Product Features and Customers Satisfaction of Patanjali Product: Evidence From Kathmandu, Nepal

The objectives of this study are to identify the features of Patanjali products and it focuses to identify the satisfaction level and purchasing decision of consumer by the features of Patanjali products. In this research, convenient sampling technique were used to get the sample, correlation and regression were used in order to get the result. Descriptive statistics is used to explain the respondents’ perception towards the features of Patanjali product. In this study, the data was collected through questionnaire from 300 respondents. This research used SPSS - 23 for analysis. This study has been done on four attributes or determinants of Patanjali product, they are price, quality, availability and healthy (organic) of product. The result of this study indicates that there is positive relationship between the features of Patanjali product and satisfaction. It can be said that the independents variables have effect on consumer satisfaction of Patanjali products. The producer and marketing managers are recommended to focus on the product features in order to obtain loyal satisfied customers.

An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer’s Disease

Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics. To investigate the intricate genetic basis of cerebrum imaging phenotypes, a great deal of other standard SCCA methods combining different interested structed have now appeared. For example, some models use group lasso penalty, and some use the fused lasso or the graph/network guided fused lasso for feature selection. However, prior knowledge may not be completely available and the group lasso methods have limited capabilities in practical applications. The graph/network guided approaches can use sample correlation to define constraints, thereby overcoming this problem. Unfortunately, this also has certain limitations. The graph/network conducted methods are susceptible to the sign of the sample correlation of the data, which will affect the stability of the model. To improve the efficiency and stability of SCCA, a sparse canonical correlation analysis model with GraphNet regularization (FGLGNSCCA) is proposed in this manuscript. Based on the FGLSCCA model, the GraphNet regularization penalty is imposed in our study and an optimization algorithm is presented to optimize the model. The structural Magnetic Resonance Imaging (sMRI) and gene expression data are used in this study to find the genotype and characteristics of brain regions associated with Alzheimer’s disease (AD). Experiment results shown that the new FGLGNSCCA model proposed in this manuscript is superior or equivalent to traditional methods in both artificially synthesized neuroimaging genetics data or actual neuroimaging genetics data. It can select essential features more powerfully compared with other multivariate methods and identify significant canonical correlation coefficients as well as captures more significant typical weight patterns which demonstrated its excellent ability in finding biologically important imaging genetic relations.

Metabolomics Study of Flavonoids of Taxilluschinensis on Different Hosts Using UPLC-ESI-MS/MS

The goal of this study was to identify and compare the main biomarkers of Taxillus chinensis from different hosts. A metabolomics approach utilizing ultra-pressure liquid chromatography coupled with tandem mass spectrometry (UPLC-MS), including cluster analysis, sample correlation analysis and orthogonal partial least squares discriminant analysis, was used to explore the flavonoid metabolites of Taxillus chinensis growing on different hosts. Results: The total flavonoids content (up to 30.08 mg/g) in Taxillus chinensis from Morus alba (CSG) was significantly higher than that from growth on Liquidambar formosana (CFG) or Clausena lansium (CHG) (p < 0.01). There were 23 different metabolites between CSG and CHG, 23 different metabolites between CSG and CFG, and 19 different metabolites between CHG and CFG. The results demonstrated that different hosts exerted a large influence on the metabolites of Taxillus chinensis; it was found that CSG differed from CFG and CHG in eleven metabolic compounds, ten of which were upregulated and one of which was downregulated. Most of these metabolites derive from compounds contained in the host plant, white mulberry (Morus alba); many feature potent anti-cancer effects. Differences in host can influence the type and abundance of flavonoids in parasitic plants such as Taxillus chinensis, which is of great significance to researchers seeking to understand the formation mechanism of Taxillus chinensis metabolites. Therefore, attention should be paid to the species of host plant when studying the Taxillus chinensis metabolome. Plants grown on Morus alba offer the greatest potential for the development of new anti-cancer drugs.

Development and validation of genomic and epigenomic signatures associated with tumor immune microenvironment in hepatoblastoma

Background This study aimed to probe and verify aberrantly methylated and expressed genes in hepatoblastoma and to analyze their interactions with tumor immune microenvironment. Methods Aberrantly methylated and expressed genes were obtained by comprehensively analyzing gene expression and DNA methylation profiles from GSE81928, GSE75271 and GSE78732 datasets. Their biological functions were predicted by the STRING and Metascape databases. CIBERSORT was utilized for inferring the compositions of tumor-infiltrating immune cells (TIICs) in each sample. Correlation between hub genes and immune cells was then analyzed. Hub genes were validated in hepatoblastoma tissues via western blot or immunohistochemistry. After transfection with sh-NOTUM, migration and invasion of HuH-6 and HepG2 cells were investigated. The nude mouse tumorigenesis model was constructed. Results Totally, 83 aberrantly methylated and expressed genes were determined in hepatoblastoma, which were mainly involved in metabolic and cancer-related pathways. Moreover, their expression was liver-specific. 13 hub genes were screened, which were closely related to immune cells in hepatoblastoma tissues. Among them, it was confirmed that AXIN2, LAMB1 and NOTUM were up-regulated and SERPINC1 was down-regulated in hepatoblastoma than normal tissues. NOTUM knockdown distinctly weakened migration and invasion of HuH-6 and HepG2 cells and tumor growth in vivo. Conclusions This study identified aberrantly methylated and expressed signatures that were in relation to immune microenvironment in hepatoblastoma. Targeting NOTUM hub gene could suppress migration and invasion of hepatoblastoma cells. Thus, these aberrantly methylated and expressed genes might act as therapeutic agents in hepatoblastoma therapy.

Limit Properties of the Largest Entries of High-Dimensional Sample Covariance and Correlation Matrices

In this paper, we consider the limit properties of the largest entries of sample covariance matrices and the sample correlation matrices. In order to make the statistics based on the largest entries of the sample covariance matrices and the sample correlation matrices more applicable in high-dimensional tests, the identically distributed assumption of population is removed. Under some moment’s assumption of the underlying distribution, we obtain that the almost surely limit and asymptotical distribution of the extreme statistics as both the dimension p and sample size n tend to infinity.

The Implementation of Talking Stick Method to Increase Muslim Prayer Movement Skills as an Extracurricular Program

This research is motivated by the use of the monotonous method of implementing the forum annisa extracurricular activities, namely the lecture method so that the skills of the participants are low. One of the skill tests conducted by the forum annisa is prayer movement skills. Therefore, it is necessary to renew the implementation method, namely applying the talking stick method in the process of implementing the forum annisa activities, especially the prayer movement material. This study aim to see the effect of the application of the talking stick method on improving prayer movemen skills in the extracurricular activities of the forum annisa for seventh grade students. This type of research is quantitative with an experimental approach with the type of Pre experimental design a one group pretest posttest design. The population of this study were all participants of the VII class forum annisa. Sampling was done by using cluster random sampling technique. Data collection techniques using pretest and posttest. Data collection tools in the form of knowledge tests in the form of question and skills tests in the form of performance appraisal sheets. While the data analysis technique uses the t-test sample correlation formula. The result was a significant effect between the application of the talking stick method on improving prayer movement skills with a value of th < -11.5099 < 2.06 on the knowledge test and th < to -12.0175 < 2.06 on the skills test.

Hierarchicell: an R-package for estimating power for tests of differential expression with single-cell data

Background Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for tests of differential expression in single-cell RNA-seq data focus on the total number of cells and not the number of independent experimental units, the true unit of interest for power. Thus, current methods grossly overestimate the power. Results Hierarchicell is the first single-cell power calculator to explicitly simulate and account for the hierarchical correlation structure (i.e., within sample correlation) that exists in single-cell RNA-seq data. Hierarchicell, an R-package available on GitHub, estimates the within sample correlation structure from real data to simulate hierarchical single-cell RNA-seq data and estimate power for tests of differential expression. This multi-stage approach models gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. Without modeling the within sample correlation structure and without properly accounting for the correlation in downstream analysis, we demonstrate that estimates of power are falsely inflated. Hierarchicell can be used to estimate power for binary and continuous phenotypes based on user-specified number of independent experimental units (e.g., individuals) and cells within the experimental unit. Conclusions Hierarchicell is a user-friendly R-package that provides accurate estimates of power for testing hypotheses of differential expression in single-cell RNA-seq data. This R-package represents an important addition to single-cell RNA analytic tools and will help researchers design experiments with appropriate and accurate power, increasing discovery and improving robustness and reproducibility.