Experimental spectra analysis in THM with the help of simulation based on Geant4 framework

The Coulomb barrier and electron screening cause difficulties in directly measuring nuclear reaction cross sections of charged particles in astrophysical energies. The Trojan-horse method has been introduced to solve the difficulties as a powerful indirect tool. In order to understand experimental spectra better, Geant4 is employed to simulate the method for the first time. Validity and reliability of the simulation are examined by comparing the experimental data with simulated results. The Geant4 simulation can give useful information to understand the experimental spectra better in data analysis and is beneficial to the design for future related experiments.Comment: 4 pages, 5 figure

Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes

BACKGROUND: Due to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using micorarray GeneChip(®), employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively. RESULTS: In addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16. CONCLUSION: This study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R^2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.Comment: 19 pages, 3 figure

Exploring the Discrepancy between Projected and Perceived Destination Images: A Cross-Cultural and Sustainable Analysis Using LDA Modeling

The projected image, created by destination marketing organizations, and the perceived image, formed by tourists’ perceptions, are crucial factors in destination selection. In this paper, machine learning models are used to construct projected image dimensions and perceptual dimensions for Chinese and English to analyze the similarities and differences between projected and perceptual images and their Chinese sustainability and cultural differences issues. We take Xiamen, a seaside tourist city in China, as an example, and analyze it by collecting 110,098 official promotional texts (both in Chinese and English) and tourist online review feedback as data sources using a latent Dirichlet allocation (LDA) model of natural language processing. The findings show that (1) the official projected image focuses on the overall image of the destination, while the tourists’ perceived image focuses on the specific image. (2) The official projected image covers the whole area of tourism, while the tourists’ perceived image focuses on Xiamen’s well-known attractions. The results of the above two points are the same for both the Chinese and English Topic models. (3) The official projected image focuses on three dimensions of destination: sustainability-economic, socio-cultural and environmental, while the tourist perception is more in the socio-cultural and environmental dimensions. (4) Both the projected and perceived images in Chinese and English differ in cross-cultural situations. The perceived images of Chinese and British tourists are influenced by their respective cultural backgrounds. Chinese tourists’ perceptions reflect cultural values associated with collectivism, long-term orientation, and uncertainty avoidance. On the other hand, British tourists’ perceptions align with cultural values of individualism, short-term orientation, and lower uncertainty avoidance. These differences can be explained using Hofstede’s cultural dimensions theory. The research in this paper can provide a reference for the promotion of tourism cities, and tourism destination organizations should not only focus on sustainable promotion, but also attract domestic and foreign tourists through differentiated promotion

Tests of association between quantitative traits and haplotypes in a reduced-dimensional space

Candidate gene association tests are currently performed using several intragenic SNPs simultaneously, by testing SNP haplotype or genotype effects in multifactorial diseases or traits. The number of haplotypes drastically increases with an increase in the number of typed SNPs. As a result, large numbers of haplotypes will introduce large degrees of freedom in haplotype-based tests, and thus limit the power of the tests. In this study we propose using the principal component method to reduce the dimension, and then construct association tests on the lower-dimensional space to test the association between haplotypes and a quantitative trait using population-based samples. The proposed method allows ambiguous haplotypes. We use simulation studies to evaluate the type I error rate of the tests, and compare the power of the proposed tests with that of the tests without dimension reduction, and the tests with dimension reduction by merging rare haplotypes. The simulation results show that the proposed tests have correct type I error rates and are more powerful than other tests in most cases considered in our simulation studies. © University College London 2005

Validated 1H and 13C Nuclear Magnetic Resonance Methods for the Quantitative Determination of Glycerol in Drug Injections

In the current study, we employed high-resolution proton and carbon nuclear magnetic resonance spectroscopy (1H and 13C NMR) for quantitative analysis of glycerol in drug injections without any complex pre-treatment or derivatization on samples. The established methods were validated with good specificity, linearity, accuracy, precision, stability, and repeatability. Our results revealed that the contents of glycerol were convenient to calculate directly via the integration ratios of peak areas with an internal standard in 1H NMR spectra, while the integration of peak heights were proper for 13C NMR in combination with an external calibration of glycerol. The developed methods were both successfully applied in drug injections. Quantitative NMR methods showed an extensive prospect for glycerol determination in various liquid samples

Haplotype sharing transmission/disequilibrium tests that allow for genotyping errors

The present study introduces new Haplotype Sharing Transmission/ Disequilibrium Tests (HS-TDTs) that allow for random genotyping errors. We evaluate the type I error rate and power of the new proposed tests under a variety of scenarios and perform a power comparison among the proposed tests, the HS-TDT and the single-marker TDT. The results indicate that the HS-TDT shows a significant increase in type I error when applied to data in which either Mendelian inconsistent trios are removed or Mendelian inconsistent markers are treated as missing genotypes, and the magnitude of the type I error increases both with an increase in sample size and with an increase in genotyping error rate. The results also show that a simple strategy, that is, merging each rare haplotype to a most similar common haplotype, can control the type I error inflation for a wide range of genotyping error rates, and after merging rare haplotypes, the power of the test is very similar to that without merging the rare haplotypes. Therefore, we conclude that a simple strategy may make the HS-TDT robust to genotyping errors. Our simulation results also show that this strategy may also be applicable to other haplotype-based TDT

Transmission/Disequilibrium Test Based on Haplotype Sharing for Tightly Linked Markers

Studies using haplotypes of multiple tightly linked markers are more informative than those using a single marker. However, studies based on multimarker haplotypes have some difficulties. First, if we consider each haplotype as an allele and use the conventional single-marker transmission/disequilibrium test (TDT), then the rapid increase in the degrees of freedom with an increasing number of markers means that the statistical power of the conventional tests will be low. Second, the parental haplotypes cannot always be unambiguously reconstructed. In the present article, we propose a haplotype-sharing TDT (HS-TDT) for linkage or association between a disease-susceptibility locus and a chromosome region in which several tightly linked markers have been typed. This method is applicable to both quantitative traits and qualitative traits. It is applicable to any size of nuclear family, with or without ambiguous phase information, and it is applicable to any number of alleles at each of the markers. The degrees of freedom (in a broad sense) of the test increase linearly as the number of markers considered increases but do not increase as the number of alleles at the markers increases. Our simulation results show that the HS-TDT has the correct type I error rate in structured populations and that, in most cases, the power of HS-TDT is higher than the power of the existing single-marker TDTs and haplotype-based TDTs

Research on the spatial allocation of national fitness resources at the street scale—taking Fuzhou city as an example

To explore the spatial allocation of national fitness resources at different spatial scales in Fuzhou city to provide useful references for optimizing and enhancing the spatial allocation of national fitness resources and urban planning. The equity, spatial distribution characteristics, accessibility and supply-demand balance of national fitness resources in Fuzhou city are analysed in depth by using the two-step mobile search method of multiple travel modes, the Gini coefficient, and exploratory spatial data analysis methods. The results show that the overall spatial allocation of national fitness resources is in a balanced state, but there are serious inequities and spatial mismatches in each district (county); the spatial distribution of national fitness resources is characterized by centralized agglomeration and surface dispersion, being dense in the south and sparse in the north. Areas with insufficient resources per capita have an agglomeration-type scattering distribution; areas with sufficient resources per capita have a dispersed patch distribution. Access to national fitness resources and the relationship between their supply and demand are characterized by positive spatial concentration; however, the layout of the national fitness resources planned for the old urban areas urgently needs to be optimized, with the Chating and Antai streets serving as centres, to increase the effective supply. Finally,we suggestions that Top-level design should be strengthened, the communalization of sports public services should be promoted, the service capacity and utilization efficiency of national fitness resources should be enhanced, and the construction of national fitness resources in new urban areas and new industrial agglomerations should be accelerated