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Summary Partial discharge (PD) detection is essential for electrical equipment. Focusing on the noise suppression of PD signal, a PD signal denoising method based on rapid sparse decomposition was proposed. Through the analysis of PD signal time‐frequency characteristics, the PD signal correlated atom and PD signal correlated overcomplete dictionary were designed, which were correlated to the original PD pulse signal while uncorrelated or weak‐correlated to the random noise and discrete spectral interference (DSI) signal. Denoising the noisy PD signal by sparse decomposition based on matching pursuit algorithm and correlation ratio iteration termination condition, only the original PD pulse signal can be expressed by the best atoms extracted from the PD signal correlated overcomplete dictionary; thus, the goal of denoising is achieved. What is more, the searching progress of best atoms was accelerated by quantum genetic algorithm. After finishing the above denoising process, the extracted noiseless pulse signals can be projected onto a Time–Frequency Map (TF Map) to overcome pulse‐shape interference influence. The denoising method presented in this article is applied to the simulated, laboratory measured and field detected PD signals. The results are critical compared with denoising method based on db2 and db8 wavelet transform. The results show that the denoising method of this article is available to precisely suppress PD signal noise jamming and the denoising effect is superior to traditional wavelet methods, which has less amplitude error as well as less waveform destination. Copyright © 2016 John Wiley & Sons, Ltd.

Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies.

Partial discharge (PD) is a symptom of insulation defect or degradation in high-voltage equipment. Thus, PD detection is an important diagnostic tool. Furthermore in practical situations, the PD can be generated from a single or multiple sources. Being able to detect and classify such PD events will help to determine the necessary corrective action to prevent insulation breakdown. To demonstrate, three different simulated discharge conditions in transformers were investigated: void, floating metal and their combination. The PD signals were captured using an ultra-high frequency (UHF) sensor and denoised using wavelet transform method by application of Matlab wavelet multi-variate denoising tool. Two types of mother wavelet, that is, db and sym, were applied to decompose the signals and extract the signal features in terms of their skewness, kurtosis and energy. These features were then used as input to train a neural network to analyse and determine the PD source type. Results show this technique is able to classify and recognise single and multiple PD source types with a high degree of success.

To accurately determine the strength and the waveform of the partial discharge (PD) signal, especially if the source position is far from the ultra-high frequency (UHF) sensor or if the signal is weak, it is necessary to properly extract the most prominent PD from the background noise in the recorded signal. This paper provides a new procedure for the extraction of PD signal in the predominant very high frequency (VHF) range from the strong noise in each of the signals recorded online and onsite using the UHF sensor in the power transformer during its normal operation in a thermal power plant. A standard UHF drain valve sensor was used with good sensitivity in the high frequency and VHF bands. First, it is necessary to determine as precisely as possible the period in which the most prominent PD occurs in the middle part of each signal. Second, it is to compare the frequency spectra of the dominant, strong noise in the left and right parts in relation to the corresponding middle part of each recorded signal. And third, it is to extract the PD with the largest amplitudes from the estimated noise in the middle part of each recorded signal by finding the cutoff frequency and performing high-pass filtering in MATLAB. The new criterion for cutoff frequency is that there are no time shifts of the first peaks of the most prominent PD of each recorded signal. The results show some obvious similarities of PDs in the recorded signals, such as frequency range, duration, repetition rate, and the same dominant frequency, which sufficiently indicates that it is the same type of PD.

Parkinson's disease (PD) is a more prevalent neurological disorder that typically manifests in adults. It is primarily caused by the death of dopaminergic neurons in the substantia nigra, which leads to the degeneration of cardinal motor symptoms. Several epigenetic elements are linked to the development of PD. The Parkin, PINK1, DJ-1, UCHL1, LRRK2, NURR1, ATP13A2, GSK3B, and SNCA are important genes that are involved in the regulatory processes and development of PD. The objective of the study is to develop a knowledge-based database for PD. “Parkinson Disease Associated SNP Database (PDASD)\" has been created to establish connections between PD-associated SNPs, related pathways, proteins, risk assessment, and molecular mechanisms, available FDA drugs for PD, nutrition involvement of PD and available PD literature through the utilization of HTML and Java programming languages. This PDASD database has been amalgamated with 13 distinct databases to improve the accessibility of SNP data. The implementation of PDASD is anticipated to expedite the process and facilitate the identification of innovative drug candidates for PD through the application of computational drug design techniques in PD therapeutics. The PDASD database serves as a secondary resource that enhances the existing data from various tools to predict the status of SNPs, specifically missense variant risk factors. This platform consolidates the effects of all identified SNPs, facilitating easier access to their positional information and thereby optimizing time efficiency for users. A novelty of this database is its capacity to inform common people about the progression of PD through accessible molecular mechanisms and information regarding nutritional benefits. It will be useful to understand the interconnection of signaling pathways, molecular mechanisms, and risk-associated SNPs of PD, which may contribute to improving human health, especially for the community with PD. The PDASD is an open and accessed database connected via the following URL: https://www.generisk.in/PDASD/

Background As the fifth most common cancer worldwide, hepatocellular carcinoma (HCC) is a highly malignant disease with a formidable prognosis. Within the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a paramount role in tumorigenesis and progression by providing a supportive environment for cancer cells. This research aims to elucidate the regulatory mechanisms of CAFs and salmonella pathogenicity island 1 (SPI1) in HCC progression. Methods Protein and mRNA expression levels were determined by western blot and reverse-transcription quantitative polymerase chain reaction (RT-qPCR), respectively. The ability of glycolysis was detected using the glucose consumption and lactate production test kit. Cell proliferation was examined using colony formation assay. Cell migration and invasion were assessed via wound healing and transwell assays, respectively. The cell apoptosis was examined by flow cytometry and terminal deoxynucleotidyl transferase dUTP Nick-End Labeling (TUNEL). Results The CAFs and para-cancer fibroblasts (PAFs) were successfully separated. CAFs under hypoxic stress (H/CAFs) promoted glucose consumption and lactate production in HCC cells (Huh7 and Hep3B). CAFs conditioned medium (CAFs-CM) facilitated HCC cell proliferation, migration, invasion, glucose consumption, and lactate production. CAFs-CM facilitated programmed cell death 1 ligand 1 (PD-L1) and CD8 + T apoptosis and hindered proliferation of CD8 + T. SPI1 was identified as the common target of the differentially expressed genes (DEGs) in CAFs (GSE192912) and transcription factors (TF). The mRNA and protein expression of SPI1 was increased in CAFs. SPI1 knockdown suppressed PD-L1 expression levels, glucose consumption, lactate production, proliferation, invasion, and immune escape in CAFs-CM-cultured HCC cells. Conclusions SPI1 derived from CAFs facilitates the malignant behaviors of HCC cells by up-regulating v-Ha-ras Harvey rat sarcoma viral oncogene homolog (HRAS) expression. This study enriches our understanding of HCC at the molecular level and may pave the way for the development of a novel strategy for the treatment of HCC.

The study proposes a new Generic phase shifted (PS) pulse-width modulation (PWM) approach. Four Generic PS PWM algorithms are proposed. The important advantage of the proposed approach is that it can be easily implemented with multilevel inverters of any topology. The first method produces the 13-level PS PWM signal from six 3-level phase disposition (PD) PWM signals whose carrier signals are PS from each other by the time interval of 1/(6fcarrier). The second method produces the 13-level PWM signal also from six 3-level PD PWM signals, whereas their modulation and the carrier signals are both PS by the time interval of Tmodulation/(6 × 5), where Tmodulation is the modulation cycle time interval. The third method produces the 13-level PWM signal from three 5-level PD PWM signals, whereas their carrier signals are PS from each other by the time interval of 1/(3fcarrier). The fourth method produces the 13-level PS PWM signal from three 5-level PD PWM signals, whereas their modulation and carrier signals are both PS by the time interval of Tmodulation/(3 × 5). The proposed methods are analysed and compared one to the other and to the conventional PD PWM method. The analysis is validated by extensive simulation results.

Time-domain features of partial discharge (PD) signals are often used to classify PD patterns. This paper proposes spectral features that are extracted using a filter bank, consisting of band-pass filters. By applying the fast Fourier transform to the PD signal, the resulting frequency bins are grouped into L octave frequency sub-bands. Two new features called the octave frequency moment coefficients (OFMC) and octave frequency Cepstral coefficients (OFCC) are defined in this paper. In addition, time–frequency domain coefficients (TFDC) obtained via wavelet analysis are also analysed. A PD signal can now be represented as an L-dimensional feature vector of OFMC, OFCC or TFDC. These features are compared with discrete wavelet transform-based higher-order statistical features (HOSF) using three different classifiers: probabilistic neural network, support vector machine and the recently emerged sparse representation classifier. Results show that the proposed spectral features are robust and provide a better classification accuracy of PD signals, compared with HOSF.

Spermatogenesis is a cyclical process in which different generations of spermatids undergo a series of developmental steps at a fixed time and finally produce spermatids. Here, we report that overexpression of PD‐L1 (B7 homolog1) in the testis causes sperm developmental disorders and infertility in male mice, with severe malformation and sloughing during spermatid development, characterized by disorganized and collapsed seminiferous epithelium structure. PD‐L1 needs to be simultaneously expressed on Sertoli cells and spermatogonia to cause spermatogenesis failure. After that, we excluded the influence of factors such as the PD‐L1 receptor and humoral regulation, confirming that PD‐L1 has an intrinsic function to interact with PD‐L1. Studies have shown that PD‐L1 not only serves as a ligand but also plays a receptor‐like role in signal transduction. PD‐L1 interacts with PD‐L1 to affect the adhesive function of germ cells, causing malformation and spermatid sloughing. Taken together, these results indicate that PD‐L1 can interact with PD‐L1 to cause germ cell detachment and male infertility.

Tumor immune escape is an important strategy of tumor survival. There are many mechanisms of tumor immune escape, including immunosuppression, which has become a research hotspot in recent years. The programmed death ligand-1/programmed death-1 (PD-L1/PD-1) signaling pathway is an important component of tumor immunosuppression, which can inhibit the activation of T lymphocytes and enhance the immune tolerance of tumor cells, thereby achieving tumor immune escape. Therefore, targeting the PD-L1/PD-1 pathway is an attractive strategy for cancer treatment; however, the therapeutic effectiveness of PD-L1/PD-1 remains poor. This situation requires gaining a deeper understanding of the complex and varied molecular mechanisms and factors driving the expression and activation of the PD-L1/PD-1 signaling pathway. In this review, we summarize the regulation mechanisms of the PD-L1/PD-1 signaling pathway in the tumor microenvironment and their roles in mediating tumor escape. Overall, the evidence accumulated to date suggests that induction of PD-L1 by inflammatory factors in the tumor microenvironment may be one of the most important factors affecting the therapeutic efficiency of PD-L1/PD-1 blocking.