Correction : Control of PD-L1 expression by miR-140/142/340/383 and oncogenic activation of the OCT4-miR-18a pathway in cervical cancer.

This research was supported by a grant from the Department of Women’s Health Educational System, JSPS Grant-in-Aid for Scientific Research (C) (15K10697 and 16K11123) and the Science and Technology Planning Project of Guangdong Province, China (2014A020212124). We thank Dr. Zhujie Xu for experimental assistance. The authors declare that they have no conflict of interest.Peer reviewedPublisher PD

Locally advanced rectal cancer with dMMR/MSI-H may be excused from surgery after neoadjuvant anti-PD-1 monotherapy: a multiple-center, cohort study

ObjectiveExamine patients with locally advanced rectal cancer (LARC) with deficient mismatch repair (dMMR) or microsatellite instability-high (MSI-H) who received neoadjuvant immunotherapy (nIT), and compare the outcomes of those who chose a watch-and-wait (WW) approach after achieving clinical complete response (cCR) or near-cCR with those who underwent surgery and were confirmed as pathological complete response (pCR).MethodsLARC patients with dMMR/MSI-H who received nIT were retrospectively examined. The endpoints were 2-year overall survival (OS), 2-year disease-free survival (DFS), local recurrence (LR), and distant metastasis (DM). The efficacy of programmed cell death protein-1 (PD-1) inhibitor, immune-related adverse events (irAEs), surgery-related adverse events (srAEs), and enterostomy were also recorded.ResultsTwenty patients who received a PD-1 inhibitor as initial nIT were examined. Eighteen patients (90%) achieved complete response (CR) after a median of 7 nIT cycles, including 11 with pCR after surgery (pCR group), and 7 chose a WW strategy after evaluation as cCR or near-cCR (WW group). Both groups had median follow-up times of 25.0 months. Neither group had a case of LR or DM, and the 2-year DFS and OS in each group was 100%. The two groups had similar incidences of irAEs (P=0.627). In the pCR group, however, 2 patients (18.2%) had permanent colostomy, 3 (27.3%) had temporary ileostomy, and 2 (18.2%) had srAEs.ConclusionNeoadjuvant PD-1 blockade had high efficacy and led to a high rate of CR in LARC patients with dMMR/MSI-H. A WW strategy appears to be a safe and reliable option for these patients who achieve cCR or near-cCR after nIT

Impacts of Parents’ Divorce on Chinese Children: A Model with Academic Performance as a Mediator

The study examined the impact of parents’ divorce on Chinese children’s well-being. A Chinese theoretical model was tested using Structural Equation Modeling. The sample consisted of 940 Chinese children aged 6-16. The well-being of children from divorced families was compared with that of two-parent and widowed families. The results showed that children’s academic performance mediated the negative impact of divorce on children’s well-being. The societal discriminating attitude towards divorce and single-parent families had a strong negative effect on the children’s well-being. Parenting skills of the custodial parent had more influence on the children’s well-being than the marital conflicts prior to the divorce. Supports from the extended families counterbalanced some negative effects associated with divorce

Automatic extraction of built-up areas in Chinese urban agglomerations based on the deep learning method using NTL data

Rapidly and accurately extracting built-up areas is an essential prerequisite of urbanization research. There have been many studies on the extraction of built-up areas using remote sensing technologies. So far, few studies have been conducted to evaluate the applicability of the deep learning method to extract built-up areas under the condition that only nighttime light (NTL) data are used. This study proposed a deep learning method to extract the built-up areas using NTL data, and applied the method to analyze the spatial and temporal changes of the built-up areas in Chinese two urban agglomerations from 2000 to 2020. The results show that the U-Net deep learning method can be used to extract built-up areas efficiently under the condition that only NTL data are used. The proposed method was able to improve the accuracy of built-up area extraction significantly compared to the existing method. For the extraction of built-up areas in large regions with long time series, the proposed method can facilitate the work and improve the processing efficiency. The gravity centre of the built-up areas in the Central Plains Urban Agglomeration migrated south-eastward, and the gravity centre of the built-up areas in the Shandong Peninsula Urban Agglomeration migrated south-westward, with these gravity centres gradually approaching the geometric centres of the corresponding urban agglomerations. The built-up areas in the Central Plains and Shandong Peninsula Urban Agglomerations grew rapidly, increasing by 4.14 times and 3.73 times from 2000 to 2020, respectively. The built-up areas in the Central Plains Urban Agglomeration expanded faster, while the urban development degree of the Shandong Peninsula Urban Agglomeration was higher. The urban distributions and development modes of these two urban agglomerations were quite different. The Central Plains Urban Agglomeration tended to further agglomerate, while the Shandong Peninsula Urban Agglomeration tended to disperse

Knowledge-Enhanced Dual-Channel GCN for Aspect-Based Sentiment Analysis

As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics and the syntax of the sentence. These methods are challenged by two issues. For one thing, the semantic-based graph convolution networks fail to capture the relation between aspect and its opinion word. For another, minor attention is assigned to the aspect word within graph convolution, resulting in the introduction of contextual noise. In this work, we propose a knowledge-enhanced dual-channel graph convolutional network. On the task of ABSA, a semantic-based graph convolutional netwok (GCN) and a syntactic-based GCN are established. With respect to semantic learning, the sentence semantics are enhanced by using commonsense knowledge. The multi-head attention mechanism is taken to construct the semantic graph and filter the noise, which facilitates the information aggregation of the aspect and the opinion words. For syntactic information processing, the syntax dependency tree is pruned to remove the irrelevant words, based on which more attention weights are given to the aspect words. Experiments are carried out on four benchmark datasets to evaluate the working performance of the proposed model. Our model significantly outperforms the baseline models and verifies its effectiveness in ABSA tasks

An Urban Land Cover Classification Method Based on Segments’ Multidimension Feature Fusion

Using object-based deep learning for the urban land cover classification has become a mainstream method. This study proposed an urban land cover classification method based on segments’ object features, deep features, and spatial association features. The proposed method used the synthetic semivariance function to determine the hyperparameters of the superpixel segmentation and subsequently optimized the image superpixel segmentation result. A convolutional neural network and a graph convolutional neural network were used to obtain segments’ deep features and spatial association features, respectively. The random forest algorithm was used to classify segments based on the multidimension features. The results showed that the image superpixel segmentation results had the significant impact on the classification results. Compared with the pixel-based method, the segment-based methods generally yielded the higher classification accuracy. The strategy of multidimension feature fusion can combine the advantages of each single-dimension feature to improve the classification accuracy. The proposed method utilizing multidimension features was more efficient than traditional methods used for the urban land cover classification. The fusion of segments’ object features, deep features, and spatial association features was the best solution for achieving the urban land cover classification

Knowledge-Enhanced Dual-Channel GCN for Aspect-Based Sentiment Analysis

As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics and the syntax of the sentence. These methods are challenged by two issues. For one thing, the semantic-based graph convolution networks fail to capture the relation between aspect and its opinion word. For another, minor attention is assigned to the aspect word within graph convolution, resulting in the introduction of contextual noise. In this work, we propose a knowledge-enhanced dual-channel graph convolutional network. On the task of ABSA, a semantic-based graph convolutional netwok (GCN) and a syntactic-based GCN are established. With respect to semantic learning, the sentence semantics are enhanced by using commonsense knowledge. The multi-head attention mechanism is taken to construct the semantic graph and filter the noise, which facilitates the information aggregation of the aspect and the opinion words. For syntactic information processing, the syntax dependency tree is pruned to remove the irrelevant words, based on which more attention weights are given to the aspect words. Experiments are carried out on four benchmark datasets to evaluate the working performance of the proposed model. Our model significantly outperforms the baseline models and verifies its effectiveness in ABSA tasks

Control of PD-L1 expression by miR-140/142/340/383 and oncogenic activation of the OCT4–miR-18a pathway in cervical cancer

PD-L1, a key inhibitory immune receptor, has crucial functions in cancer immune evasion, but whether PD-L1 promotes the malignant properties of cervical cancer (CC) cells and the mechanism by which PD-L1 is regulated in CC remains unclear. We report that PD-L1 is overexpressed in CC, and shRNA-mediated PD-L1 depletion suppresses the proliferation, invasion, and tumorigenesis of CC cells. Loss of miR-140/142/340/383 contributes to PD-L1 upregulation. miR-18a enhances PD-L1 levels by targeting PTEN, WNK2 (ERK1/2 pathway inhibitor), and SOX6 (Wnt/β-catenin pathway inhibitor and p53 pathway activator) to activate the PI3K/AKT, MEK/ERK, and Wnt/β-catenin pathways and inhibit the p53 pathway, and miR-18a also directly suppresses the expression of the tumor suppressors BTG3 and RBSP3 (CTDSPL). miR-18a overexpression in CC cells is triggered by OCT4 overexpression. Our data implicate PD-L1 as a novel oncoprotein and indicate that miR-140/142/340/383 and miR-18a are key upstream regulators of PD-L1 and potential targets for CC treatment