Performance Analysis of Ferronickel Slag-Ordinary Portland Cement Pervious Concrete.

It is unknown whether Ferronickel slag (FNS)-ordinary Portland cement (OPC)-based pervious concrete (FOPC) is feasible. To this end, a feasibility study was conducted on FOPC. Firstly, a detailed microscopic examination of the FNS powder was conducted, encompassing analyses of its particle size distribution, SEM, EDS, and chemical composition. These analyses aimed to establish the suitability of a composite of FNS and OPC as a composite cementitious material. Subsequent experimentation focused on evaluating the compressive strength of the composite paste material with varying mixed proportions, revealing a slight reduction in strength as the FNS substitution rate increased. Furthermore, the study designed eighteen different mix proportions of FOPC to investigate the key physical properties, including porosity, density, compressive strength, and the coefficient of permeability. Findings indicated that increases in the cementitious material proportion correlate with enhanced concrete strength, where the ratio of cementitious to aggregate increased by 6.7% and 16.5%, and the strength of FOPC increased by 10-13% and 30-50%, respectively. Conversely, a rise in the FNS substitution rate led to a reduction in compressive strength across different mix ratios. Additionally, the ratio of paste material to aggregate was found to significantly influence the permeability coefficient. These comprehensive performance evaluations suggest that incorporating FNS into OPC for pervious concrete applications is a feasible approach, offering valuable insights for the promotion of waste reuse and the advancement of energy conservation and emissions reduction efforts.

Development of a Repair Enzyme Fluorescent Probe to Reveal the Intracellular DNA Damage Induced by Benzo[a]pyrene in Living Cells.

Pollutant exposure causes a series of DNA damage in cells, resulting in the initiation and progression of diseases and even cancers. An investigation of the DNA damage induced by pollutants in living cells is significant to evaluate the cytotoxicity, genotoxicity, and carcinogenicity of environmental exposure, providing critical insight in the exploration of the etiologies of diseases. In this study, we develop a repair enzyme fluorescent probe to reveal the DNA damage caused by an environmental pollutant in living cells by single-cell fluorescent imaging of the most common base damage repair enzyme named human apurinic/apyrimidinic endonuclease 1 (APE1). The repair enzyme fluorescent probe is fabricated by conjugation of an APE1 high affinity DNA substrate on a ZnO2 nanoparticle surface to form a ZnO2@DNA nanoprobe. The ZnO2 nanoparticle serves as both a probe carrier and a cofactor supplier, releasing Zn2+ to activate APE1 generated by pollutant exposure. The AP-site in the DNA substrate of the fluorescent probe is cleaved by the activated APE1, releasing fluorophore and generating fluorescent signals to indicate the position and degree of APE1-related DNA base damage in living cells. Subsequently, the developed ZnO2@DNA fluorescent probe is applied to investigate the APE1-related DNA base damage induced by benzo[a]pyrene (BaP) in living human hepatocytes. Significant DNA base damage by BaP exposure is revealed, with a positive correlation of the damage degree with exposure time in 2-24 h and the concentration in 5-150 µM, respectively. The experimental results demonstrate that BaP has a significant effect on the AP-site damage, and the degree of DNA base damage is time-dependent and concentration-dependent.

Study on Temperature-Dependent Uniaxial Tensile Tests and Constitutive Relationship of Modified Polyurethane Concrete.

Modified polyurethane concrete (MPUC) is a new material for steel deck pavements. In service, the pavement is often cracked due to excessive tensile stress caused by temperature changes. In order to study the tensile properties of MPUC in the diurnal temperature range of steel decks, uniaxial tensile tests of MPUC were carried out at five temperatures. Three kinds of specimens and a novel fixture were designed and fabricated to compare the results of four different tensile test methods. The deformation of the specimen was collected synchronously by two methods: pasting strain gauge and digital image correlation (DIC) technique. Based on the experiment, the tensile mechanical properties, failure modes, and constitutive relations of MPUC were studied under the effect of temperature. The research results show that the novel fixture can avoid stress concentration. By observing the fracture surface of the specimens, the bonding performance is great between the binder and the aggregate at different temperatures. The tensile strength and elastic modulus of MPUC decrease with increasing temperatures, while the fracture strain, and fracture energy increase with increasing temperatures. The formulas of temperature-dependent tensile strength, fracture strain, and elastic modulus of MPUC were established, and the constitutive relationship of MPUC is further constructed in the rising stage under uniaxial tension. The calculation results show good agreement with experimental ones.

Tumor Microenvironment-Responsive Theranostic Nanoplatform for Guided Molecular Dynamic/Photodynamic Synergistic Therapy.

The integration of reactive oxygen species (ROS)-involved molecular dynamic therapy (MDT) and photodynamic therapy (PDT) holds great promise for enhanced anticancer effects. Herein, we report a biodegradable tumor microenvironment-responsive nanoplatform composed of sinoporphyrin sodium (SPS) photosensitizer-loaded zinc peroxide nanoparticles (SPS@ZnO2 NPs), which can enhance the action of ROS through the production of hydrogen peroxide (H2O2) and singlet oxygen (1O2) for MDT and PDT, respectively, and the depletion of glutathione (GSH). Under these conditions, SPS@ZnO2 NPs show excellent MDT/PDT synergistic therapeutic effects. We demonstrate that the SPS@ZnO2 NPs quickly degrade to H2O2 and endogenous Zn2+ in an acidic tumor environment and produce toxic 1O2 with 630 nm laser irradiation both in vitro and in vivo. Anticancer mechanistic studies show that excessive production of ROS damages lysosomes and mitochondria and induces cellular apoptosis. We show that SPS@ZnO2 NPs increase the uptake and penetration depth of photosensitizers in cells. In addition, the fluorescence of SPS is a powerful diagnostic tool for the treatment of tumors. The depletion of intracellular GSH through H2O2 production and the release of cathepsin B enhance the effectiveness of PDT. This theranostic nanoplatform provides a new avenue for tumor microenvironment-responsive and ROS-involved therapeutic strategies with synergistic enhancement of antitumor activity.

Self-weighted adaptive structure learning for ASD diagnosis via multi-template multi-center representation.

As a kind of neurodevelopmental disease, autism spectrum disorder (ASD) can cause severe social, communication, interaction, and behavioral challenges. To date, many imaging-based machine learning techniques have been proposed to address ASD diagnosis issues. However, most of these techniques are restricted to a single template or dataset from one imaging center. In this paper, we propose a novel multi-template multi-center ensemble classification scheme for automatic ASD diagnosis. Specifically, based on different pre-defined templates, we construct multiple functional connectivity (FC) brain networks for each subject based on our proposed Pearson's correlation-based sparse low-rank representation. After extracting features from these FC networks, informative features to learn optimal similarity matrix are then selected by our self-weighted adaptive structure learning (SASL) model. For each template, the SASL method automatically assigns an optimal weight learned from the structural information without additional weights and parameters. Finally, an ensemble strategy based on the multi- template multi-center representations is applied to derive the final diagnosis results. Extensive experiments are conducted on the publicly available Autism Brain Imaging Data Exchange (ABIDE) database to demonstrate the efficacy of our proposed method. Experimental results verify that our proposed method boosts ASD diagnosis performance and outperforms state-of-the-art methods.

Breast Cancer Image Classification via Multi-level Dual-network Features and Sparse Multi-Relation Regularized Learning.

Breast cancer is one of the leading causes of cancer death worldwide. Recently, the computer-aided diagnosis and detection technique has been developed for the early diagnosis of breast cancer, but the diagnostic efficiency has still been a challenging issue. For this reason, we aim to improve the breast cancer diagnostic accuracy and reduce the workload of doctors in this paper by devising a deep learning framework based on histological image. Therefore, we develop a model of multi-level feature of dual-network combined with sparse multi-relation regularized learning method, which enhances the classification performance and robustness. Specifically, first, we preprocess the histological images using scale transformation and color enhancement methods. Second, the multi-level features are extracted from preprocessed images using InceptionV3-ML and ResNet-50 networks. Third, the feature selection method via sparse multi-relation regularization is further developed for performance boosting and overfitting reduction. We evaluate the proposed method based on the public ICIAR 2018 Challenge dataset of breast cancer histology images. Experimental results show that our method has achieved promising performance and outperformed the related works.