Aerial View of the Association Between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer

Pancreatic cancer is a highly malignant tumor with a poor survival prognosis. We attempted to establish a robust prognostic model to elucidate the clinicopathological association between lncRNA, which may lead to poor prognosis by influencing m6A modification, and pancreatic cancer. We investigated the lncRNAs expression level and the prognostic value in 440 PDAC patients and 171 normal tissues from GTEx, TCGA, and ICGC databases. The bioinformatic analysis and statistical analysis were used to illustrate the relationship. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier methods were performed to identify the seven prognostic lncRNAs signatures. We inputted them in the LASSO Cox regression to establish a prognostic model in the TCGA database, verified in the ICGC database. The AUC of the ROC curve of the training set is 0.887, while the validation set is 0.711. Each patient has calculated a risk score and divided it into low-risk and high-risk subgroups by the median value. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. We established a ceRNA network between lncRNAs and m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNA. We have even identified small molecule drugs, such as Thapsigargin, Mepacrine, and Ellipticine, that may affect pancreatic cancer progression. We found that seven lncRNAs were highly expressed in tumor patients in the GTEx-TCGA database, and LncRNA CASC19/UCA1/LINC01094/LINC02323 were confirmed in both pancreatic cell lines and FISH relative quantity. We provided a comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer’s clinicopathological characteristics, and performed experiments to verify the robustness of the prognostic model.

A pedestrian detection algorithm for low light and dense crowd Based on improved YOLO algorithm

The real-time pedestrian detection algorithm requires the model to be lightweight and robust. At the same time, the pedestrian object detection problem has the characteristics of aerial view Angle shooting, object overlap and weak light, etc. In order to design a more robust real-time detection model in weak light and crowded scene, this paper based on YOLO, raised a more efficient convolutional network. The experimental results show that, compared with YOLOX Network, the improved YOLO Network has a better detection effect in the lack of light scene and dense crowd scene, has a 5.0% advantage over YOLOX-s for pedestrians AP index, and has a 44.2% advantage over YOLOX-s for fps index.

Multi-View Urban Scene Classification with a Complementary-Information Learning Model

Traditional urban scene-classification approaches focus on images taken either by satellite or in aerial view. Although single-view images are able to achieve satisfactory results for scene classification in most situations, the complementary information provided by other image views is needed to further improve performance. Therefore, we present a complementary information-learning model (CILM) to perform multi-view scene classification of aerial and ground-level images. Specifically, the proposed CILM takes aerial and ground-level image pairs as input to learn view-specific features for later fusion to integrate the complementary information. To train CILM, a unified loss consisting of cross entropy and contrastive losses is exploited to force the network to be more robust. Once CILM is trained, the features of each view are extracted via the two proposed feature-extraction scenarios and then fused to train the support vector machine classifier for classification. The experimental results on two publicly available benchmark data sets demonstrate that CILM achieves remarkable performance, indicating that it is an effective model for learning complementary information and thus improving urban scene classification.

La exposición de arte visual novelada por Enrique Vila-Matas

The Art Exhibition as a Novel by Enrique Vila-Matas. I set out to analyze, using the New Rhetoric methods, the book entitled Cabinet d`amateur, an oblique novel published by the contemporary Spanish writer Enrique Vila-Matas in 2019, at the same time as the opening of an exhibition whose curator he was in the Whitechapel Gallery in east London. Choosing the six visual art works, different in nature, concept, and aesthetics, from the collection of the “laCaixa” Foundation represents an occasion for the writer, led by curiosity, to investigate the works of art and to make a personal, purely subjective selection, on which he reflects in his heterogenic work as a genre: the catalogue of an exhibition, memoirs, essay (auto)biography, the skeleton of an oblique novel of the future. The selected works of art (I.G., the mysterious portrait of a woman by the painter Gerhard Richter; an installation, Petite, by Dominique Gonzalez-Foerster; a videoclip, La lección respiratoria, belonging to the artist Dora García; Milonga, Carlos Pazos’s self-portrait; a detailed scenery, Une poignée de terre, by Miquel Barceló and a photography of Theban by Andreas Gursky, in an overlap of an aerial view with one detailed figure) are included in the text as a starting point for meditations and reflections upon the nature of the art in general, because the metaphor of the literary work, the novel of the future, is precisely the building of Rem Koolhass, the library in Seattle, in which different styles overlap and whose shapes are imprecise, undetermined, incongruent, disharmonic and lacking in logic. Literature, visual arts, music, and architecture are associated, and different figures are used to point out the aesthetic of the negative and the idea that form and content are interchangeable. Keywords: Enrique Vila-Matas, Cabinet d´amateur, an oblique novel, general-rhetoric analysis, the exhibition as a novel, literary biography

Human Rights, Posthuman Ethics, and the Material Aesthetics of Flight in Contemporary Irish Poetry

This article examines a series of poems by Irish authors, and focuses on their engagement with human rights violations and conflicts through the metaphors and imagery of flight and the aerial view. It argues that these poems address the need for a shift away from the perspective of a defined, distinct human subject, and towards a posthumanist framework which emphasizes relational, situated, and embodied ethics and aesthetics in an interconnected world. Since the introduction of modern aviation, Irish poets have frequently employed the imagery of flying to consider poetry's role in relation to conflict and crisis. Here, the adoption of visual and material metaphors of flight and aerial travel in human rights contexts is discussed, particularly in poems by Seamus Heaney, Peter Sirr and Justin Quinn. Through a reimagined poetics of flight, these poets question established dichotomies between proximity and distance, and material embodiment and disembodied abstraction.

Detection for Dangerous Goods Vehicles in Expressway Service Station Based on Surveillance Videos

To improve the safety capabilities of expressway service stations, this study proposes a method for detecting dangerous goods vehicles based on surveillance videos. The information collection devices used in this method are the surveillance cameras that already exist in service stations, which allows for the automatic detection and position recognition of dangerous goods vehicles without changing the installation of the monitoring equipment. The process of this method is as follows. First, we draw an aerial view image of the service station to use as the background model. Then, we use inverse perspective mapping to process each surveillance video and stitch these videos with the background model to build an aerial view surveillance model of the service station. Next, we use a convolutional neural network to detect dangerous goods vehicles from the original images. Finally, we mark the detection result in the aerial view surveillance model and then use that model to monitor the service station in real time. Experiments show that our aerial view surveillance model can achieve the real-time detection of dangerous goods vehicles in the main areas of the service station, thereby effectively reducing the workload of the monitoring personnel.