Indium-Containing Visible-Light-Driven (VLD) Photocatalysts for Solar Energy Conversion and Environment Remediation

Indium-containing visible-light-driven (VLD) photocatalysts including indium-containing oxides, indium-containing sulfides, indium-containing hydroxides, and other categories have attracted more attention due to their high catalytic activities for oxidation and reduction ability under visible light irradiation. This chapter will therefore concentrate on indium-containing nano-structured materials that demonstrate useful activity under solar excitation in fields concerned with the elimination of pollutants, partial oxidation and the vaporization of chemical compounds, water splitting, and CO2 reduction processes. The indium-containing photocatalysts can extend the light absorption range and improve the photocatalytic activity by doping, heterogeneous structures, load promoter, and morphology regulation. A number of synthetic and modification techniques for adjusting the band structure to harvest visible light and improve the charge separation in photocatalysis are discussed. In this chapter, preparation, properties, and potential applications of indium-containing nano-structured materials used as photocatalysis will be systematically summarized, which is beneficial for understanding the mechanism and developing the potential applications

DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation

Onboard intelligent processing is widely applied in emergency tasks in the field of remote sensing. However, it is predominantly confined to an individual platform with a limited observation range as well as susceptibility to interference, resulting in limited accuracy. Considering the current state of multi-platform collaborative observation, this article innovatively presents a distributed collaborative perception network called DCP-Net. Firstly, the proposed DCP-Net helps members to enhance perception performance by integrating features from other platforms. Secondly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners, prioritizing critical collaborative features and reducing redundant transmission cost. Thirdly, a related feature fusion module is designed to address the misalignment between local and collaborative features, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, including Potsdam, iSAID and DFC23. The results demonstrate that DCP-Net outperforms the existing methods comprehensively, improving mIoU by 2.61%~16.89% at the highest collaboration efficiency, which promotes the performance to a state-of-the-art level

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser-induced breakdown spectroscopy

The rapid and accurate diagnosis of cancer is an important topic in clinical medicine. In the present work, an innovative method based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning was developed to distinguish and classify different tumor cell lines. The LIBS spectra of cells were first acquired. Then the spectral pre-processing was performed as well as detailed optimization to improve the classification accuracy. After that, the convolutional neural network (CNN), support vector machine (SVM), and K-nearest neighbors were further compared for the optimized classification ability of tumor cells. Both the CNN algorithm and SVM algorithm have achieved impressive discrimination performances for tumor cells distinguishing, with an accuracy of 97.72%. The results show that the heterogeneity of elements in tumor cells plays an important role in distinguishing the cells. It also means that the LIBS technique can be used as a fast classification method for classifying tumor cells

Ulinastatin Ameliorates IL-1β-Induced Cell Dysfunction in Human Nucleus Pulposus Cells via Nrf2/NF-κB Pathway

Low back pain (LBP) has been a wide public health concern worldwide. Among the pathogenic factors, intervertebral disc degeneration (IDD) has been one of the primary contributors to LBP. IDD correlates closely with inflammatory response and oxidative stress, involving a variety of inflammation-related cytokines, such as interleukin 1 beta (IL-1β), which could result in local inflammatory environment. Ulinastatin (UTI) is a kind of acidic protein extracted from human urine, which inhibits the release of tumor necrosis factor alpha (TNF-α) and other inflammatory factors to protect organs from inflammatory damage. However, whether this protective effect of UTI on human nucleus pulposus (NP) exists, and how UTI affects the biological behaviors of human NP cells during IDD remain elusive. In this current study, we revealed that UTI could improve the viability of NP cells and promote the proliferation of NP cells. Additionally, UTI could protect human NP cells via ameliorating IL-1β-induced apoptosis, inflammatory response, oxidative stress, and extracellular matrix (ECM) degradation. Molecular mechanism analysis suggested that the protective effect from UTI on IL-1β-treated NP cells were through activating nuclear factor- (erythroid-derived 2-) like 2 (Nrf2)/heme oxygenase-1 (HO-1) signaling pathway and the suppression of NF-κB signaling pathway. Therefore, UTI may be a promising therapeutic medicine to ameliorate IDD