Despite significant interest in corporate culture, there is little empirical research on its role in influencing corporate misconduct. Using cultural background information on key company insiders, I ...construct a measure of corporate corruption culture, capturing a firm's general attitude toward opportunistic behavior. Firms with high corruption culture are more likely to engage in earnings management, accounting fraud, option backdating, and opportunistic insider trading. I further explore the inner workings of corruption culture and find evidence that it operates both as a selection mechanism and by having a direct influence on individual behavior.
Understanding the driving factors and assessing the risk of rainstorm waterlogging are crucial in the sustainable development of urban agglomerations. Few studies have focused on rainstorm ...waterlogging at the scale of urban agglomeration areas. We used the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case study. Kernel density estimation (KDE) and spatial autocorrelation analysis were applied to study the spatial distribution characteristics of rainstorm waterlogging spots during 2013–2017. A geographical detector (GD) and geographically weighted regression (GWR) were used to discuss the driving mechanism of rainstorm waterlogging by considering eight driving factors: impervious surface ratio (ISR), mean shape index of impervious surface (Shape_MN), aggregation index of impervious surface (AI), fractional vegetation cover (FVC), elevation, slope, river density, and river distance. The risk of rainstorm waterlogging was assessed using GWR based on principal component analysis (PCA). The results show that the spatial distribution of rainstorm waterlogging in the GBA has the characteristics of multicenter clustering. Land cover characteristic factors are the most important factors influencing rainstorm waterlogging in the GBA and most of the cities within the GBA. The rainstorm waterlogging density increases when ISR, Shape_MN, and AI increase, while it decreases when FVC, elevation, slope, and river distance increase. There is no obvious change rule between rainstorm waterlogging and river density. All of the driving factors enhance the impacts on rainstorm waterlogging through their interactions. The relationships between rainstorm waterlogging and the driving factors have obvious spatial differences because of the differences in the dominant factors affecting rainstorm waterlogging in different spatial positions. Furthermore, the result of the risk assessment of rainstorm waterlogging indicates that the southwest area of Guangzhou and the central area of Shenzhen have the highest risks of rainstorm waterlogging in GBA. These results may provide references for rainstorm waterlogging mitigation through urban renewal planning in urban agglomeration areas.
The characteristics of a wide variety of scales about objects and complex texture features of high-resolution remote sensing images make deep learning-based change detection methods the mainstream ...method. However, existing deep learning methods have problems with spatial information loss and insufficient feature representation, resulting in unsatisfactory effects of small objects detection and boundary positioning in high-resolution remote sensing images change detection. To address the problems, a network architecture based on 2-dimensional discrete wavelet transform and adaptive feature weighted fusion is proposed. The proposed network takes Siamese network and Nested U-Net as the backbone; 2-dimensional discrete wavelet transform is used to replace the pooling layer; and the inverse transform is used to replace the upsampling to realize image reconstruction, reduce the loss of spatial information, and fully retain the original image information. In this way, the proposed network can accurately detect changed objects of different scales and reconstruct change maps with clear boundaries. Furthermore, different feature fusion methods of different stages are proposed to fully integrate multi-scale and multi-level features and improve the comprehensive representation ability of features, so as to achieve a more refined change detection effect while reducing pseudo-changes. To verify the effectiveness and advancement of the proposed method, it is compared with seven state-of-the-art methods on two datasets of Lebedev and SenseTime from the three aspects of quantitative analysis, qualitative analysis, and efficiency analysis, and the effectiveness of proposed modules is validated by an ablation study. The results of quantitative analysis and efficiency analysis show that, under the premise of taking into account the operation efficiency, our method can improve the recall while ensuring the detection precision, and realize the improvement of the overall detection performance. Specifically, it shows an average improvement of 37.9% and 12.35% on recall, and 34.76% and 11.88% on F1 with the Lebedev and SenseTime datasets, respectively, compared to other methods. The qualitative analysis shows that our method has better performance on small objects detection and boundary positioning than other methods, and a more refined change map can be obtained.
One of the major challenges remaining in the classification of thyroid tumor is the determination of whether a nodule is benign or malignant. We aimed to characterize the mutational profiles of ...follicular thyroid tumor and to identify markers with potential diagnostic and prognostic implications.
Targeted sequencing with a panel of 18 thyroid cancer-related genes was performed on 48 tissue samples from follicular thyroid adenoma (FTA), 32 follicular tumors of uncertain malignant potential (FT-UMP), 17 well-differentiated tumors of uncertain malignant potential (WDT-UMP) and 53 samples from follicular thyroid carcinoma (FTC). The correlation of mutation profiles and clinicopathological features and prognosis were also analyzed.
We identified 95 nonsilent mutations spanning 14 genes. Specifically, TERT promoter (TERTp) mutations were exclusively detected in FTC. A total of 80% EIF1AX exon 2 mutations (4/5) and 75% TSHR mutations (3/4) occurred in FTA, whereas the rest of them occurred in FT-UMP. KRAS mutations and TP53 mutations were only presented in borderline or malignant tumors. H/N-RAS mutations were detected in all four subtypes, but were most commonly found in WDT-UMP (p = 0.031). All N-RAS mutations were located at codon 61. BRAF V600E and RET fusion were absent in the entire cohort. In FTC cases, EIF1AX mutations were all located at intron 5/exon 6 and correlated with advanced disease (p = 0.032). Both EIF1AX and TERTp mutations predicted shorter disease-free survival (p = 0.007, p = 0.024, respectively). Further analysis revealed that TERTp mutations were correlated with shorter disease-free survival in patients with minimally invasive /encapsulated angioinvasive FTC (p = 0.017), but not in those with widely invasive FTC (p = 0.297).
TERTp, EIF1AX, TSHR, H/N/K-RAS and TP53 mutations may have diagnostic or prognostic potential in follicular thyroid tumors. TERTp mutations may predict a poor outcome in patients with minimally invasive/encapsulated angioinvasive FTC.
We hypothesize that corporate income taxes distort firms’ incentives to innovate by reducing their pledgeable income. Using a differences-in-differences methodology, we document that large corporate ...income tax cuts boost corporate innovation. We find a similar but opposite effect for tax increases. Most of the change in innovation occurs 2 or more years after the tax change, and there’s no effect before the tax change. Exploring the mechanisms, we show that tax cuts have a stronger impact on innovation for firms with weaker governance, greater financial constraints, fewer tangible assets, smaller patent stock, and a greater degree of tax avoidance.
Pancreatic ductal adenocarcinoma (PDAC) has the lowest overall survival rate primarily due to the late onset of symptoms and rapid progression. Reliable and accurate tests for early detection are ...lacking. We aimed to develop a noninvasive test for early PDAC detection by capturing the circulating tumour DNA (ctDNA) methylation signature in blood.
Genome-wide methylation profiles were generated from PDAC and nonmalignant tissues and plasma. Methylation haplotype blocks (MHBs) were examined to discover de novo PDAC markers. They were combined with multiple cancer markers and screened for PDAC classification accuracy. The most accurate markers were used to develop PDACatch, a targeted methylation sequencing assay. PDACatch was applied to additional PDAC and healthy plasma cohorts to train, validate and independently test a PDAC-discriminating classifier. Finally, the classifier was compared and integrated with carbohydrate antigen 19-9 (CA19-9) to evaluate and maximize its accuracy and utility.
In total, 90 tissues and 546 plasma samples were collected from 232 PDAC patients, 25 chronic pancreatitis (CP) patients and 323 healthy controls. Among 223 PDAC cases with known stage information, 43/119/38/23 cases were of Stage I/II/III/IV. A total of 171 de novo PDAC-specific markers and 595 multicancer markers were screened for PDAC classification accuracy. The top 185 markers were included in PDACatch, from which a 56-marker classifier for PDAC plasma was trained, validated and independently tested. It achieved an area under the curve (AUC) of 0.91 in both the validation (31 PDAC, 26 healthy; sensitivity = 84%, specificity = 89%) and independent tests (74 PDAC, 65 healthy; sensitivity = 82%, specificity = 88%). Importantly, the PDACatch classifier detected CA19-9-negative PDAC plasma at sensitivities of 75 and 100% during the validation and independent tests, respectively. It was more sensitive than CA19-9 in detecting Stage I (sensitivity = 80 and 68%, respectively) and early-stage (Stage I-IIa) PDAC (sensitivity = 76 and 70%, respectively). A combinatorial classifier integrating PDACatch and CA19-9 outperformed (AUC=0.94) either PDACatch (0.91) or CA19-9 (0.89) alone (p < 0.001).
The PDACatch assay demonstrated high sensitivity for early PDAC plasma, providing potential utility for noninvasive detection of early PDAC and indicating the effectiveness of methylation haplotype analyses in discovering robust cancer markers.
The classification of urban functional areas plays an important role in urban planning and resource management. Although previous studies have confirmed that different urban functional areas have ...different morphological structures and Land Surface Temperature (LST) characteristics, these two types of characteristics have rarely been fully integrated and used for functional area classification. In this paper, a new framework for classifying urban functional areas is proposed by combining urban morphological features and LST features. First, metrics are constructed from three levels, namely, building, road and region, which are used to portray urban morphology; LST is retrieved using thermal infrared remote sensing to reflect LST features with four metrics: the average temperature, maximum temperature, temperature difference and standard deviation of temperature. Then, the functional areas are classified into four categories: service/public land, commercial land, residential land and industrial land. A random forest algorithm is used to effectively fuse the features of these two categories and classify the functional areas. The effectiveness of the proposed framework is tested in the study area of Shenzhen City, Guangdong Province. The results show that the combined classification accuracy of the proposed classification method is 0.85, which is 0.26 higher than that of the classification model based on urban morphology and 0.1 higher than that of the classification model based on LST features. The proposed framework verifies that the integration of LST features into urban functional area classification is reliable and effectively combines urban morphology and LST features for functional area classification.
Coastal reclamation in Guangdong Province is highly concentrated and is growing rapidly. However, intensive reclamation use has resulted in serious influence on the coastal ecosystem, directly and ...indirectly. The current conditions and spatial distribution of reclamations must be detected for coastal preservation and management using efficient technology. This study aims to find a suitable method and data to map reclamations accurately at a large scale. Pixel-based and object-oriented classification methods were applied in extracting the three typical types of coastal reclamation, namely, ports, aquaculture ponds, and salt pans, in Guangdong Province from Landsat 8 and Sentinel 2 images. The algorithms of a support vector machine, random forest, decision tree, and rule-based algorithm were performed. Classification results were compared with statistical measures to assess the performance of different algorithms. The results indicated that all of the algorithms could obtain classification results with high accuracy, whereas the object-oriented algorithm showed less efficiency than other algorithms in classifying ports with complicated features. High-resolution data were not always superior to lower-resolution data in the reclamation classification. Generally speaking, applying the rule-based object-oriented algorithm in Sentinel 2A MSI images is relatively efficient at detecting the reclamation use in coastal Guangdong considering its actual situation. The mapping of reclamations in the whole of coastal Guangdong shows that they present obvious agglomeration characteristics in the space. The aquaculture ponds are mainly distributed in the coastal zones of western Guangdong and eastern Guangdong, with the largest area of 77,963 ha. The other types of ports are mainly distributed in the coastal zones of the Pearl River Delta, with an area of 8146 ha, while salt pans are mainly distributed in the coastal zones of Jiangmen, Zhuhai, and Zhongshan, with a total area of 4072 ha. The results can provide key supporting data for decision making in coastal management and preservation.
Background
Pancreatic ductal adenocarcinoma (PDAC) remains treatment refractory. Immunotherapy has achieved success in the treatment of multiple malignancies. However, the efficacy of immunotherapy ...in PDAC is limited by a lack of promising biomarkers. In this research, we aimed to identify robust immune molecular subtypes of PDAC to facilitate prognosis prediction and patient selection for immunotherapy.
Methods
A training cohort of 149 PDAC samples from The Cancer Genome Atlas (TCGA) with mRNA expression data was analyzed. By means of non-negative matrix factorization (NMF), we virtually dissected the immune-related signals from bulk gene expression data. Detailed immunogenomic and survival analyses of the immune molecular subtypes were conducted to determine their biological and clinical relevance. Validation was performed in five independent datasets on a total of 615 samples.
Results
Approximately 31% of PDAC samples (46/149) had higher immune cell infiltration, more active immune cytolytic activity, higher activation of the interferon pathway, a higher tumor mutational burden (TMB), and fewer copy number alterations (CNAs) than the other samples (all P < 0.001). This new molecular subtype was named Immune Class, which served as an independent favorable prognostic factor for overall survival (hazard ratio, 0.56; 95% confidence interval, 0.33-0.97). Immune Class in cooperation with previously reported tumor and stroma classifications had a cumulative effect on PDAC prognostic stratification. Moreover, programmed cell death-1 (PD-1) inhibitors showed potential efficacy for Immune Class (P = 0.04). The robustness of our immune molecular subtypes was further verified in the validation cohort.
Conclusions
By capturing immune-related signals in the PDAC tumor microenvironment, we reveal a novel molecular subtype, Immune Class. Immune Class serves as an independent favorable prognostic factor for overall survival in PDAC patients.
Although RNA m
6
A regulators have been implicated in the tumorigenesis of several different types of tumors, including pancreatic cancer, their clinical relevance and intrinsic regulatory mechanism ...remain elusive. This study analyzed eight m
6
A regulators (METTL3, METTL14, WTAP, FTO, ALKBH5, and YTHDF1-3) in pancreatic ductal adenocarcinoma (PDAC) and found that only RNA m
6
A demethylase ALKBH5 serves as an independent favorable prognostic marker for this tumor. To better understand the molecular mechanism underlying the protective effect conferred by ALKBH5 against pancreatic tumorigenesis, we performed a transcriptome-wide analysis of m
6
A methylation, gene expression, and alternative splicing (AS) using the MIA PaCa-2 stable cell line with ALKBH5 overexpression. We demonstrated that ALKBH5 overexpression induced a reduction in RNA m
6
A levels globally. Furthermore, mRNAs encoding ubiquitin ligase FBXL5, and mitochondrial iron importers SLC25A28 and SLC25A37, were identified as substrates of ALKBH5. Mechanistically, the RNA stabilities of
FBXL5
and
SLC25A28
, and the AS of
SLC25A37
were affected, which led to their upregulation in pancreatic cancer cell line. Particularly, we observed that downregulation of FBXL5 in tumor samples correlated with shorter survival time of patients. Owing to FBXL5-mediated degradation, ALKBH5 overexpression incurred a significant reduction in iron-regulatory protein IRP2 and the modulator of epithelial-mesenchymal transition (EMT) SNAI1. Notably, ALKBH5 overexpression led to a significant reduction in intracellular iron levels as well as cell migratory and invasive abilities, which could be rescued by knocking down
FBXL5
. Overall, our results reveal a previously uncharacterized mechanism of ALKBH5 in protecting against PDAC through modulating regulators of iron metabolism and underscore the multifaceted role of m
6
A in pancreatic cancer.
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