Calpain activation and disturbance of autophagy are induced in cortical neurons in vitro by exposure to HA/β-Ga2O3:Cr3+ nanoparticles

The toxicity of engineered nanoparticles remains a concern. The knowledge of biohazards associated with particular nanoparticles is crucial to make this cutting-edge technology more beneficial and safe. Here, we evaluated the toxicity of Ga2O3 nanoparticles (NPs), which are frequently used to enhance the performance of metal catalysts in a variety of catalytic reactions. The potential inflammatory signaling associated with the toxicity of HA/β-Ga2O3:Cr3+ NPs in primary cortical neurons was examined. We observed a dose-dependent decrease in cell viability and an increase in apoptosis in neurons following various concentrations (0, 1, 5, 25, 50, 100 µg/ml) of HA/β-Ga2O3:Cr3+ NPs treatment. Consistently, constitutively active forms of calcineurin (48 kDa) were significantly elevated in cultured primary cortical neurons, which was consistent with calpain activation indicated by the breakdown products of spectrin. Moreover, HA/β-Ga2O3:Cr3+ NPs result in the elevation of LC3-II formation, SQSTM/p62, and Cathepsin B, whereas phosphorylation of CaMKII (Thr286) and Synapsin I (Ser603) were downregulated in the same context. Taken together, these results demonstrate for the first time that calpain activation and a disturbance of autophagy signaling are evoked by exposure to HA/β-Ga2O3:Cr3+ NPs, which may contribute to neuronal injury in vitro

Beyond extraction accuracy: addressing the quality of geographical named entity through advanced recognition and correction models using a modified BERT framework

ABSTRACTIn the realm of geospatial services and applications, the accuracy of address information is of utmost importance. Traditional methods of data collection, being both labor-intensive and costly, have prompted researchers to turn to Volunteered Geographic Information (VGI) for the extraction of Geographical Named Entity (GNE).Notwithstanding, prior studies have predominantly concentrated on enhancing extraction accuracy, while often overlooking the critical aspect of GNE quality. This study addresses this gap by employing a multifaceted approach. Initially, a Geographical Named Entity Semantic Model (GNESM) was constructed by improving the BERT framework and conducting ablation experiments on multiple influencing factors to verify its feasibility. Based on GNESM, a Geographical Named Entity Recognition Model (GNERM) was constructed by incremental pre-training with social media text data and fine-tuning to achieve a recognition accuracy of 90.9%. Subsequently, a Geographical Named Entity Error Correction Model (GNEECM) was constructed by training GNESM with standard GNE data and incorporating error detection and correction modules, achieving a remarkable accuracy of 96.6% in error detection and correction tasks. The experimental results convincingly demonstrate that the proposed identification and correction methods outperform all compared methods. Through the identification and correction process, this study successfully obtained high-quality GNE data, providing a reference for expanding standard address libraries and subsequent research on geographic named entity

Ultracompact Polarization Splitter–Rotator Based on Shallowly Etched Subwavelength Gratings and Anisotropic Metasurfaces

Polarization splitter–rotators (PSRs) are an essential component in on-chip polarization-sensitive and polarization–division multiplexing systems. In this work, we propose an ultracompact and high-performance silicon-based polarization splitter–rotator utilizing anisotropic metasurfaces, which is the first to combine the two, to our knowledge. The tilted periodic metasurface structure has different modulation effects on different polarized light fields, such as the transverse–electric (TE) mode and the transverse–magnetic (TM) mode, which are beneficial for designing polarization management devices. According to the results, the entire length of the silicon PSR was ~13.5 μm. The TE-to-TM conversion loss and polarization conversion ratio ere −0.154 dB and 96.5% at 1.55 μm, respectively. In the meanwhile, the cross talk and reflection loss were −27.0 dB and −37.3 dB, when the fundamental TE mode was input. The insertion loss and cross talk were −0.19 dB and −25.01 dB at the central wavelength when the fundamental TM mode was input. In addition, the bandwidth reached up to ~112 nm with polarization conversion loss and insertion loss higher than −0.46 dB and −0.36 dB. The simulations also show that the designed devices had good fabrication tolerance

An Interface for Biomedical Big Data Processing on the Tianhe-2 Supercomputer

Big data, cloud computing, and high-performance computing (HPC) are at the verge of convergence. Cloud computing is already playing an active part in big data processing with the help of big data frameworks like Hadoop and Spark. The recent upsurge of high-performance computing in China provides extra possibilities and capacity to address the challenges associated with big data. In this paper, we propose Orion—a big data interface on the Tianhe-2 supercomputer—to enable big data applications to run on Tianhe-2 via a single command or a shell script. Orion supports multiple users, and each user can launch multiple tasks. It minimizes the effort needed to initiate big data applications on the Tianhe-2 supercomputer via automated configuration. Orion follows the “allocate-when-needed” paradigm, and it avoids the idle occupation of computational resources. We tested the utility and performance of Orion using a big genomic dataset and achieved a satisfactory performance on Tianhe-2 with very few modifications to existing applications that were implemented in Hadoop/Spark. In summary, Orion provides a practical and economical interface for big data processing on Tianhe-2