sj-docx-1-jtr-10.1177_00472875211047273 – Supplemental material for Virtual Reality in Destination Marketing: Telepresence, Social Presence, and Tourists’ Visit Intentions

Supplemental material, sj-docx-1-jtr-10.1177_00472875211047273 for Virtual Reality in Destination Marketing: Telepresence, Social Presence, and Tourists’ Visit Intentions by Tianyu Ying, Jingyi Tang, Shun Ye, Xiaoyuan Tan and Wei Wei in Journal of Travel Research</p

Table_5_Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation.xlsx

BackgroundMembranous nephropathy (MN) is an autoimmune glomerular disease that is predominantly mediated by immune complex deposition and complement activation. The aim of this study was to identify key biomarkers of MN and investigate their association with immune-related mechanisms, inflammatory cytokines, chemokines and chemokine receptors (CCRs).MethodsMN cohort microarray expression data were downloaded from the GEO database. Differentially expressed genes (DEGs) in MN were identified, and hub genes were determined using a protein-protein interaction (PPI) network. The relationships between immune-related hub genes, immune cells, CCRs, and inflammatory cytokines were examined using immune infiltration analysis, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA). Finally, the immune-related hub genes in MN were validated using ELISA.ResultsIn total, 501 DEGs were identified. Enrichment analysis revealed the involvement of immune- and cytokine-related pathways in MN progression. Using WGCNA and immune infiltration analysis, 2 immune-related hub genes (CYBB and CSF1R) were identified. These genes exhibited significant correlations with a wide range of immune cells and were found to participate in B cell/T cell receptor and chemokine signaling pathways. In addition, the expressions of 2 immune-related hub genes were positively correlated with the expression of CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2.ConclusionOur study identified CSF1 and CYBB as immune-related hub genes that potentially influence the expression of CCRs and pro-inflammatory cytokines (CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2). CSF1 and CYBB may be potential biomarkers for MN progression, providing a perspective for diagnostic and immunotherapeutic targets of MN.</p

Table_2_Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation.xlsx

BackgroundMembranous nephropathy (MN) is an autoimmune glomerular disease that is predominantly mediated by immune complex deposition and complement activation. The aim of this study was to identify key biomarkers of MN and investigate their association with immune-related mechanisms, inflammatory cytokines, chemokines and chemokine receptors (CCRs).MethodsMN cohort microarray expression data were downloaded from the GEO database. Differentially expressed genes (DEGs) in MN were identified, and hub genes were determined using a protein-protein interaction (PPI) network. The relationships between immune-related hub genes, immune cells, CCRs, and inflammatory cytokines were examined using immune infiltration analysis, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA). Finally, the immune-related hub genes in MN were validated using ELISA.ResultsIn total, 501 DEGs were identified. Enrichment analysis revealed the involvement of immune- and cytokine-related pathways in MN progression. Using WGCNA and immune infiltration analysis, 2 immune-related hub genes (CYBB and CSF1R) were identified. These genes exhibited significant correlations with a wide range of immune cells and were found to participate in B cell/T cell receptor and chemokine signaling pathways. In addition, the expressions of 2 immune-related hub genes were positively correlated with the expression of CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2.ConclusionOur study identified CSF1 and CYBB as immune-related hub genes that potentially influence the expression of CCRs and pro-inflammatory cytokines (CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2). CSF1 and CYBB may be potential biomarkers for MN progression, providing a perspective for diagnostic and immunotherapeutic targets of MN.</p

DataSheet_1_Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation.xlsx

BackgroundMembranous nephropathy (MN) is an autoimmune glomerular disease that is predominantly mediated by immune complex deposition and complement activation. The aim of this study was to identify key biomarkers of MN and investigate their association with immune-related mechanisms, inflammatory cytokines, chemokines and chemokine receptors (CCRs).MethodsMN cohort microarray expression data were downloaded from the GEO database. Differentially expressed genes (DEGs) in MN were identified, and hub genes were determined using a protein-protein interaction (PPI) network. The relationships between immune-related hub genes, immune cells, CCRs, and inflammatory cytokines were examined using immune infiltration analysis, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA). Finally, the immune-related hub genes in MN were validated using ELISA.ResultsIn total, 501 DEGs were identified. Enrichment analysis revealed the involvement of immune- and cytokine-related pathways in MN progression. Using WGCNA and immune infiltration analysis, 2 immune-related hub genes (CYBB and CSF1R) were identified. These genes exhibited significant correlations with a wide range of immune cells and were found to participate in B cell/T cell receptor and chemokine signaling pathways. In addition, the expressions of 2 immune-related hub genes were positively correlated with the expression of CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2.ConclusionOur study identified CSF1 and CYBB as immune-related hub genes that potentially influence the expression of CCRs and pro-inflammatory cytokines (CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2). CSF1 and CYBB may be potential biomarkers for MN progression, providing a perspective for diagnostic and immunotherapeutic targets of MN.</p

Table_3_Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation.xlsx

BackgroundMembranous nephropathy (MN) is an autoimmune glomerular disease that is predominantly mediated by immune complex deposition and complement activation. The aim of this study was to identify key biomarkers of MN and investigate their association with immune-related mechanisms, inflammatory cytokines, chemokines and chemokine receptors (CCRs).MethodsMN cohort microarray expression data were downloaded from the GEO database. Differentially expressed genes (DEGs) in MN were identified, and hub genes were determined using a protein-protein interaction (PPI) network. The relationships between immune-related hub genes, immune cells, CCRs, and inflammatory cytokines were examined using immune infiltration analysis, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA). Finally, the immune-related hub genes in MN were validated using ELISA.ResultsIn total, 501 DEGs were identified. Enrichment analysis revealed the involvement of immune- and cytokine-related pathways in MN progression. Using WGCNA and immune infiltration analysis, 2 immune-related hub genes (CYBB and CSF1R) were identified. These genes exhibited significant correlations with a wide range of immune cells and were found to participate in B cell/T cell receptor and chemokine signaling pathways. In addition, the expressions of 2 immune-related hub genes were positively correlated with the expression of CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2.ConclusionOur study identified CSF1 and CYBB as immune-related hub genes that potentially influence the expression of CCRs and pro-inflammatory cytokines (CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2). CSF1 and CYBB may be potential biomarkers for MN progression, providing a perspective for diagnostic and immunotherapeutic targets of MN.</p

Table_1_Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation.xlsx

BackgroundMembranous nephropathy (MN) is an autoimmune glomerular disease that is predominantly mediated by immune complex deposition and complement activation. The aim of this study was to identify key biomarkers of MN and investigate their association with immune-related mechanisms, inflammatory cytokines, chemokines and chemokine receptors (CCRs).MethodsMN cohort microarray expression data were downloaded from the GEO database. Differentially expressed genes (DEGs) in MN were identified, and hub genes were determined using a protein-protein interaction (PPI) network. The relationships between immune-related hub genes, immune cells, CCRs, and inflammatory cytokines were examined using immune infiltration analysis, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA). Finally, the immune-related hub genes in MN were validated using ELISA.ResultsIn total, 501 DEGs were identified. Enrichment analysis revealed the involvement of immune- and cytokine-related pathways in MN progression. Using WGCNA and immune infiltration analysis, 2 immune-related hub genes (CYBB and CSF1R) were identified. These genes exhibited significant correlations with a wide range of immune cells and were found to participate in B cell/T cell receptor and chemokine signaling pathways. In addition, the expressions of 2 immune-related hub genes were positively correlated with the expression of CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2.ConclusionOur study identified CSF1 and CYBB as immune-related hub genes that potentially influence the expression of CCRs and pro-inflammatory cytokines (CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2). CSF1 and CYBB may be potential biomarkers for MN progression, providing a perspective for diagnostic and immunotherapeutic targets of MN.</p

Table_4_Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation.xlsx

BackgroundMembranous nephropathy (MN) is an autoimmune glomerular disease that is predominantly mediated by immune complex deposition and complement activation. The aim of this study was to identify key biomarkers of MN and investigate their association with immune-related mechanisms, inflammatory cytokines, chemokines and chemokine receptors (CCRs).MethodsMN cohort microarray expression data were downloaded from the GEO database. Differentially expressed genes (DEGs) in MN were identified, and hub genes were determined using a protein-protein interaction (PPI) network. The relationships between immune-related hub genes, immune cells, CCRs, and inflammatory cytokines were examined using immune infiltration analysis, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA). Finally, the immune-related hub genes in MN were validated using ELISA.ResultsIn total, 501 DEGs were identified. Enrichment analysis revealed the involvement of immune- and cytokine-related pathways in MN progression. Using WGCNA and immune infiltration analysis, 2 immune-related hub genes (CYBB and CSF1R) were identified. These genes exhibited significant correlations with a wide range of immune cells and were found to participate in B cell/T cell receptor and chemokine signaling pathways. In addition, the expressions of 2 immune-related hub genes were positively correlated with the expression of CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2.ConclusionOur study identified CSF1 and CYBB as immune-related hub genes that potentially influence the expression of CCRs and pro-inflammatory cytokines (CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2). CSF1 and CYBB may be potential biomarkers for MN progression, providing a perspective for diagnostic and immunotherapeutic targets of MN.</p

Nonlinearly Shaped Pulses in Photoinjectors and Free-Electron Lasers

Photoinjectors and Free Electron Lasers (FEL) are amongst the most advanced systems in accelerator physics and have consistently pushed the boundaries of emittance and x-ray peak power. In this paper, laser shaping at the cathode is proposed to further lower the emittance and reduce electron beam tails, which would result in brighter x-ray production. Using dispersion controlled nonlinear shaping (DCNS), laser pulses and beam dynamics were simulated in LCLS-II. The photoinjector emittance was optimized and the resulting e-beam profiles were then simulated and optimized in the linac. Finally, the expected FEL performance is estimated and compared to the current technology: Gaussian laser pulses on the cathode. The e-beams produced by DCNS pulses show a potential for 35% increase in x-ray power per pulse during SASE when compared to the standard Gaussian laser pulses

Image2_Association between body fat distribution and age at menarche: a two sample Mendelian randomization study.pdf

BackgroundNumerous studies have examined the association between obesity and age at menarche (AAM), with most focusing on traditional obesity indicators such as body mass index. However, there are limited studies that explored the connection between body fat distribution and AAM, as well as a scarcity of Mendelian randomization (MR) studies.MethodsIn this study, we conducted a two-sample MR study to evaluate the causal effects of eight body fat distribution indicators on AAM. Inverse variance weighted (IVW) method was used for primary analysis, while supplementary approaches such as MR-Egger and weighted median were also utilized. Considering that the eight exposures were highly correlated, we performed an MR Bayesian model averaging (MR-BMA) analysis to prioritize the effect of major exposure on AAM. A series of sensitivity analyses were also performed.ResultsFrom a range of 82–105 single nucleotide polymorphisms (SNPs) were utilized as genetic instrumental variables for each of the exposure factors. After Bonferroni correction, we found that whole body fat mass (β: −0.17; 95% CI: −0.24, −0.11), left leg fat percentage (β: −0.14; 95% CI: −0.21, −0.07), left leg fat mass (β: −0.20; 95% CI: −0.27, −0.12), left arm fat percentage (β: −0.18; 95% CI: −0.26, −0.11) and left arm fat mass (β: −0.18; 95%CI: −0.26, −0.10) were associated with decreased AAM using random effects IVW method. And the beta coefficients for all MR evaluation methods exhibited consistent trends. MR-BMA method validated that left arm fat percentage plays a dominant role in AAM.ConclusionsOur MR study suggested that body fat has broad impacts on AAM. Obtaining more information on body measurements would greatly enhance our comprehension of pubertal development.</p

Image1_Association between body fat distribution and age at menarche: a two sample Mendelian randomization study.pdf

BackgroundNumerous studies have examined the association between obesity and age at menarche (AAM), with most focusing on traditional obesity indicators such as body mass index. However, there are limited studies that explored the connection between body fat distribution and AAM, as well as a scarcity of Mendelian randomization (MR) studies.MethodsIn this study, we conducted a two-sample MR study to evaluate the causal effects of eight body fat distribution indicators on AAM. Inverse variance weighted (IVW) method was used for primary analysis, while supplementary approaches such as MR-Egger and weighted median were also utilized. Considering that the eight exposures were highly correlated, we performed an MR Bayesian model averaging (MR-BMA) analysis to prioritize the effect of major exposure on AAM. A series of sensitivity analyses were also performed.ResultsFrom a range of 82–105 single nucleotide polymorphisms (SNPs) were utilized as genetic instrumental variables for each of the exposure factors. After Bonferroni correction, we found that whole body fat mass (β: −0.17; 95% CI: −0.24, −0.11), left leg fat percentage (β: −0.14; 95% CI: −0.21, −0.07), left leg fat mass (β: −0.20; 95% CI: −0.27, −0.12), left arm fat percentage (β: −0.18; 95% CI: −0.26, −0.11) and left arm fat mass (β: −0.18; 95%CI: −0.26, −0.10) were associated with decreased AAM using random effects IVW method. And the beta coefficients for all MR evaluation methods exhibited consistent trends. MR-BMA method validated that left arm fat percentage plays a dominant role in AAM.ConclusionsOur MR study suggested that body fat has broad impacts on AAM. Obtaining more information on body measurements would greatly enhance our comprehension of pubertal development.</p