes that were linked with the 5mC antibody (Ab) to bind with methylated sites. A detection limit down to 2.9 fM was obtained for DNA methylation detection and a DNA methylation level as low as 0.1% could be distinguished, which was superior to conventional affinity assays. Moreover, the affinity assays could detect DNA methylation more specifically and directly, implying their great potential for the analysis of tumor-specific genes in liquid biopsy. [ABSTRACT FROM AUTHOR]

Changes in Sn2+ and glycine levels are relevant to many important physiological procedures in human health. However, investigation of their physiological functions is limited because few versatile methods towards Sn2+ and glycine detection have been developed. In this work, a fluorescence turn on–off-on strategy was firstly constructed for rapid and sensitive detection of Sn2+ and glycine through the specific binding between Sn2+ and glycine. Carbon nanodots (CDs) with a quantum yield of 19.5% were synthesized by utilizing inner film of waste eggshell as carbon source and employed as fluorescent probe. In the presence of Sn2+, the fluorescence of CDs was quenched by Sn2+ via the primary inner filter effect (IFE). However, the binding between Sn2+ and glycine prevented the IFE between Sn2+ and CDs, resulting in fluorescence recovery of CDs. Under optimized conditions, the fluorescent response of CDs displayed good linear relationships with the concentrations of Sn2+ in the range of 10–200 µM and 200–5000 µM, and the limit of detection (LOD) was 2.4 µM. For glycine detection, a good linear relationship was obtained in the concentration range of 5–1000 µM with a low LOD down to 0.76 µM. Moreover, the practicability of the assay was also demonstrated by measuring glycine content in human serum samples. This work provides an economical, green and fast method for biological analysis of Sn2+ and glycine. [ABSTRACT FROM AUTHOR]

Herein, a green, economical, and waste-utilization approach is reported for the synthesis of water-soluble carbon nanodots (C-Dots) with a high fluorescence quantum yield of 19.5%. As a common protein-rich waste, eggshell membrane was selected as a cost-effective and ideal precursor to prepare C-Dots using the microwave method. The as-prepared C-Dots showed a maximum emission at 375 nm with an excitation wavelength at 235 nm. The fluorescent C-Dots were adopted as a sensitive probe for the rapid detection of Hg2+ and glutathione (GSH) based on the fluorescence off and on (turn-off–on) strategy. This was ascribed to the fact that Hg2+ could effectively quench the fluorescence of the C-Dots and GSH was able to prevent fluorescence quenching owing to the specific binding between Hg2+ and GSH. The designed method exhibited a high sensitivity and selectivity towards the detection of Hg2+ and GSH. Under the optimized conditions, the method showed a good linear relationship with Hg2+ concentration in the range from 100 nM to 50 μM with a detection limit of 32.0 nM. For GSH detection, it displayed a linear range from 50 nM to 10 μM with a detection limit of 9.8 nM. Moreover, this method was successfully applied to detect GSH in human serum samples. The eggshell derived fluorescent C-Dots pave the way for economical environmental and biological analyses. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) poses a significant threat to forests due to its high infectivity and lethality. The absence of an effective treatment underscores the importance of timely detection and isolation of infected trees for effective prevention and control. While deep learning techniques combined unmanned aerial vehicle (UAV) remote sensing images offer promise for accurate identification of diseased pine trees in their natural environments, they often demand extensive prior professional knowledge and struggle with efficiency. This paper proposes a detection model YOLOv5L-s-SimAM-ASFF, which achieves remarkable precision, maintains a lightweight structure, and facilitates real-time detection of diseased pine trees in UAV RGB images under natural conditions. This is achieved through the integration of the ShuffleNetV2 network, a simple parameter-free attention module known as SimAM, and adaptively spatial feature fusion (ASFF). The model boasts a mean average precision (mAP) of 95.64% and a recall rate of 91.28% in detecting pine wilt diseased trees, while operating at an impressive 95.70 frames per second (FPS). Furthermore, it significantly reduces model size and parameter count compared to the original YOLOv5-Lite. These findings indicate that the proposed model YOLOv5L-s-SimAM-ASFF is most suitable for real-time, high-accuracy, and lightweight detection of PWD-infected trees. This capability is crucial for precise localization and quantification of infected trees, thereby providing valuable guidance for effective management and eradication efforts., 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., (Copyright © 2024 Du, Wu, Wen, Zheng, Lin and Wu.)

Herein, a green, economical, and waste-utilization approach is reported for the synthesis of water-soluble carbon nanodots (C-Dots) with a high fluorescence quantum yield of 19.5%. As a common protein-rich waste, eggshell membrane was selected as a cost-effective and ideal precursor to prepare C-Dots using the microwave method. The as-prepared C-Dots showed a maximum emission at 375 nm with an excitation wavelength at 235 nm. The fluorescent C-Dots were adopted as a sensitive probe for the rapid detection of Hg 2+ and glutathione (GSH) based on the fluorescence off and on (turn-off-on) strategy. This was ascribed to the fact that Hg 2+ could effectively quench the fluorescence of the C-Dots and GSH was able to prevent fluorescence quenching owing to the specific binding between Hg 2+ and GSH. The designed method exhibited a high sensitivity and selectivity towards the detection of Hg 2+ and GSH. Under the optimized conditions, the method showed a good linear relationship with Hg 2+ concentration in the range from 100 nM to 50 μM with a detection limit of 32.0 nM. For GSH detection, it displayed a linear range from 50 nM to 10 μM with a detection limit of 9.8 nM. Moreover, this method was successfully applied to detect GSH in human serum samples. The eggshell derived fluorescent C-Dots pave the way for economical environmental and biological analyses.