Storage protein SfSP8 mediates larval starvation tolerance of Spodoptera frugiperda.

BACKGROUND: Energy homeostasis is vital for insects to survive food shortages. This study investigated the starvation tolerance of Spodoptera frugiperda, which invaded China in 2019, focusing on its storage protein family, crucial for energy balance. 10 storage protein family members were identified and their expression patterns at different development stages and under different starvation stress were analyzed. METHODS AND RESULTS: We used qPCR to evaluate the expression levels of storage protein family members under various larval instars and starvation conditions. We discovered that, among above 10 members, only 2 storage proteins, SfSP8 and SfSP7 showed significant upregulation in response to starvation stress. Notably, SfSP8 upregulated markedly after 24 h of fasting, whereas SfSP7 exhibited a delayed response, with significant upregulation observed only after 72 h of starvation. Then we significantly reduced the starvation tolerance of larvae through RNAi-mediated knockdown of SfSP8 and also altered the starvation response of SfSP7 from a late to an early activation pattern. Finally, we constructed transgenic Drosophila melanogaster with heterologous overexpressing SfSP8 revealed that the starvation tolerance of the transgenic line was significantly stronger than that of wild-type lines. CONCLUSIONS: SfSP8 was the core storage protein member that mediated the starvation tolerance of larvae of S. frugiperda. Our study on the novel function of storage proteins in mediating larval starvation tolerance of S. frugiperda is conducive to understanding the strong colonization of this terrible invasive pest.

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder.

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level.

Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells' dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield's central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths.

Silylation and (Hetero)aryl/alkenylation of Unactivated Alkenes via Radical-Mediated Distal 1,4-Migration with Hydrosilanes under Organophotocatalysis.

We report a novel organic photoredox catalysis to achieve unprecedented γ-(hetero)aryl/alkenyl-δ-silyl aliphatic amines via silyl-mediated distal (hetero)aryl/alkenyl migration of aromatic/alkenyl amines bearing unactivated alkenes with hydrosilanes. This protocol features mild and metal-free reaction conditions, high atom economy, excellent selectivity, and functional group compatibility. Mechanistic studies suggest that silylation and (hetero)aryl/alkenylation involve photoredox hydrogen atom transfer catalysis and subsequent 1,4-migration of a remote (hetero)aryl/alkenyl group from nitrogen to carbon.

Electroreductive Cross-Coupling between Aromatic Aldehydes and Chlorosilanes Enabling the Synthesis of α-Silyl Alcohols.

α-Silyl alcohols are powerful structural motifs for pharmaceutical chemistry, materials chemistry, and organic synthesis. The limitations of current synthetic techniques encompass a requirement for difficult-to-obtain silyl precursors, noble-metal catalysts, and narrow substrate scopes. Here, we developed a general synthetic method for α-silyl alcohols through electroreductive cross-coupling of aldehydes and chlorosilane. This method features easily available reagents, mild conditions, and a wide substrate scope. The establishment of this protocol will provide an alternative for access to α-silyl alcohols.