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Your Inside along with Posterior Data compresion of the Distal Femoral Vein: A great Underused Way of Detect Venous Thrombosis.
The purpose of this research is to develop a maximum likelihood estimator (MLE) for lifetime performance index C L for the parameter of mixture Rayleigh-Half Normal distribution (RHN) under progressively type-II right-censored samples under the constraint of knowing the lower specification limit (L). Additionally, we suggest an asymptotic normal distribution for the MLE for C L in order to construct a mechanism for evaluating products' lifespan efficiency. We have specified all the steps to carry out the test. Additionally, not only does hypothesis testing successfully assess the lifetime performance of items, but it also functions as a supplier selection criterion for the consumer. Finally, we have added two real data examples as illustration examples. These two applications are provided to demonstrate how the results can be applied.The current unsupervised domain adaptation person re-identification (re-ID) method aims to solve the domain shift problem and applies prior knowledge learned from labelled data in the source domain to unlabelled data in the target domain for person re-ID. At present, the unsupervised domain adaptation person re-ID method based on pseudolabels has obtained state-of-the-art performance. This method obtains pseudolabels via a clustering algorithm and uses these pseudolabels to optimize a CNN model. Although it achieves optimal performance, the model cannot be further optimized due to the existence of noisy labels in the clustering process. In this paper, we propose a stable median centre clustering (SMCC) for the unsupervised domain adaptation person re-ID method. SMCC adaptively mines credible samples for optimization purposes and reduces the impact of label noise and outliers on training to improve the performance of the resulting model. In particular, we use the intracluster distance confidence measure of the sample and its K-reciprocal nearest neighbour cluster proportion in the clustering process to select credible samples and assign different weights according to the intracluster sample distance confidence of samples to measure the distances between different clusters, thereby making the clustering results more robust. The experiments show that our SMCC method can select credible and stable samples for training and improve performance of the unsupervised domain adaptation model. Our code is available at https//github.com/sunburst792/SMCC-method/tree/master.In high-paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map-based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of parallelized deep convolutional neural network (DCNN) training; a correction is proposed in the Map stage. The secant conjugate gradient method (CGMSE) realizes the fast convergence of the conjugate gradient method and improves the convergence speed of the network; in the Reduce stage, a load balancing strategy to control the load rate (LBRLA) is proposed to achieve fast and uniform data grouping to ensure the parallelization performance of the parallel system. Finally, the related fatigue algorithm's research and simulation based on the human eye are carried out on the PC. learn more The human face and eye area are detected from the video image collected using the USB camera, and the frame difference method and the position information of the human eye on the face are used. To track the human eye area, extract the relevant human eye fatigue characteristics, combine the blink frequency, closed eye duration, PERCLOS, and other human eye fatigue determination mechanisms to determine the fatigue state, and test and verify the designed platform and algorithm through experiments. This system is designed to enable people who doze off, such as drivers, to discover their state in time through the system and reduce the possibility of accidents due to fatigue.In the 21st century, with the increasingly urgent requirements for lightweight in the fields of aviation, aerospace, and electronics, especially automobiles, many properties of magnesium alloy materials, especially the low-density performance characteristics, have been widely valued. In order to better use magnesium metal materials, it is very important to evaluate its mechanical properties. This article is based on 196 sets of mechanical performance experimental results and related data of AZ31 and AZ91 2 magnesium alloys. Based on data analysis and sorting, take deformation temperature, deformation rate, deformation coefficient, solid solution temperature, and solid solution time as input and take ultimate tensile strength (UTS), yield strength (YS), and elongation (ELO) as output. The 5-8-1 three-layer BP neural network forecast model optimized by the genetic algorithm is used for data training. The training results show that the prediction model can accurately predict the tensile strength, yield strength, and elongation. Compared with the general BP neural network prediction model, the BP neural network based on the genetic algorithm has small discreteness and high fitness the average error of UTS and YS of AZ31 magnesium alloy is reduced to 0.88% and 3.3%, respectively. The most obvious is that the elongation of AZ31 ELO is reduced, and the error is reduced to 8.1%.Here presented a quick and easy synthesis of copper nanoparticles (CuNPs). Pistachio hull extract has been used as a reducing and stabilizing agent in the preparation of CuNPs. This biosynthesis is a kind of supporter of the environment because chemical agents were not used to making nanoparticles, and on the other hand, it prevents the release of pistachio waste in nature and its adverse effects on nature. The biosynthesized CuNPs and CuNPs/silver Schiff base nanocomposite (CSS NC) were characterized by UV-VIS spectroscopy, Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and energy-dispersive X-ray spectroscopy (EDS). CuNP and CSS NC antimicrobial activity was examined by both well diffusion and determination MIC methods against four bacteria Staphylococcus aureus, Bacillus cereus, Escherichia coli, and Pseudomonas aeruginosa and two fungi Aspergillus Niger and Candida albicans. CuNPs and CSS NC showed significant antimicrobial activity on the samples, preventing the growth of bacteria and fungi at very low concentrations. CuNPs and CSS NC had the greatest effect on Escherichia coli bacteria and Aspergillus niger fungi. Phenolic compounds are one of the most important antioxidants that are involved in various fields, including pharmacy. Pistacia vera hull is a rich source of phenolic compounds. In this study, the most phenolic compound in Pistacia vera hull is gallic acid and rutin, which has been identified by HPLC analysis. In this study, Pistacia vera hull essential oil analysis was performed by the GC-MS method, in which α-pinene, D-limonene, and isobornyl acetate compounds constitute the highest percentage of Pistacia vera hull essential oil.Enhancer of zeste homolog 2 (EZH2) is abnormally highly expressed in pancreatic cancer (PC). However, it is not ideal to treat PC by inhibiting EZH2. This study reported that the combined use of pan-peroxisome proliferator-activated receptor (PPAR) agonist could significantly improve the anti-PC effect of EZH2 inhibitor. In vitro, PC cell lines PANC-1 and AsPC-1 were cultured, and MTT and flow cytometry were performed to observe the effects of pan-PPAR agonist bezafibrate and EZH2 selective inhibitor GSK126 on cell viability and apoptosis. In vivo, CDXs of PANC-1 and AsPC-1 were established to observe the effects of bezafibrate and GSK126 on bearing tumors. Western blotting was performed to detect the protein expressions of H3K27me3, β-catenin, p-β-catenin, cyclin D1, c-Myc, and cleaved caspase 3 in vitro and in vivo. The results showed that bezafibrate significantly improved the effects of GSK126 on proliferation inhibition and apoptosis promotion in vitro and the growth suppression of CDX tumors in vivo. It also significantly enhanced the effects of GSK126 on upregulating the expression level of p-β-catenin and that of cleaved caspase 3 in vitro and in vivo. In parallel, downregulation of the expression levels of H3K27me3, β-catenin, cyclin D1, and c-Myc was also observed in vitro or in vivo. These results suggest that the combination of bezafibrate and GSK126 has synergistic effects on PC, and the molecular mechanism may be related to the enhanced inhibition of the Wnt/β-catenin signaling pathway. We believe that targeting the EZH2-PPAR axis is a potential therapeutic pathway for PC.Breast cancer is one of the most common malignant tumors in women, which seriously endangers women's health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.Polyploidy, which is widely distributed in angiosperms, presents extremely valuable commercial applications in plant growth and reproduction. The flower development process of higher plants is essential for genetic improvement. Nevertheless, the reproduction difference between polyploidy and the polyploid florescence regulatory network from the perspective of microRNA (miRNA) remains to be elucidated. In this study, the autotetraploid of Lycium ruthenicum showed late-flowering traits compared with the progenitor. Combining the association of miRNA and next-generation transcriptome technology, the late-flowering characteristics triggered by chromosome duplication may be caused by the age pathway involved in miR156-SPLs and miR172-AP2, which inhibits the messenger RNA (mRNA) transcripts of FT in the leaves. Subsequently, FT was transferred to the shoot apical meristem (SAM) to inhibit the expression of the flowering integration factor SOC1, which can eventually result in delayed flowering time. Our exploration of the flowering regulation network and the control of the flowering time are vital to the goji producing in the late frost area, which provides a new perspective for exploring the intrinsic molecular mechanism of polyploid and the reproductive development of flowering plants.
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