Physiological and transcriptomic responses of silkworms to graphene oxide exposure.

The growing use of nanomaterials has sparked significant interest in assessing the insect toxicities of nanoparticles. The silkworm, as an economically important insect, serves as a promising model for studying how insects respond to harmful substances. Here, we conducted a comprehensive investigation on the impact of graphene oxide (GO) on silkworms using a combination of physiological and transcriptome analyses. GO can enter the midguts and posterior silk glands of silkworms. High GO concentrations (> 25 mg/L) significantly (P < 0.01) inhibited larval growth. Additionally, GO (> 5 mg/L) significantly reduced the cocooning rate, and GO (> 15 mg/L) hindered oviduct development and egg laying in silkworms. GO increased the reactive oxygen species content and regulated catalase activity, suggesting that it may affect insect growth by regulating reactive oxygen detoxification. The transcriptome data analysis showed that 35 metabolism-related genes and 20 ribosome biogenesis-related genes were differentially expressed in response to GO, and their expression levels were highly correlated. Finally, we propose that a Ribosome biogenesis-Metabolic signaling network is involved in responses to GO. The research provides a new perspective on the molecular responses of insects to GO.

Optimization of lightweight structure and supporting bipod flexure for a space mirror.

This article presents an optimization process for integrated optomechanical design. The proposed optimization process for integrated optomechanical design comprises computer-aided drafting, finite element analysis (FEA), optomechanical transfer codes, and an optimization solver. The FEA was conducted to determine mirror surface deformation; then, deformed surface nodal data were transferred into Zernike polynomials through MATLAB optomechanical transfer codes to calculate the resulting optical path difference (OPD) and optical aberrations. To achieve an optimum design, the optimization iterations of the FEA, optomechanical transfer codes, and optimization solver were automatically connected through a self-developed Tcl script. Two examples of optimization design were illustrated in this research, namely, an optimum lightweight design of a Zerodur primary mirror with an outer diameter of 566 mm that is used in a spaceborne telescope and an optimum bipod flexure design that supports the optimum lightweight primary mirror. Finally, optimum designs were successfully accomplished in both examples, achieving a minimum peak-to-valley (PV) value for the OPD of the deformed optical surface. The simulated optimization results showed that (1) the lightweight ratio of the primary mirror increased from 56% to 66%; and (2) the PV value of the mirror supported by optimum bipod flexures in the horizontal position effectively decreased from 228 to 61 nm.

Reliable automatic sleep stage classification based on hybrid intelligence.

Sleep staging is a vital aspect of sleep assessment, serving as a critical tool for evaluating the quality of sleep and identifying sleep disorders. Manual sleep staging is a laborious process, while automatic sleep staging is seldom utilized in clinical practice due to issues related to the inadequate accuracy and interpretability of classification results in automatic sleep staging models. In this work, a hybrid intelligent model is presented for automatic sleep staging, which integrates data intelligence and knowledge intelligence, to attain a balance between accuracy, interpretability, and generalizability in the sleep stage classification. Specifically, it is built on any combination of typical electroencephalography (EEG) and electrooculography (EOG) channels, including a temporal fully convolutional network based on the U-Net architecture and a multi-task feature mapping structure. The experimental results show that, compared to current interpretable automatic sleep staging models, our model achieves a Macro-F1 score of 0.804 on the ISRUC dataset and 0.780 on the Sleep-EDFx dataset. Moreover, we use knowledge intelligence to address issues of excessive jumps and unreasonable sleep stage transitions in the coarse sleep graphs obtained by the model. We also explore the different ways knowledge intelligence affects coarse sleep graphs by combining different sleep graph correction methods. Our research can offer convenient support for sleep physicians, indicating its significant potential in improving the efficiency of clinical sleep staging.