Understanding Economic Dynamics Behind Growth-Inequality Relationships

In this paper, a Dynamic General Equilibrium (DGE) model of growth-inequality relationships, with missing credit markets, knowledge spillover and self-employed agents, is calibrated to New Zealand data. The model explains how two distinct policy shocks involving redistribution and immigration imply, subsequently, two completely opposite outcomes. Agents' inability to borrow aggravates a negative macroeconomic effect of heterogeneity on growth. Redistribution mitigates that effect but creates microeconomic disincentives on saving and work-effort. Consequently, immigration shocks that perturb variance of efficiency induce a negative growth-inequality relationship, while redistribution shocks, in New Zealand's case, produce larger fluctuations in incentives than in macro benefits, implying a positive growth-inequality relationship.Heterogeneous agents, externality, income inequality, growth, progressive redistribution.

The Impact of Bequest Motives on Retirement Behavior in Japan: A Theoretical and Empirical Analysis

In this paper, we conduct a theoretical and empirical analysis of the impact of bequest motives on the work and retirement behavior of households in Japan using micro data from the Preference Parameters Study of Osaka University. Our empirical findings are consistent with our theoretical model and show that respondents with an altruistic or strategic/exchange bequest motive work more at the intensive margin than those without any bequest motive but that respondents with a strategic or exchange bequest motive work less at the extensive margin (i.e., retire earlier) than those without any bequest motive. Our findings for the strategic or exchange motive suggest that respondents with such a motive tend to work harder than others before they retire so that they can earn more, leave a larger bequest to their children, and elicit more care from them but that they tend to retire earlier than others so that they can start receiving care for themselves and their spouses from their children sooner. A policy implication of our findings is that the exchange of bequests for the care of parents by children may be very sensitive to the inheritance tax framework

Effect of Maize (Zea mays L.) Plant-Type on Yield and Photosynthetic Characters of Sweet Potato (Ipomoea balatas L.) in Intercropping System

Sweet potato/maize relay-cropping mode is considered as the main farming practices of dry land in Southwest China. Although relay-cropping would cause the reduction of fresh tuber yield, it still remained unclear that the reason was shade resulted from maize or genotype of sweet potato. The present work aims at exploring the effects of maize (Zea mays L.) plant-type on photosynthetic physiology and yield of sweet potato (Ipomoea balatas L.) in relay-cropping system. Besides, three plant-types maize cultivars including compact, semi-compact and expanded type were used for relay-cropping with different sweet potato cultivars (‘Yushu-2’, ‘Yushu-6’ and ‘Nanshu-88’) in field. The results showed that the photosynthetically active radiation (PAR) was declined with the increase of expansion of maize plant-type, which decreased by 77.5%, 80.1% and 82.1% respectively. When relay-cropped with extended maize, the yield reduction rate of sweet potato was the highest (67%). The shade-resistance of different genotype of sweet potatoes was different, and the yield reduction rate of ‘Yushu-2’ was the lowest (37.01%). Through conducting correlations analysis, it showed that fresh tuber yield had significant positive correlation with Effective Quantum Yield (Y(II)) and significant negative correlation with Non Photochemical Quenching Coefficient (NPQ). In terms of ‘Yushu-2’, the proportion of heat dissipation was the lowest, and its light quantum efficiency was higher than others. As a result, its reduction rate of yield was lower than the other two. We suggested that compact maize cultivar relay-cropping with strong shade-resistance sweet potato cultivar should be mainly applied in practice of sweet potato

Why Do Children Take Care of Their Elderly Parents? Are the Japanese Any Different?

In this paper, we conduct a theoretical analysis of why individuals provide are and attention to their elderly parents using a two-period overlapping generations model with endogenous saving and a “contest success function” and test this model using micro data from a Japanese household survey, the Osaka University Preference Parameter Study. To summarize our main findings, we find that the Japanese are more likely to live with (or near) their elderly parents and/or to provide care and attention to them if they expect to receive a bequest from them, which constitutes strong support for the selfish bequest motive or the exchange motive (much stronger than in the United States), but we find that their caregiving behavior is also heavily influenced by the strength of their altruism toward their parents and social norms

LncRNA BASP1-AS1 interacts with YBX1 to regulate Notch transcription and drives the malignancy of melanoma

Melanoma is a fatal skin malignant tumor with a poor prognosis. We found that long noncoding RNA BASP1-AS1 is essential for the development and prognosis of melanoma. The methylation, RNA sequencing, copy number variation, mutation data, and sample follow-up information of melanoma from The Cancer Genome Atlas (TCGA) were analyzed using weighted gene co-expression network analysis and 366 samples common to the three omics were selected for multigroup clustering analysis. A four-gene prognostic model (BASP1-AS1, LOC100506098, ARHGAP27P1, and LINC01532) was constructed in the TCGA cohort and validated using the GSE65904 series. The expression of BASP1-AS1 was upregulated in melanoma tissues and various melanoma cell lines. Functionally, the ectopic expression of BASP1-AS1 promoted cell proliferation, migration, and invasion in both A375 and SK-MEL-2 cells. Mechanically, BASP1-AS1 interacted with YBX1 and recruited it to the promoter of NOTCH3, initiating its transcription process. The activation of the Notch signaling then resulted in the transcription of multiple oncogenes, including c-MYC, PCNA, and CDK4, which contributed to melanoma progression. Thus, BASP1-AS1 could act as a potential biomarker for cutaneous malignant melanoma

Random forest can accurately predict the technique failure of peritoneal dialysis associated peritonitis patients

InstructionsPeritoneal dialysis associated peritonitis (PDAP) is a major cause of technique failure in peritoneal dialysis (PD) patients. The purpose of this study is to construct risk prediction models by multiple machine learning (ML) algorithms and select the best one to predict technique failure in PDAP patients accurately.MethodsThis retrospective cohort study included maintenance PD patients in our center from January 1, 2010 to December 31, 2021. The risk prediction models for technique failure were constructed based on five ML algorithms: random forest (RF), the least absolute shrinkage and selection operator (LASSO), decision tree, k nearest neighbor (KNN), and logistic regression (LR). The internal validation was conducted in the test cohort.ResultsFive hundred and eight episodes of peritonitis were included in this study. The technique failure accounted for 26.38%, and the mortality rate was 4.53%. There were resignificant statistical differences between technique failure group and technique survival group in multiple baseline characteristics. The RF prediction model is the best able to predict the technique failure in PDAP patients, with the accuracy of 93.70% and area under curve (AUC) of 0.916. The sensitivity and specificity of this model was 96.67 and 86.49%, respectively.ConclusionRF prediction model could accurately predict the technique failure of PDAP patients, which demonstrated excellent predictive performance and may assist in clinical decision making

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

PurposeTo develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).Material and methodsThe study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the “One-vs-Rest” strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.ResultsFollowing pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary “One-vs-Rest” strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively.ConclusionThe DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52–71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia