Kernel principal component analysis (PCA) control chart for monitoring mixed non-linear variable and attribute quality characteristics

Ahsan, Muhammad; Mashuri, Muhammad; Khusna, Hidayatul; Wibawati,

doi:10.1016/j.heliyon.2022.e09590

Cited by 8 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Principal component analysis (PCA) is a multivariate statistical method to investigate the correlation between multiple variables. It studies how to reveal the internal structure of multiple variables through a few principal components and tries to recombine the original many with certain correlations into a new set of independent, comprehensive indicators to replace the original indicators ( Ahsan et al, 2022 ). The statistical evaluation by PCA showed samples from Marsdenia tenacissima (MT), Ganoderma lucidum (GL) and Marsdenia tenacissima with Ganoderma lucidum co-fermentation (MGF) were located in different areas of the figure, which suggested that each sample have different metabolites ( Figure 7D ).…”

Section: Resultsmentioning

confidence: 99%

Nontargeted metabolomics study and pharmacodynamic evaluation of bidirectional fermentation for Ganoderma lucidum with Marsdenia tenacissima

Zhang²,

Su³

et al. 2022

Front. Pharmacol.

View full text Add to dashboard Cite

Lung cancer is one of the malignant tumors with the fastest incidence rate and mortality growth and the greatest threat to human health and life. Marsdenia tenacissima is an antitumor of Chinese medicine. However, Marsdenia tenacissima has low bioavailability in the human body and most of its main active substances are aglycones, such as Tenacigenin A, Tenacigenin B. This study aims to produce biotransformation products rich in pungent saponins by using Marsdenia tenacissima as a fermentation medium of Ganoderma lucidum. Non-targeted metabolomics analysis was carried out on the fermentation products after the optimization process. A total of 249 differential metabolites were detected, and the content of saponins increased from 0.1% to 0.41% and most of them were tenacigenin. Furthermore, the biotransformation of C21 steroidal glycosides in Marsdenia tenacissima was the central reaction in this fermentation process. Pharmacodynamics resewed that the anticancer effect of Marsdenia tenacissima was significantly enhanced after fermentation, mainly through inhibiting the growth and apoptosis of cancer cells.

show abstract

Section: Resultsmentioning

confidence: 99%

Nontargeted metabolomics study and pharmacodynamic evaluation of bidirectional fermentation for Ganoderma lucidum with Marsdenia tenacissima

Zhang²,

Su³

et al. 2022

Front. Pharmacol.

View full text Add to dashboard Cite

show abstract

“…To allow for comparison of different data findings, the logFC transformation equation was used to normalize the expression values for each data point in each expression data condition [ 32 ]. The dataset was downloaded through the GEOquery package (2.54.1 version), using the surrogate variable analysis (SVA) package (3.34.0 version), LIMMA package (3.42.2 version), umap package (0.2.7.0 version) (UMAP analysis) [ 33 ], ggplot2 package (3.3.3 version) and Complex Heatmap package (2.2.0 version) to organize and analyze datasets. The data analysis process is shown in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Identification of Differential Expression Genes between Volume and Pressure Overloaded Hearts Based on Bioinformatics Analysis

Zhao

Zhou

et al. 2022

Genes

View full text Add to dashboard Cite

Volume overload (VO) and pressure overload (PO) are two common pathophysiological conditions associated with cardiac disease. VO, in particular, often occurs in a number of diseases, and no clinically meaningful molecular marker has yet been established. We intend to find the main differential gene expression using bioinformatics analysis. GSE97363 and GSE52796 are the two gene expression array datasets related with VO and PO, respectively. The LIMMA algorithm was used to identify differentially expressed genes (DEGs) of VO and PO. The DEGs were divided into three groups and subjected to functional enrichment analysis, which comprised GO analysis, KEGG analysis, and the protein–protein interaction (PPI) network. To validate the sequencing data, cardiomyocytes from AR and TAC mouse models were used to extract RNA for qRT-PCR. The three genes with random absolute values of LogFC and indicators of heart failure (natriuretic peptide B, NPPB) were detected: carboxylesterase 1D (CES1D), whirlin (WHRN), and WNK lysine deficient protein kinase 2 (WNK2). The DEGs in VO and PO were determined to be 2761 and 1093, respectively, in this study. Following the intersection, 305 genes were obtained, 255 of which expressed the opposing regulation and 50 of which expressed the same regulation. According to the GO and pathway enrichment studies, DEGs with opposing regulation are mostly common in fatty acid degradation, propanoate metabolism, and other signaling pathways. Finally, we used Cytoscape’s three techniques to identify six hub genes by intersecting 255 with the opposite expression and constructing a PPI network. Peroxisome proliferator-activated receptor (PPARα), acyl-CoA dehydrogenase medium chain (ACADM), patatin-like phospholipase domain containing 2 (PNPLA2), isocitrate dehydrogenase 3 (IDH3), heat shock protein family D member 1 (HSPD1), and dihydrolipoamide S-acetyltransferase (DLAT) were identified as six potential genes. Furthermore, we predict that the hub genes PPARα, ACADM, and PNPLA2 regulate VO myocardial changes via fatty acid metabolism and acyl-Coa dehydrogenase activity, and that these genes could be employed as basic biomarkers for VO diagnosis and treatment.

show abstract

“…Variable and attribute charts are the two primary categories of Shewhart charts. These categories are depending on the qualities being measured (Ahsan et al, 2022). If the sample's quality parameters can be measured and stated in numbers, then a variable control chart should be utilized.…”

Section: 𝑈𝐶𝐿 = 𝜇 𝑤 + 𝑘𝜎 𝑤mentioning

confidence: 99%

Control Chart for Correcting the ARIMA Time Series Model of GDP Growth Cases

Imro'ah,

Huda,

Utami

et al. 2024

JTAM

View full text Add to dashboard Cite

The essential prerequisite for attending the G20 conference is a country's GDP because G20 members can significantly boost the economy and preserve the nation's financial stability. Time series data can be thought of as a country's Gross Domestic Product (GDP) at a particular point in time. In this research, the GDP numbers from five Southeast Asian nations that are attending the G20 fulfilling are used. The total was 47 observations made yearly, which extended from 1975 to 2001. A time series analysis was performed on the data gathered. The correctness of time series models is also evaluated using control charts based on this research. The control chart is constructed using the time series model's residuals as observations. After applying the IMR control chart for verification, the results revealed that the residuals, specifically the models for GDP in Malaysia, Singapore, and Thailand, are out of control. The white noise assumption is fulfilled by the time series model obtained for Brunei and Indonesia's GDP, but the residuals are out of control. Whether controlled residuals are used depends on the accuracy with which the time series model predicts the future. If the amount of residuals is under control, then the time series model produced is accurate and good enough for prediction. After using the IMR control chart to verify the residuals, the results indicate that the residuals, namely the models for GDP in Malaysia, Singapore, and Thailand, are not under control. The assumption of white noise is proved correct by the time series model obtained for the GDP of Brunei Darussalam and Indonesia. With that being said, the residuals are entirely out of control. The model must improve its ability to forecast various future periods. It is a consequence of the unmanageable residuals that the model contains. Even if the best available model has been obtained based on the criteria that have been defined, it is anticipated that the research findings will improve the theories that have previously been developed and raise knowledge regarding the usefulness of testing the time series model. In addition to all of that, it is intended that the research will produce a summary of cases of an increase in GDP from five Southeast Asian countries participating in the G20 conference.

show abstract

Kernel principal component analysis (PCA) control chart for monitoring mixed non-linear variable and attribute quality characteristics

Cited by 8 publications

References 37 publications

Nontargeted metabolomics study and pharmacodynamic evaluation of bidirectional fermentation for Ganoderma lucidum with Marsdenia tenacissima

Nontargeted metabolomics study and pharmacodynamic evaluation of bidirectional fermentation for Ganoderma lucidum with Marsdenia tenacissima

Identification of Differential Expression Genes between Volume and Pressure Overloaded Hearts Based on Bioinformatics Analysis

Control Chart for Correcting the ARIMA Time Series Model of GDP Growth Cases

Contact Info

Product

Resources

About