Single-double-band switchable optical circular polarizers based on surface plasmon resonance: publisher's note.

This publisher's note reports a correction in Appl. Opt.63, 1153 (2024)APOPAI0003-693510.1364/AO.513837.

Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China.

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km2 in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20 °C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 °C is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance.

Single-double-band switchable optical circular polarizers based on surface plasmon resonance.

A single-double-band switchable circular polarization filter based on surface plasmon resonance exhibits significant potential for applications in fields such as communication and sensing due to its adjustable, low-cost, and easy integration features. In this study, we propose a bi-layer rod nanostructure and use FEM simulation to study the transmission spectra of the structure. The results demonstrate that the structure exhibits both single- and double-band circular polarization filtering effects, which can be switched by varying geometric parameters such as the distance between the two layers and the width of nanorods. Furthermore, the filtering effects of both single- and double-band are highly dependent on the length of the nanorods, with average extinction rates reaching 486 and 2020/129, respectively; the operating bandwidths (defined as extinction ratio >10) can reach 170 nm and 35 nm/70 nm, respectively. The underlying physical mechanisms are clarified by analyzing the electric dipole, magnetic dipole resonance modes, and induced chiral fields on nanostructures.

Sodium aescinate induces renal toxicity by promoting Nrf2/GPX4-mediated ferroptosis.

Sodium aescinate (SA) is extracted from Aesculus wilsonii Rehd seeds and was first marketed as a medicament in German. With the wide application of SA in clinical practice, reports of adverse drug reactions and adverse events have gradually increased, including renal impairment. However, the pathogenic mechanisms of SA have not yet been fully elucidated. The toxic effects and underlying mechanisms of SA were explored in this study. Our data showed that SA significantly elevated the levels of blood urea nitrogen (BUN), serum creatinine (Scr) and Kidney injury molecule 1 (Kim-1), accompanied by pathologically significant changes in renal tissue. SA induced NRK-52E cell death and disrupted the integrity of the cell membrane. Moreover, SA caused significant reductions in FTH, Nrf2, xCT, GPX4, and FSP1 levels, but increased TFR1 and ACSL4 levels. SA decreased glutathione peroxidase (GPx), glutathione (GSH) and cysteine (Cys) levels, but improved Fe2+, malondialdehyde (MDA), reactive oxygen species (ROS) and lipid peroxidation levels, ultimately leading to the induction of ferroptosis. Importantly, inhibition of ferroptosis or activation of the Nrf2/GPX4 pathway prevented SA-induced nephrotoxicity. These findings indicated that SA induced oxidative damage and ferroptosis-mediated kidney injury by suppressing the Nrf2/GPX4 axis activity.

Matrine disrupts Nrf2/GPX4 antioxidant system and promotes hepatocyte ferroptosis.

Matrine (MT) is an alkaloid isolated from Sophora flavescens with various bioactivities and is widely used clinically. However, the broader its clinical use, the greater its toxicity concerns. We investigate the role of ferroptosis in MT-induced liver injury caused by an imbalance in the antioxidant pathway. Our results showed that MT could cause pathological changes in liver tissues and lead to a significant reduction in L02 cell viability. MT also reduced superoxide dismutase (SOD) and glutathione (GSH), increased malondialdehyde (MDA), reactive oxygen species (ROS), and lipid peroxidation levels, and disrupted iron homeostasis, leading to ferroptosis. In addition, MT decreased the protein levels of FTH, Nrf2, xCT, GPX4, HO-1 and ferroptosis suppressor protein 1 (FSP1) and increased the protein levels of TRF1 and DMT1, characteristic indicators of ferroptosis. Interestingly, the cytotoxic effects of MT were alleviated by ferroptosis inhibitor, Nrf2 agonist, or selenium supplementation. These results revealed that MT triggers hepatocyte ferroptosis by inhibiting the Nrf2/GPX4 antioxidant system.

Hyperspectral Image Classification Using Deep Genome Graph-Based Approach.

Recently developed hybrid models that stack 3D with 2D CNN in their structure have enjoyed high popularity due to their appealing performance in hyperspectral image classification tasks. On the other hand, biological genome graphs have demonstrated their effectiveness in enhancing the scalability and accuracy of genomic analysis. We propose an innovative deep genome graph-based network (GGBN) for hyperspectral image classification to tap the potential of hybrid models and genome graphs. The GGBN model utilizes 3D-CNN at the bottom layers and 2D-CNNs at the top layers to process spectral-spatial features vital to enhancing the scalability and accuracy of hyperspectral image classification. To verify the effectiveness of the GGBN model, we conducted classification experiments on Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SA) datasets. Using only 5% of the labeled data for training over the SA, IP, and UP datasets, the classification accuracy of GGBN is 99.97%, 96.85%, and 99.74%, respectively, which is better than the compared state-of-the-art methods.

Energy-Guided Temporal Segmentation Network for Multimodal Human Action Recognition.

To achieve the satisfactory performance of human action recognition, a central task is to address the sub-action sharing problem, especially in similar action classes. Nevertheless, most existing convolutional neural network (CNN)-based action recognition algorithms uniformly divide video into frames and then randomly select the frames as inputs, ignoring the distinct characteristics among different frames. In recent years, depth videos have been increasingly used for action recognition, but most methods merely focus on the spatial information of the different actions without utilizing temporal information. In order to address these issues, a novel energy-guided temporal segmentation method is proposed here, and a multimodal fusion strategy is employed with the proposed segmentation method to construct an energy-guided temporal segmentation network (EGTSN). Specifically, the EGTSN had two parts: energy-guided video segmentation and a multimodal fusion heterogeneous CNN. The proposed solution was evaluated on a public large-scale NTU RGB+D dataset. Comparisons with state-of-the-art methods demonstrate the effectiveness of the proposed network.