Ferritin level prospectively predicts hepatocarcinogenesis in patients with chronic hepatitis B virus infection

Previous studies have detected a higher level of ferritin in patients with hepatocellular carcinoma (HCC), but a potential causal association between serum ferritin level and hepatocarcinogenesis remains to be clarified. Using a well-established prospective cohort and longitudinally collected serial blood samples, the association between baseline ferritin levels and HCC risk were evaluated in 1,152 patients infected with hepatitis B virus (HBV), a major risk factor for HCC. The association was assessed by Cox proportional hazards regression model using univariate and multivariate analyses and longitudinal analysis. It was demonstrated that HBV patients who developed HCC had a significantly higher baseline ferritin level than those who remained cancer-free (188.00 vs. 108.00 ng/ml, P\u3c0.0001). The patients with a high ferritin level (≥200 ng/ml) had 2.43-fold increased risk of HCC compared to those with lower ferritin levels [hazard ratio (HR), 2.43; 95% confidence interval, 1.63-3.63]. A significant trend of increasing HRs along with elevated ferritin levels was observed (P for trend \u3c0.0001). The association was still significant after multivariate adjustment. Incorporating ferritin into the α-fetoprotein (AFP) model significantly improved the performance of HCC prediction (the area under the curve from 0.74 to 0.77, P=0.003). Longitudinal analysis showed that the average ferritin level in HBV patients who developed HCC was persistently higher than in those who were cancer-free during follow-up. HCC risk reached a peak at approximately the fifth year after baseline ferritin detection. Moreover, stratified analyses showed that the association was noted in both males and females, and was prominent in patients with a low AFP value. In short, serum ferritin level could independently predict the risk of HBV-related HCC and may have a complementary role in AFP-based HCC diagnosis. Future studies are warranted to validate these findings and test its clinical applicability in HCC prevention and management. © 2018, Spandidos Publication

Composite Sorting

We propose a new sorting framework: composite sorting. Composite sorting comprises of (1) distinct worker types assigned to the same occupation, and (2) a given worker type simultaneously being part of both positive and negative sorting. Composite sorting arises when fixed investments mitigate variable costs of mismatch. We completely characterize optimal sorting and additionally show it is more positive when mismatch costs are less concave. We then characterize equilibrium wages. Wages have a regional hierarchical structure - relative wages depend solely on sorting within skill groups. Quantitatively, composite sorting can generate a sizable portion of within-occupations wage dispersion in the US.Comment: 81 pages, 26 figure

Using Non-Additive Measure for Optimization-Based Nonlinear Classification

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2 – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are a relatively small number of training cases available (). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered

Optimal Power Flow in Hybrid AC and Multi-terminal HVDC Networks with Offshore Wind Farm Integration Based on Semidefinite Programming

Multi-terminal high voltage direct current (MTHVDC) technology is a promising technology for the offshore wind farm integration, which requires the new control and operation scheme. Therefore, the optimal power flow problem for this system is important to achieve the optimal economic operation. In this paper, convex relaxation model based on semidefinite programming for the MT-HVDC system considering DC/DC converters is proposed to solve the optimal power flow problem. A hybrid AC and MT-HVDC system for offshore wind farm integration is used for the test. The simulation results validate the effectiveness of the proposed model and guarantee that the global optimum solution is achieved.Comment: Accepted in IEEE/PES ISGT Asia 2019 conference (May, 2019), Chengdu, Chin

Self-organized global control of carbon emissions

There is much disagreement concerning how best to control global carbon emissions. We explore quantitatively how different control schemes affect the collective emission dynamics of a population of emitting entities. We uncover a complex trade-off which arises between average emissions (affecting the global climate), peak pollution levels (affecting citizens' everyday health), industrial efficiency (affecting the nation's economy), frequency of institutional intervention (affecting governmental costs), common information (affecting trading behavior) and market volatility (affecting financial stability). Our findings predict that a self-organized free-market approach at the level of a sector, state, country or continent, can provide better control than a top-down regulated scheme in terms of market volatility and monthly pollution peaks.Comment: 4 pages, 4 figure

Transcriptome analysis reveals the molecular basis of the response to acute hypoxic stress in blood clam Scapharca broughtonii

Hypoxia tolerance and adaptive regulation are important for aquatic animals, especially for species with poor mobility, such as most bivalves. Previous studies have confirmed that the blood clam Scapharca broughtonii has strong hypoxia resistance. However, the molecular mechanism supporting its hypoxic tolerance is still largely limited. To further screen the genes and their potential regulation of hypoxia tolerance, the transcriptome changes of S. broughtonii after acute hypoxic stress were explored by RNA sequencing. In this study, the average value of Q30 is 92.89%, indicating that the quality of sequencing is relatively high. The Unigenes obtained were annotated using four databases, namely Interpo, KEGG, Swisspro and TrEMBL. The annotation rates in these four databases were 71.82%, 75.95%, 92.98%, and 79.26%, respectively. And also, there were 649 DEGs in group B (dissolved oxygen (DO) of 2.5 mg/L) compared with group D (DO of 7.5 mg/L), among which 252 were up-regulated, and 397 were down-regulated. There were 965 DEGs in group A (DO of 0.5 mg/L), 2.5 mg/L, and 7.5 mg/L, compared with group B, among which 530 were up-regulated, and 435 were down-regulated. Meanwhile, there were 2,040 DEGs in group A compared with group D, among which 901 were up-regulated, and 1,139 were down-regulated. The main metabolic-related pathways of KEGG enriched in this study included Insulin secretion, Insulin signaling pathway, MAPK signal transduction pathway, and PPAR signaling pathway. These pathways may be critical metabolic pathways to solve energy demand and rebuild energy balance in S. broughtonii under hypoxic conditions. This study preliminarily clarified the response of S. broughtonii to hypoxia stress on the molecular levels, providing a reference for the following study on the response laws of related genes and pathways under environmental stress of S. broughtonii

ESG ratings and stock performance in the internet industry

Amidst the escalating emphasis on sustainable development, numerous corporations and organizations have intensified their environmental, social, and governance (ESG) efforts. The internet sector, intrinsically linked to the ESG domain, has consequently garnered amplified scrutiny. This study delves into the correlation between ESG ratings and the stock performance of publicly listed Chinese companies in the internet sector from 2016 to 2020. The findings reveal that initiatives in the ESG sphere significantly and negatively influence stock performance in these firms, assessed through raw stock returns, stock excess returns relative to the market index, Jensen’s one-factor alpha, and the Fama-French three-factor alpha. This inverse correlation between ESG ratings and stock performance is nonlinear and convex, indicating a lessening negative impact at elevated ESG levels. Moreover, this adverse effect is more pronounced in value stocks compared to growth stocks. Predominantly manifesting before 2018, this negative trend diminishes amidst the COVID-19 period. The reverse causality analysis employing lagged ESG ratings suggests that higher ESG ratings precipitate reduced stock performance, as opposed to vice versa. This study bridges a gap in the existing literature concerning ESG and stock performance specific to the Chinese internet industry and proposes recommendations for its sustainable evolution. AcknowledgmentThis research was supported by the Department of Education of Zhejiang Province General Program (Y202249981, Y202353438), the Wenzhou Association for Science and Technology – Service and Technology Innovation Program (jczc0254), the Wenzhou-Kean University Student Partnering with Faculty Research Program (WKUSPF2023004), the Wenzhou-Kean University International Collaborative Research Program (ICRP2023002, ICRP2023004), and the Wenzhou-Kean University Internal Research Support Program (IRSPG202205, IRSPG202206)