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The N7-methylguanosine (m7G), a modification at defined internal positions within tRNAs and rRNAs, is correlated with tumor progression. Methyltransferase like 1 (METTL1)/ WD repeat domain 4 (WDR4) mediated tRNA m7G modification, which could alter many oncogenic mRNAs translation to promote progress of multiple cancer types. However, whether and how the internal mRNA m7G modification is involved in tumorigenesis remains unclear. The immunohistochemistry assay was conducted to detect the expression of WDR4 and METTL1 in hepatocellular carcinoma (HCC) and the expression of both genes whether contributes to the prognosis of the survival rate of HCC patients. Then, CCK8, colony formation assays and tumor xenograft models were conducted to determine the effects of WDR4 on HCC cells in vitro and vivo. Besides, dot blot assay, m7G-MeRIP-seq and RNA-seq analysis were conducted to determine whether WDR4 contributes to m7G modification and underlying mechanism in HCC cells. Finally, rescue and CO-IP assay were conducted to explore whether WDR4 and METTL1 proteins form a complex in Huh7 cells. WDR4 modulates m7G modification at the internal sites of tumor-promoting mRNAs by forming the WDR4-METTL1 complex. WDR4 knockdown downregulated the expression of mRNA and protein levels of METTL1 gene and thus further modulate the formation of WDR4-METTL1 complex indirectly. METTL1 expression was markedly correlated with WDR4 expression in HCC tissues. HCC patients with high expression of both genes had a poor prognosis. WDR4 may contribute to HCC pathogenesis by interacting with and regulating the expression of METTL1 to synergistically modulate the m7G modification of target mRNAs in tumor cells. [ABSTRACT FROM AUTHOR]

Abstract In the context of cap-and-trade regulation, this paper develops the models of evolutionary game between governments and manufacturers, and analyzes the impacts of government policy on the decisions of manufacturers and the dynamic tendency of cap-and-trade market. It investigates the impacts of government policies on carbon trading market under two scenarios: the static carbon trading price and the dynamic carbon trading price. Under dynamic carbon trading price, the equilibrium strategy of the evolutionary game can be obtained. Then a numerical example is examined with system dynamics simulation. It is shown that, when governments implement the static carbon trading price, the evolutionary game cannot reach the stability. Under the dynamic carbon trading price, the evolutionary game has the evolutionary stable strategy. Dynamic carbon trading pricing policy is effective in accelerating the carbon reduction. Moreover, the probability of manufacturers introducing green technology is negatively correlated with governmental intervention costs and positively correlated with governmental penalty for manufacturers' speculations. [ABSTRACT FROM AUTHOR]

Under cap-and-trade regulation, this paper investigates the decision and coordination in the dual-channel supply chain arising out of low-carbon preference and channel substitution. From the game theoretical perspective, we develop the decision-making models of the centralized and decentralized supply chain, which consist of one manufacturer and one retailer. We design an improved revenue-sharing contract to effectively coordinate the manufacturer and retailer. The results suggestthe government make cap-and-trade regulation to reduce carbon emission efficiently andrealize coordinated development betweenthe economy and environment. In addition, the supply chaincan obtain greater profits based on the consumer's low-carbon preference. Meanwhile, the improved revenue-sharing contract leads to a Paretoimprovement of the efficiency between the manufacturer and retailer, as well as the optimal profits are related to bargaining powers. [ABSTRACT FROM AUTHOR]

• Adversarial mechanism is proposed to precisely regress action proposal boundaries aiming to enhance their exclusivity and independence. • Complementary learning evaluates the information gap between the original video and all of the action proposals, which guarantees the integrity of video action information, and ensures no candidate clip is casually ignored and dismissed. • The aggregation algorithm of relevant action proposals is designed to merge temporally adjacent action snippets into a longer segment, which can avoid video over fragmentation. Weakly supervised Temporal Action Localization (WTAL) aims to locate the start and end boundaries of action instances and recognize their corresponding categories. Classical methods mainly rely on random erasure mechanisms, attention mechanisms, or imposing loss constraints. Despite their great progress, there are still two challenges of incomplete positioning and context confusion. Therefore, we propose a framework with complementary adversarial mechanisms to address these issues. In the adversarial learning stage, for an input snippet, we roughly determine its proper duration, by matching it with the specified multi-scaled anchors based on CAS score loss; then, it undergoes a frame-level iterative regression to precisely figure out its boundary, which can reject none closely related frames merged in, and ensures no overlapping between different action proposals. Subsequently, the GCN module explicitly enhances the feature representation for this fine localized snippet, aiming to strengthen the exclusiveness between different action snippets. Afterward, our complementary learning module calculates the similarity between the original input video V g and the video V r reconstructed with the above localization refined snippets, aiming to ensure no closely relevant frames missing, this checking mechanism works as feedback to guide the regression module for more accurate localization regression. Finally, each refined snippet undergoes multi-instance learning to obtain its classification score, and the top-k strategy is used to aggregate temporally adjacent snippets based on their content similarity, which can avoid fragmentation of an action proposal. This method is tested on datasets of THUMOS14 and ActivityNet1.2, and their average accuracy is 64.68% and 32.94% respectively, its comparisons with other articles prove its effectiveness. [ABSTRACT FROM AUTHOR]

• Propose a pooling and global feature fusion self-attention mechanism network. • Propose a spatial self-attention pooling fusion module. • Propose a channel self-attention pooling fusion module. • Propose a spatial and channel global self-attention fusion module. • Capture multi-level correlations among a variety of features. Object detection has been a challenging task due to the complexity and diversity of objects. The emergence of self-attention mechanism provides a new clue for feature fusion in object detection task. Most existing self-attention mechanisms focus on extracting the correlation between global and local information in space or among channels, however it remains problematic issues of how to effectively fuse all those features. To address the above problems, we propose a Pooling and Global feature Fusion Self-attention Mechanism (PGFSM) to capture multi-level correlations among a variety of features, so as to perform cascaded aggregations upon them. PGFSM consists of three parts: Spatial Self-attention Pooling Fusion Module (SSPFM), Channel Self-attention Pooling Fusion Module (CSPFM), and Spatial and Channel Global Self-attention Fusion Module (SCGSFM). SSPFM and CSPFM respectively carried out in space and channel, extract the global maximum pooling and global average pooling self-attention features; SCGSFM extracts the spatial and channel fused characteristic relationship in the global. Finally, the three fused feature relations are added on the original feature to achieve an enhanced trait representation. In test, our PGFSM is embedded into YOLOv4, YOLOv5, and EfficientDet network respectively, and evaluated in PASCAL VOC and MS COCO datasets. The experiment results show that the feature fusion self-attention mechanism improves the performance of object detection compared to each original framework and also the state-of-the-art modules, which proves the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Background Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) severely restricts the long-term survival of patients. Toll-like receptor 4 (TLR4) has been considered to be involved in hepatocarcinogenesis and metastasis. Additionally, there is a study demonstrating the significant association between TLR4 gene rs1927914 polymorphism and HCC, but no study investigated the association of the TLR4 rs1927914 polymorphism with the risk of HCC recurrence following LT. Aim The purpose of this study was to assess the potential association between the TLR4 gene rs1927914 polymorphism of donors and recipients and hepatocellular carcinoma recurrence after LT. Methods Eighty-three patients with HCC undergoing LT from July 2006–June 2015 were identified for this analysis. We genotyped a single-nucleotide polymorphism (rs1927914) in both donors and recipients and evaluated the association between the polymorphism and risk of tumor recurrence. Results The donor TLR4 rs1927914 polymorphism was found to be significantly associated with HCC recurrence following LT. In multivariate logistic regression analysis, Milan criteria, microvascular invasion and donor TLR4 rs1927914 genotype were confirmed to be independent risk factors for HCC recurrence. Kaplan-Meier survival curves showed that patients carrying donors homozygous TT had a significantly lower recurrence-free survival and overall survival than CC/CT patients. Cox proportional hazards modeling indicated that TNM stage or Milan criteria, microvascular invasion, and donor TLR4 rs1927914 genotype were independent factors for the clinical outcomes of LT patients. Conclusions Donor TLR4 rs1927914 polymorphism is associated with an increased risk of HCC recurrence following LT and has a potential clinical value for the prediction of HCC recurrence after LT. [ABSTRACT FROM AUTHOR]

The reduction of carbon emissions is a world-wide concern. Many countries have implemented various policies to reduce carbon emissions, which has affected the operational decisions of enterprises in the supply chain since most carbon is emitted during the production process. In this paper, the mutual relationship between supply chain enterprise operations and government policy are considered. Governmental carbon emission tax policies are examined using the three-stage Stackelberg game model with a decentralized supply chain and the two-stage Stackelberg game model with a centralized supply chain. The optimal strategies of enterprises and governments are theorized for both types of supply chains. In the decentralized supply chain, a product's retail price and the government's carbon tax are higher than those in centralized decision-making mode. In the centralized supply chain, the stocking factor of the retailer, the profit of the supply chain and the expected social welfare of the government are higher than those in decentralized supply chain. With the increase of carbon emissions tax, the wholesale price of manufacturer and retailing price of retailer under the decentralized supply chain, as well as retailing price of retailer under centralized supply chain increase. The results indicate that the centralized supply chain is more beneficial to enterprises and government than the decentralized supply chain. By performing the comparisons of the manufacturer-dominated supply chain with retailer-dominated and no agent dominated supply chains, it is shown that both the environment performance and financial performance of supply chain are better off when the supply chain members have distributed pricing powers. [ABSTRACT FROM AUTHOR]

For encouraging the manufacturer and supplier to innovate in carbon reduction technology, the government tends to set a number of mandatory standards and some punitive measures in practice. However, research on the regulatory ability of the government and the relationship between governmental regulation and enterprises' carbon reduction technology innovation is often neglected. Thus, the bilateral interaction mechanism between the government and enterprises should be established in which enterprises and governments constantly adjust individual decisions to response to the other, so as to improve the carbon emission reduction of enterprises and the level of government regulation. We use differential game methods to study the interaction between carbon reduction technology innovation and government intervention under decentralized decision without cost sharing, decentralized decision with cost sharing and centralized decision respectively. We find that the optimal level of carbon reduction technology innovation under decentralized decision is the same as centralized situation when there is no cost sharing. Furthermore, the optimal levels of government intervention are also the same in both cases. However, the optimal level of carbon reduction technology innovation and government intervention will be improved only in the case that cost-sharing proportion reaches a certain range. Therefore, a positive interaction mechanism between carbon reduction technology innovation of enterprise and intervention of government can be established. Theoretically, our paper reveals a new perspective in the research of the relationship between government and enterprises in the process of carbon reduction technology innovation and use differential game methods to study the interaction between carbon reduction technology innovation and government intervention. In practice, this paper can help government and enterprises to form a closer cooperative relationship in reducing carbon emissions. The government should give full consideration for enterprises' actual production capacity when taking intervention measures. Meanwhile, enterprises should refer to the intervention indicators of the government when conducting carbon reduction technology innovations. [ABSTRACT FROM AUTHOR]

As ocean container transport becoming a critical element across the global markets, container terminals start to impose investment in terminal efficiency to enhance their competitiveness and capture greater market share. To analyze this strategy, we formulate a traditional game model in a two-terminals-one-port system, and derive the optimal investment level and pricing strategy of each participate while considering the customer efficiency preference and terminal's capital restrictions. Numerical simulation is applied in the terminals of Shanghai Yangshan Deep Water Port to explore the investment and pricing strategy. We find efficiency investment of a terminal is related to the market high efficiency preference. Terminal with higher basic efficiency is more inclined to invest to improve the efficiency level, while the rival terminal will set lower terminal charge to appeal the customers to avoid loss of customers and competitiveness. The study provides the feasibility for terminals' future development. [Display omitted] • We analyze in detail strategic decision about whether to invest and how much to invest in improving terminal efficiency. • The models considering consumer preference and capital constraint have been presented and analyzed strategically. • Relative to the capital constraint, terminal investment are more responsive to the consumer efficiency preference. [ABSTRACT FROM AUTHOR]