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As the leading cause of disability worldwide, low back pain (LBP) is recognized as a pivotal socioeconomic challenge to the aging population and is largely attributed to intervertebral disc degeneration (IVDD). Elastic nucleus pulposus (NP) tissue is essential for the maintenance of IVD structural and functional integrity. The accumulation of senescent NP cells with an inflammatory hypersecretory phenotype due to aging and other damaging factors is a distinctive hallmark of IVDD initiation and progression. In this study, we reveal a mechanism of IVDD progression in which aberrant genomic DNA damage promoted NP cell inflammatory senescence via activation of the cyclic GMP-AMP synthase/stimulator of IFN genes (cGAS/ STING) axis but not of absent in melanoma 2 (AIM2) inflammasome assembly. Ataxia-telangiectasia-mutated and Rad3-related protein (ATR) deficiency destroyed genomic integrity and led to cytosolic mislocalization of genomic DNA, which acted as a powerful driver of cGAS/STING axis-dependent inflammatory phenotype acquisition during NP cell senescence. Mechanistically, disassembly of the ATR-tripartite motif-containing 56 (ATR-TRIM56) complex with the enzymatic liberation of ubiquitin-specific peptidase 5 (USP5) and TRIM25 drove changes in ATR ubiquitination, with ATR switching from K63- to K48-linked modification, c thereby promoting ubiquitin-proteasome-dependent dynamic instability of ATR protein during NP cell senescence progression. Importantly, an engineered extracellular vesicle-based strategy for delivering ATR-overexpressing plasmid cargo efficiently diminished DNA damage-associated NP cell senescence and substantially mitigated IVDD progression, indicating promising targets and effective approaches to ameliorate the chronic pain and disabling effects of IVDD., Introduction Low back pain (LBP), the leading cause of disability worldwide and the second most common reason for medical consultation in industrialized countries, is the most prevalent and burdensome musculoskeletal [...]

With the rapid development of robot technology and the higher requirements of safe and efficient production in coal mine, underground mechanical and electrical equipment has changed from the traditional manual inspection to the robot inspection with the function of “monitoring, detection and early warning”. In this paper, sound processing and deep learning are introduced into the intelligent processing of mining system to analyze the data intelligently. The key technologies of voice preprocessing, spectrogram generation, feature extraction and classification are studied, which solve the problems that the speech features can not be described simultaneously in time domain and frequency domain, and lack of effective use of dynamic sequence information. The experimental data show that the CNN + LSTM + Softmax network based on CNN + LSTM model and caffe C + + deep learning framework can effectively improve the accuracy and robustness of abnormal sound recognition of coal mine equipment, reduce the complexity of the algorithm to adapt to the operation of the algorithm in embedded equipment, and realize the fault audio identification and diagnosis of robotic coal machine.

Accurate photovoltaic power prediction is of great significance to the stable operation of the electric power system with renewable energy as the main body. In view of the different influence mechanisms of meteorological factors on photovoltaic power generation in different irradiation intervals and that the data-driven algorithm has the problem of regression to the mean, in this article, a prediction method based on irradiation interval distribution and Transformer-long short-term memory (IID-Transformer-LSTM) is proposed. Firstly, the irradiation interval distribution is calculated based on the boxplot. Secondly, the distributed data of each irradiation interval is input into the Transformer-LSTM model for training. The self-attention mechanism of the Transformer is applied in the coding layer to focus more important information, and LSTM is applied in the decoding layer to further capture the potential change relationship of photovoltaic power generation data. Finally, sunny data, cloudy data, and rainy data are selected as test sets for case analysis. Through experimental verification, the method proposed in this article has a certain improvement in prediction accuracy compared with the traditional methods under different weather conditions. In the case of local extrema and large local fluctuations, the prediction accuracy is clearly improved. [ABSTRACT FROM AUTHOR]