Going public: why and when? Evidence from the UK

This thesis empirically investigates the motive for and the timing of initial public offerings (IPOs) in the UK. Due to an apparent lack of research, the two questions as to why firms choose to go public and how they time their IPOs are left under-addressed in the existing IPO literature. Answers to these questions are critical to understand IPO activities and the extent to which firms can make efficient use of the stock market. The empirical studies in this thesis were based on a large and unique sample of 183 UK IPOs that floated on the London Stock Exchange during 1998–2003 and a control group of 2135 UK firms that remained private during 1996–2007. [Continues.

Additional file 2: of Current prevalence status of gastric cancer and recent studies on the roles of circular RNAs and methods used to investigate circular RNAs

Figure S2. Distribution of gastric cancer incidence and mortality in China; the place with red spots indicates high incidence and mortality of gastric cancer (south and east of China). (JPG 300 kb

Additional file 1: of Current prevalence status of gastric cancer and recent studies on the roles of circular RNAs and methods used to investigate circular RNAs

Figure S1. Distribution of gastric cancer incidence and mortality worldwide; the place with red spots indicates high incidence and mortality of gastric cancer (from left to right: East Asia, Central and Eastern Europe and South America). (JPG 149 kb

Mobile money and financial inclusion: an analytical review

Book description: Inclusive Financial Development provides theoretical and empirical analyses of the nature of financial inclusion. The contributing authors explore the impediments to inclusion that exist around the world, the macro and stability implications, and the regulation dimension

Mobile money and financial inclusion: an analytical survey

We survey literature on mobile money and its contribution in promoting financial inclusion and development, with a focus on sub-Saharan Africa. We use taxonomic, descriptive and analytical methods to evaluate the state of knowledge in the area. We analyse how mobile technology in general may contribute to economic development and financial inclusion in theory and practise. We explain the mechanics of mobile money using Kenya's M-Pesa as a canonical example; and consider whether the literature has fully established the potential economic impact of mobile money especially its contribution to financial inclusion. We also consider market structure, pricing and regulatory implications of mobile money. We conclude by highlighting issues that require further investigation: the take-up of mobile money; mobile money and financial inclusion; substitutability between mobile money and conventional finance; and regulatory structures for institutions providing mobile money services.</p

DataSheet1_Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer.PDF

Background: Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)–signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism.Methods: The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus (GEO) database was used as the validation set. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and multivariate Cox regression analyses; and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments were used to predict the model-related potential pathways, m6A modifications, and the association between model genes and m6A.Results: A four-gene prognostic model (GHR, PIM1, IFNA8, and IFNB1) was constructed, namely, riskScore. The Kaplan–Meier curves suggested that patients with high riskScore expression had a poorer prognosis than those with low riskScore expression (p = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor (p Conclusion: This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.</p

Table4_Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer.XLS

Background: Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)–signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism.Methods: The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus (GEO) database was used as the validation set. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and multivariate Cox regression analyses; and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments were used to predict the model-related potential pathways, m6A modifications, and the association between model genes and m6A.Results: A four-gene prognostic model (GHR, PIM1, IFNA8, and IFNB1) was constructed, namely, riskScore. The Kaplan–Meier curves suggested that patients with high riskScore expression had a poorer prognosis than those with low riskScore expression (p = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor (p Conclusion: This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.</p

Table1_Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer.XLS

Background: Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)–signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism.Methods: The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus (GEO) database was used as the validation set. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and multivariate Cox regression analyses; and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments were used to predict the model-related potential pathways, m6A modifications, and the association between model genes and m6A.Results: A four-gene prognostic model (GHR, PIM1, IFNA8, and IFNB1) was constructed, namely, riskScore. The Kaplan–Meier curves suggested that patients with high riskScore expression had a poorer prognosis than those with low riskScore expression (p = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor (p Conclusion: This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.</p

sj-pdf-1-imr-10.1177_03000605211066397 - Supplemental material for Minimally invasive management of large duodenal lipoma: endoscopic submucosal dissection

Supplemental material, sj-pdf-1-imr-10.1177_03000605211066397 for Minimally invasive management of large duodenal lipoma: endoscopic submucosal dissection by Bin Yang, Fei Jiang, Pinxiang Lu and Huazhong Han in Journal of International Medical Research</p

Functional Censored Quantile Regression

We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.</p