Due to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new ...method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults.
The Multidrug and Toxic Compound Extrusion (MATE) protein belongs to a secondary transporter gene family, which plays a primary role in transporting many kinds of substrates such as organic ...compounds, secondary metabolites, and phytohormones. MATE protein members exist in both prokaryotes and eukaryotes. However, evolution and comprehensive analysis of the MATE genes has not been performed in Rosaceae species. In the present study, a total of 404 MATEs genes were identified from six Rosaceae genomes (Prunus avium, Pyrus bretschneideri, Prunus persica, Fragaria vesca, Prunus mume, and Malus domestica) and classified into eight main subfamilies (I–VII) based on structural and phylogenetic analysis. Microcollinearity analysis showed that whole‐genome duplication events might play a vital role in the expansion of the MATE genes family. The Ka/Ks analysis, chromosomal localization, subcellular localization, and molecular characteristics (length, weight, and pI) were performed using various bioinformatics tools. Furthermore, different subfamilies have different introns‐exons structures, cis‐acting elements, and conserved motifs analysis, indicating functional divergence in the MATE family. Subsequently, RNA‐seq analysis and real‐time qRT‐PCR were conducted during Chinese pear fruit development. Moreover, PbMATE genes were significantly expressed under hormonal treatments of MeJA (methyl jasmonate), SA (salicylic acid), and ABA (abscisic acid). Overall, our results provide helpful insights into the functions, expansion complexity, and evolutions of the MATE genes in Chinese pear and five Rosaceae species.
The Cluster of Differentiation 27 (CD27) is aberrantly expressed in multiple myeloma (MM) -derived. This expression facilitates the interaction between tumor and immune cells within TME via the ...CD27-CD70 pathway, resulting in immune evasion and subsequent tumor progression. The objective of this study is to investigate the correlation between CD27 expression and the prognosis of MM, and to elucidate its potential relationship with the immune microenvironment.
In this research, CD27 expression in T cells within the 82 newly diagnosed MM microenvironment was assessed via flow cytometry. We then examined the association between CD27 expression levels and patient survival. Subsequent a series of bioinformatics and in vitro experiments were conducted to reveal the role of CD27 in MM.
Clinical evidence suggests that elevated CD27 expression in T cells within the bone marrow serves as a negative prognostic marker for MM survival. Data analysis from the GEO database has demonstrated a strong association between MM-derived CD27 and the immune response, as well as the hematopoietic system. Importantly, patients with elevated levels of CD27 expression were also found to have an increased presence of MDSCs and macrophages in the bone marrow microenvironment. Furthermore, the PERK-ATF4 signaling pathway has been implicated in mediating the effects of CD27 in MM.
We revealed that CD27 expression levels serve as an indicative marker for the prognosis of MM patients. The CD27- PERK-ATF4 is a promising target for the treatment of MM.
Multiple myeloma (MM) is a malignancy of plasma cells that remains incurable. Toll-like receptor 4 (TLR4) acts as a stress-responsive signal, protecting mitochondria during proteasome inhibitor (PI) ...exposure, maintaining mitochondrial metabolism and increasing drug resistance in MM. However, the mechanism of TLR4 regulation remains elusive.
The purpose of this study was to investigate the methylation pattern of multiple myeloma and its effect on the expression of HNRNPA2B1 and downstream targets.
The methylation level in MM and normal bone marrow specimens was detected using a colorimetric assay. HNRNPA2B1 gene knockdown was achieved in RPMI 8226 MM cells via adenovirus transfection. CCK8 and flow cytometric assays were used to detect proliferation and apoptosis, respectively. Transcriptome sequencing and m6A methylation MeRIP sequencing were applied, and differentially expressed genes (DEGs) were detected. Three independent NCBI GEO datasets were applied to examine the effects of HNRNPA2B1 and TLR4 expression on MM patient survival.
HNRNPA2B1 promoted MM progression. Clinical data from database revealed that HNRNPA2B1 was adverse prognostic factor for survival among MM patients. Furthermore, transcriptome sequencing and methylation sequencing showed that HNRNPA2B1 recognized and was enriched at the m6A sites of TLR4 and TLR4 was down-regulated of both the m6A level and transcription level in HNRNPA2B1-knockdown MM cells. Moreover, TLR4 was an adverse survival prognostic factor based on database analysis.
Overall, our study implies that the RNA-binding protein HNRNPA2B1 increases cell proliferation and deregulates cell apoptosis in MM through TLR4 signaling. Our study suggests HNRNPA2B1 as a potential therapeutic target for MM.
Cancer is a leading cause of death worldwide, with an increasing mortality rate over the past years. The early detection of cancer contributes to early diagnosis and subsequent treatment. How to ...detect early cancer has become one of the hot research directions of cancer. Tumor biomarkers, biochemical parameters for reflecting cancer occurrence and progression have caused much attention in cancer early detection. Due to high sensitivity, convenience and low cost, biosensors have been largely developed to detect tumor biomarkers. This review describes the application of various biosensors in detecting tumor markers. Firstly, several typical tumor makers, such as neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), prostate-specific antigen (PSA), squamous cell carcinoma antigen (SCCA), carbohydrate, antigen19-9 (CA19-9) and tumor suppressor p53 (TP53), which may be helpful for early cancer detection in the clinic, are briefly described. Then, various biosensors, mainly focusing on electrochemical biosensors, optical biosensors, photoelectrochemical biosensors, piezoelectric biosensors and aptamer sensors, are discussed. Specifically, the operation principles of biosensors, nanomaterials used in biosensors and the application of biosensors in tumor marker detection have been comprehensively reviewed and provided. Lastly, the challenges and prospects for developing effective biosensors for early cancer diagnosis are discussed.
Hyperspectral image super-resolution, which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation, aims to improve the spatial resolution of the ...hyperspectral image, which is beneficial for subsequent applications. The development of deep learning has promoted significant progress in hyperspectral image super-resolution, and the powerful expression capabilities of deep neural networks make the predicted results more reliable. Recently, several latest deep learning technologies have made the hyperspectral image super-resolution method explode. However, a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is absent. To this end, in this survey, we first introduce the concept of hyper-spectral image super-resolution and classify the methods from the perspectives with or without auxiliary information. Then, we review the learning-based methods in three categories, including single hyperspectral image super-resolution, panchromatic-based hyperspectral image super-resolution, and multispectral-based hyperspectral image super-resolution. Subsequently, we summarize the commonly used hyperspectral dataset, and the evaluations for some representative methods in three categories are performed qualitatively and quantitatively. Moreover, we briefly introduce several typical applications of hyperspectral image super-resolution, including ground object classification, urban change detection, and ecosystem monitoring. Finally, we provide the conclusion and challenges in existing learning-based methods, looking forward to potential future research directions.
Agricultural insurance is an important tool for promoting low-carbon agriculture and achieving the “Carbon Peaking and Neutrality” goal. Using panel data from 31 provinces in China from 2001 to 2020, ...this study analyzes the carbon-reducing effects of agricultural insurance in both crop and livestock sectors. The results show that: (1) Agricultural insurance can decrease agricultural carbon emissions. (2) For crops, agricultural insurance reduces carbon emissions through green technical efficiency, and for livestock products by green technological advances. (3) Agricultural insurance could lower carbon emissions from the livestock and crop sectors in the eastern region. The carbon-reducing benefits of agricultural insurance for the crop sector are seen in the agricultural, agro-pastoral, and pastoral domains; for the livestock sector, these impacts are only seen in the agricultural domains.
Previous deep learning-based super-resolution (SR) methods rely on the assumption that the degradation process is predefined (e.g., bicubic downsampling). Thus, their performance would suffer from ...deterioration if the real degradation is not consistent with the assumption. To deal with real-world scenarios, existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme. However, degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors. In this paper, we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples, respectively. Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space. Furthermore, instead of estimating the degradation, we extract global statistical prior information to capture the character of the distortion. Considering the coupling between the degradation and the low-resolution image, we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions. We term our distortion-specific network with contrastive regularization as CRDNet. The extensive experiments on synthetic and real-world scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.
