Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer.

Pathological diagnosis of gastric cancer requires pathologists to have extensive clinical experience. To help pathologists improve diagnostic accuracy and efficiency, we collected 1,514 cases of stomach H&E-stained specimens with complete diagnostic information to establish a pathological auxiliary diagnosis system based on deep learning. At the slide level, our system achieves a specificity of 0.8878 while maintaining a high sensitivity close to 1.0 on 269 biopsy specimens (147 malignancies) and 163 surgical specimens (80 malignancies). The classified accuracy of our system is 0.9034 at the slide level for 352 biopsy specimens (201 malignancies) from 50 medical centers. With the help of our system, the pathologists' average false-negative rate and average false-positive rate on 100 biopsy specimens (50 malignancies) are reduced to 1/5 and 1/2 of the original rates, respectively. At the same time, the average uncertainty rate and the average diagnosis time are reduced by approximately 22% and 20%, respectively.

Effects of Digital Transformation on Environmental Governance of Mining Enterprises: Evidence from China.

Digitization offers fresh impetus to the transformation and upgrading of mining enterprises, while on the other hand, the rapid development and broad application of digital technologies make the environmental governance of mining enterprises the most important themes of theoretical research and practical exploration. In this paper, A-share companies listed between 2007 and 2020 are taken as samples to study the influence of digital transformation on the environmental governance of mining enterprises, and its relative acting paths. Our main research methods are multiple linear regression analysis, the panel fixed-effect model and the intermediary effect model. The results show that digital transformation significantly improves the environmental governance of mining enterprises, which is still tenable even after going through a series of endogeneity and robustness tests. It is found via the path test that, by strengthening the supervision of the media, the digital transformation performed in mining enterprises helps improve their environmental governance level, but the comparability of the accounting data shows no significant mediation effect between digital transformation and environmental governance. The heterogeneity test found that the promotion of digital transformation in environmental governance was significant only in non-state-owned enterprises, large-scale enterprises, and mature-growth enterprises. The findings enrich studies on the economic consequences and the environmental governance influences brought by mining enterprise's transformation based on advanced technologies. This provides an important reference and is of great heuristic significance in promoting digital transformation and strengthening the environmental governance of mining enterprises.

The Impacts of Fiscal Subsidies on the Carbon Emissions of Mining Enterprises: Evidence from China.

Lowering carbon output and reducing emissions have been worldwide concerns as global warming and environmental pollution worsen. Governments play a key role in driving corporate action related to carbon and emission reduction. In this paper, mining companies listed in China's A-share market were taken as samples to analyze the impacts of fiscal subsidies on the carbon emissions of mining enterprises through empirical tests. These findings demonstrated that fiscal subsidies could substantially lower corporate carbon emissions by incentivizing and enhancing their green-technology innovation. Financing constraints provided no prominent mediator effects between fiscal subsidies and carbon emissions, and these subsidies failed to considerably relieve their financing constraints in order to restrain carbon emissions. These results indicate that government policies on fiscal subsidies could represent significant guidance for corporate low-carbon and environmental-protection efforts, thereby providing empirical evidence for governmental environmental-protection policies.

Investigating the Relationship Between Entity Financialization, Managers' Incentives, and Enterprise's Innovation: Fresh Evidence From China.

The current study examines the relationship between financialization, managers' incentives, and the enterprise's innovation. Based on the principal-agent and incentive theories, this study proposes a research model with two management incentives as moderating variables between financialization and the enterprise's innovation. First, we analyze the direct relationship between financialization and the enterprise's innovation. Second, we examine the moderating effect of managers' equity incentive and compensation incentives on the relationship between entity financialization and the enterprise's innovation in high-tech/non-high-tech enterprises and state-owned and non-state-owned enterprises. This study covers the most recent updated data from both A-share listed companies in the Shenzhen and Shanghai stock exchange in China from 2009 to 2019. This study's finding indicates a significant negative impact of entity financialization and the enterprise's innovation. It means that the entity financial has a significant "crowding-out" effect on the enterprise's innovation. This study also confirms that management incentives cannot effectively suppress a "crowding-out" impact of entity financialization on firm innovation because of the principal-agent severe problem in financialization. Finally, considering the heterogeneities of property rights and degrees of dependence on the enterprise's innovation, a "crowding-out" effect of entity financialization on the enterprise's innovation is more significant in high-tech and state-owned enterprises. Managers' equity incentive significantly affects the enterprise's innovation in high-tech enterprises, while the managers' compensation incentive affects the enterprise's innovation in state-owned enterprises. Our study could help the enterprise to improve the company manager's incentive and provide the optimal assets allocation to improve the enterprise's innovation ability. Lastly, this study provides significant policies and recommendations for the public sector high-tech enterprise and private sector high-tech enterprises. Moreover, policies and recommendations are fruitful for the public sector non-high-tech enterprise and private sector non-high-tech enterprise.

INDEED: R package for network based differential expression analysis.

With recent advancement of omics technologies, fueled by decreased cost and increased number of available datasets, computational methods for differential expression analysis are sought to identify disease-associated biomolecules. Conventional differential expression analysis methods (e.g. student's t-test, ANOVA) focus on assessing mean and variance of biomolecules in each biological group. On the other hand, network-based approaches take into account the interactions between biomolecules in choosing differentially expressed ones. These interactions are typically evaluated by correlation methods that tend to generate over-complicated networks due to many seemingly indirect associations. In this paper, we introduce a new R/Bioconductor package INDEED that allows users to construct a sparse network based on partial correlation, and to identify biomolecules that have significant changes both at individual expression and pairwise interaction levels. We applied INDEED for analysis of two omic datasets acquired in a cancer biomarker discovery study to help rank disease-associated biomolecules. We believe biomolecules selected by INDEED lead to improved sensitivity and specificity in detecting disease status compared to those selected by conventional statistical methods. Also, INDEED's framework is amenable to further expansion to integrate networks from multi-omic studies, thereby allowing selection of reliable disease-associated biomolecules or disease biomarkers.