A Generalized Endogenous Grid Method for Default Risk Models

Default risk models have been widely employed to assess the ability of households and sovereigns to insure themselves against shocks. Grid search has often been used to solve these models because the complexity of the problem prevents the use of faster but less general methods. In this paper, we propose an extension of the endogenous grid method for default risk models, which is faster and more accurate than grid search. In particular, we find that our solution method leads to a more accurate bond price function, thus making substantial differences in the model’s main predictions. When applied to Arellano’s (2008) model, our approach predicts a standard deviation of the interest rate spread one-third lower and defaults 3 to 5 times less frequently than does the conventional approach. On top of that, our method is efficient. It is approximately 4 to 7 times faster than grid search when applied to a canonical model of Arellano (2008) and 19 to 27 times faster than grid search when applied to the richer model of Nakajima and R´ıos-Rull (2014). Finally, we show that our method is applicable to a broad class of default risk models by characterizing sufficient conditions

A Generalized Endogenous Grid Method for Models with the Option to Default

We develop an endogenous grid method for models with the option to default in which price schedules are endogenously determined in equilibrium and depend on individuals’ states. The algorithm has noticeable computational benefits in efficiency and accuracy. We obtain these computational benefits by combining Fella’s (2014) identification for non-concave regions with our algorithm that numerically searches for risky borrowing limits. These two procedures identify the region of solution sets to which Carroll’s (2006) endogenous grid method is applicable. To demonstrate the method, we apply our method to Nakajima and Rios-Rull’s(2014) model. In terms of computation time, this method is seven to twenty-seven times faster than the conventional grid search method. Moreover, various types of accuracy tests indicate that our method yields more accurate results than the grid search method

Continually Updating Generative Retrieval on Dynamic Corpora

Generative retrieval has recently been gaining a lot of attention from the research community for its simplicity, high performance, and the ability to fully leverage the power of deep autoregressive models. However, prior work on generative retrieval has mostly investigated on static benchmarks, while realistic retrieval applications often involve dynamic environments where knowledge is temporal and accumulated over time. In this paper, we introduce a new benchmark called STREAMINGIR, dedicated to quantifying the generalizability of retrieval methods to dynamically changing corpora derived from StreamingQA, that simulates realistic retrieval use cases. On this benchmark, we conduct an in-depth comparative evaluation of bi-encoder and generative retrieval in terms of performance as well as efficiency under varying degree of supervision. Our results suggest that generative retrieval shows (1) detrimental performance when only supervised data is used for fine-tuning, (2) superior performance over bi-encoders when only unsupervised data is available, and (3) lower performance to bi-encoders when both unsupervised and supervised data is used due to catastrophic forgetting; nevertheless, we show that parameter-efficient measures can effectively mitigate the issue and result in competitive performance and efficiency with respect to the bi-encoder baseline. Our results open up a new potential for generative retrieval in practical dynamic environments. Our work will be open-sourced.Comment: Work in progres

Between comments and repeat visit: capturing repeat visitors with a hybrid approach

Purpose Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews. Design/methodology/approach The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision). Findings The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses. Originality/value The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services

Overexpression of cathepsin S exacerbates lupus pathogenesis through upregulation TLR7 and IFN-α in transgenic mice

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple organs. Recent studies suggest relevance between cysteine protease cathepsin S (CTSS) expression and SLE. To investigate the mechanism of CTSS in SLE, CTSS-overexpressing transgenic (TG) mice were generated, and induced lupus-like symptoms. Eight months later, the TG mice spontaneously developed typical SLE symptoms regardless of the inducement. Furthermore, we observed increased toll-like receptor 7 (TLR7) expression with increased monocyte and neutrophil populations in the TG mice. In conclusion, overexpression of CTSS in mice influences TLR7 expression, autoantibodies and IFN-α, which leads to an autoimmune reaction and exacerbates lupus-like symptoms. © 2021, The Author(s).1

Association between exposure to traffic-related air pollution and pediatric allergic diseases based on modeled air pollution concentrations and traffic measures in Seoul, Korea: a comparative analysis

Background Pediatric allergic diseases are a major public health concern, and previous studies have suggested that exposure to traffic-related air pollution (TRAP) exposure is a risk factor. These studies have typically assessed TRAP exposure using traffic measures, such as distance to major roads, or by modeling air pollutant concentrations; however inconsistent associations with pediatric allergic diseases have often been found. Using road proximity and density, we previously found an association between TRAP and atopic eczema among approximately 15,000 children living in Seoul, Korea, heavily populated and highly polluted city in which traffic is a major emission source. We aimed to conduct a parallel analysis using modeled air pollution concentrations and thus examine the consistency of the association. Specifically, we examined the associations of individual-level annual-average concentrations of NO2, PM10, and PM2.5 with symptoms and diagnoses of three pediatric allergic diseases including asthma, allergic rhinitis, and atopic eczema. Methods The study population included 14,614 children from the Seoul Atopy Friendly School Project Survey in Seoul, Korea, in 2010. To assess individual exposures to TRAP among these children, we predicted annual-average concentrations of NO2, PM10, and PM2.5 at the childrens home addresses in 2010 using universal kriging and land use regression models along with regulatory air quality monitoring data and geographic characteristics. Then, we estimated odds ratios (ORs) of the three allergic diseases for interquartile increases in air pollution concentrations after adjusting for individual risk factors in mixed effects logistic regression. Results Symptoms and diagnoses of atopic eczema symptoms showed an association with NO2 (OR = 1.07, 95% confidence interval = 1.02–1.13; 1.08, 1.03–1.14) and PM10 (1.06, 1.01–1.12; 1.07, 1.01–1.13). ORs of PM2.5 were positive but not statistically significant (1.01, 0.95–1.07; 1.04, 0.98–1.10). No association was found between asthma and allergic rhinitis, although PM2.5 showed a marginal association with allergic rhinitis. Conclusions Our consistent findings regarding the association between TRAP and the prevalence of atopic eczema using traffic measures and surrogate air pollutants suggested the effect of TRAP on childrens health. Follow-up studies should elucidate the causal link, to support subsequent policy considerations and minimize adverse health effects in children.This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A6A3A04059017, 2018R1A2B6004608) and the National Cancer Center of Korea (NCC-1810220-01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

JAZF1 heterozygous knockout mice show altered adipose development and metabolism

Background: Juxtaposed with another zinc finger protein 1 (JAZF1) is associated with metabolic disorders, including type 2 diabetes mellitus (T2DM). Several studies showed that JAZF1 and body fat mass are closely related. We attempted to elucidate the JAZF1 functions on adipose development and related metabolism using in vitro and in vivo models. Results: The JAZF1 expression was precisely regulated during adipocyte differentiation of 3T3-L1 preadipocyte and mouse embryonic fibroblasts (MEFs). Homozygous JAZF1 deletion (JAZF1-KO) resulted in impaired adipocyte differentiation in MEF. The JAZF1 role in adipocyte differentiation was demonstrated by the regulation of PPARγ—a key regulator of adipocyte differentiation. Heterozygous JAZF1 deletion (JAZF1-Het) mice fed a normal diet (ND) or a high-fat diet (HFD) had less adipose tissue mass and impaired glucose homeostasis than the control (JAZF1-Cont) mice. However, other metabolic organs, such as brown adipose tissue and liver, were negligible effect on JAZF1 deficiency. Conclusion: Our findings emphasized the JAZF1 role in adipocyte differentiation and related metabolism through the heterozygous knockout mice. This study provides new insights into the JAZF1 function in adipose development and metabolism, informing strategies for treating obesity and related metabolic disorders. © 2021, The Author(s).1

Targeting AKT with costunolide suppresses the growth of colorectal cancer cells and induces apoptosis in vitro and in vivo

BACKGROUND: Colorectal cancer (CRC) is a clinically challenging malignant tumor worldwide. As a natural product and sesquiterpene lactone, Costunolide (CTD) has been reported to possess anticancer activities. However, the regulation mechanism and precise target of this substance remain undiscovered in CRC. In this study, we found that CTD inhibited CRC cell proliferation in vitro and in vivo by targeting AKT. METHODS: Effects of CTD on colon cancer cell growth in vitro were evaluated in cell proliferation assays, migration and invasion, propidium iodide, and annexin V-staining analyses. Targets of CTD were identified utilizing phosphoprotein-specific antibody array; Costunolide-sepharose conjugated bead pull-down analysis and knockdown techniques. We investigated the underlying mechanisms of CTD by ubiquitination, immunofluorescence staining, and western blot assays. Cell-derived tumour xenografts (CDX) in nude mice and immunohistochemistry were used to assess anti-tumour effects of CTD in vivo. RESULTS: CTD suppressed the proliferation, anchorage-independent colony growth and epithelial-mesenchymal transformation (EMT) of CRC cells including HCT-15, HCT-116 and DLD1. Besides, the CTD also triggered cell apoptosis and cell cycle arrest at the G2/M phase. The CTD activates and induces p53 stability by inhibiting MDM2 ubiquitination via the suppression of AKT's phosphorylation in vitro. The CTD suppresses cell growth in a p53-independent fashion manner; p53 activation may contribute to the anticancer activity of CTD via target AKT. Finally, the CTD decreased the volume of CDX tumors without of the body weight loss and reduced the expression of AKT-MDM2-p53 signaling pathway in xenograft tumors. CONCLUSIONS: Our project has uncovered the mechanism underlying the biological activity of CTD in colon cancer and confirmed the AKT is a directly target of CTD. All of which These results revealed that CTD might be a new AKT inhibitor in colon cancer treatment, and CTD is worthy of further exploration in preclinical and clinical trials.1