The behavior of real exchange rates: the case of Japan

The study examines the convergence rate of mean reversion by contrasting the estimated half-life of real exchange rate (RER). We employ an extensive monthly consumer price index (CPI)-based product price’s panel for Japan (the U.S. as the num´eraire). We find that the disaggregated RERs are persistent due to the cross-sectional dependence problems. By controlling common correlated effects, the estimated half-life for all goods may fall to as low as 2.54 years, below the consensus view of 3 to 5 years summarized by Rogoff (1996). After correcting the small-sample bias, the estimated half-life of deviations from purchasing power parity (PPP) increase by 1.03 year. Our findings also support that the half-life of mean reversion of RER is about 3.55 years for traded goods, about 0.11 year lower than non-traded goods. We also show that traded goods and non-traded goods perform distinct distributions of persistence

Threshold Effects in Cigarette Addiction: An Application of the Threshold Model in Dynamic Panels

We adopt the threshold model of myopic cigarette addiction to US state-level panel data. The threshold model is used to identify the structural effects of cigarette demand determinants across the income stratification. Furthermore, we apply a bootstrap approach to correct for the small-sample bias that arises in the dynamic panel threshold model with fixed effects. Our empirical results indicate that there exists the heterogeneity of smoking dynamics across consumers.Cigarettes demand, price elasticity, threshold regression model, dynamic panel model, bias correction, bootstrap

Editorial: Women in AI medicine and public health 2022

The landscape of technology has undergone a dramatic transformation with the widespread adoption and rapid advancement of artificial intelligence (AI). This evolution has had a profound impact on various sectors, reshaping not only the way industries operate but also fundamentally altering the way we perceive and interact with the world around us. In particular, AI are revolutionizing the fields of medicine and public health by offering innovative ways to analyze data, make predictions, improve patient care, and even playing central role in advancing agendas of inclusion and equality. However, gender disparity is still evident within the realms of AI, especially within the context of medicine and public health. Despite their accomplishments, women scientists continue to face gender-specific hurdles, such as navigating their public presence and cultivating secure, inclusive work environments. This Research Topic from Frontiers in Big Data aims to promote and highlight the research work of women scientists, across the fields of AI in medicine and public health. This Research Topic is part of the Women in Artificial Intelligence series. In each work, the first author or the last author needs to be a woman researcher. Each paper underwent a rigorous review process, involving at least two reviewers and two rounds of thorough revisions before acceptance. Six articles were selected that comprise four original research, one brief research report, and one study protocol. Listed below are the papers that made important contributions to this Research Topic

Identification of LRP8, CDCA7, and MLK4 as Novel Therapeutic Targets for Cancer Stem Cells in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is characterized by the lack of expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2. TNBC is the most challenging breast cancer subtype with poor prognosis, high metastatic potential, and lack of effective targeted therapies. Currently, chemotherapy remains the major strategy to treat TNBC. However, TNBC patients with residual disease after chemotherapy have higher risk of relapse and significantly worse survival than non-TNBC patients with residual disease. Therefore, there is an imperative need to identify novel and effective targeted therapies for TNBC. Cancer stem cells, also termed tumor-initiating cells, have been considered important targets for cancer treatment due to their high metastatic potential and resistance to conventional chemotherapy. In agreement with the inherently aggressive clinical behavior of TNBC, emerging evidence has demonstrated that breast cancer stem cells (BCSCs) are enriched in TNBC. Therefore, BCSCs serve as ideal therapeutic targets for TNBC. This study aims to identify novel therapeutic targets for BCSCs in TNBC. Based on the analysis of our unpublished RNA sequencing (RNA-Seq) data and patient data sets such as METABRIC and TCGA, we identify several potential oncogenes in TNBC. Our study further demonstrates that three of the candidates, namely cell division cycle associated 7 (CDCA7), low-density lipoprotein receptor-related protein 8 (LRP8), and mixed-lineage kinase 4 (MLK4), are functionally important to the maintenance of BCSCs in TNBC. The candidate genes are highly expressed in TNBC compared to other breast cancer subtypes according to the analysis of TCGA or METABRIC datasets. Genetic silencing of the candidate genes in TNBC cell lines significantly decreased CD44+/CD24- BCSCs and mammosphere formation in vitro. Furthermore, silencing of the genes suppressed both tumor growth and tumorigenesis in vivo. By analyzing the RNA-Seq data of the siRNA transfected TNBC cells, we found that knockdown of the candidate genes inhibited epithelial-to-mesenchymal transition (EMT), an important developmental program that can enrich stemness of cancer cells. Immunofluorescence staining of the xenograft tumor biopsies further revealed that the candidate gene knockdown decreased the expression of CD44 and increased the expression of CD24 and CK8/18, confirming the inhibition of EMT. Mechanistically, our RNA-Seq data analysis and experiments reveal that LRP8 and CDCA7 are critical to Wnt signaling and PRC2-mediated epithelial gene suppression, respectively. In addition, silencing of CDCA7 and MLK4 significantly dysregulates cell cycle of TNBC cells. Collectively, this study has demonstrated the benefits of targeting CDCA7, LRP8, and MLK4 to remove BCSCs and suppress tumorigenesis in TNBC. Therefore, our study uncovers LRP8, CDCA7, and MLK4 as novel therapeutic targets for TNBC.PHDPharmaceutical SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147607/1/ckfox_1.pd