An inflammatory-related genes signature based model for prognosis prediction in breast cancer

Background:Breast cancer has become the most common malignant tumor in the world.It is vital to discover novel prognostic biomarkers despite the fact that the majority of breast cancer patients have a good prognosis because of the high heterogeneity of breast cancer,which causes the disparity in prognosis.Recently,inflammatory-related genes have been proven to play an important role in the development and progression of breast cancer,so we set out to investigate the predictive usefulness of inflammatory-related genes in breast malignancies.Methods:We assessed the connection between Inflammatory-Related Genes(IRGs)and breast cancer by studying the TCGA database.Following differential and univariate Cox regression analysis,prognosis-related differentially expressed inflammatory genes were estimated.The prognostic model was constructed through the Least Absolute Shrinkage and Selector Operation(LASSO)regression based on the IRGs.The accuracy of the prognostic model was then evaluated using the Kaplan-Meier and Receiver Operating Characteristic(ROC)curves.The nomogram model was established to predict the survival rate of breast cancer patients clinically.Based on the prognostic expression,we also looked at immune cell infiltration and the function of immune-related pathways.The CellMiner database was used to research drug sensitivity.Results:In this study,7 IRGs were selected to construct a prognostic risk model.Further research revealed a negative relationship between the risk score and the prognosis of breast cancer patients.The ROC curve proved the accuracy of the prognostic model,and the nomogram accurately predicted survival rate.The scores of tumorinfiltrating immune cells and immune-related pathways were utilized to calculate the differences between the low-and high-risk groups,and then explored the relationship between drug susceptibility and the genes that were included in the model.Conclusion:These findings contributed to a better understanding of the function of inflammatory-related genes in breast cancer,and the prognostic risk model provides a potentially promising prognostic strategy for breast cancer.

THE C_n WP-BAILEY CHAIN

The purpose of this paper is to introduce the concept of Cn WP-Bailey pairs. The Cn WP-Bailey transform is obtained by applying the Ca 6Ф5 summation formula. From thisresult, the C,, WP-Bailey lemma is deduced by making use of the Cn q-Dougall summationformula. Some applications are investigated. Finally, the case of elliptic Cn WP-Bailey pairsis discussed.

Ground-Based Cloud Recognition Based on Dense_SIFT Features

Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere.Currently,measurement of meteorological elements such as temperature,air pressure,humidity,and wind has been automated.However,the cloud's automatic identification technology is still not perfect.Thus,this paper presents an approach that extracts dense scale-invariant feature transform(Dense_SIFT)as the local features of four typical cloud images.The extracted cloud features are then clustered by K-means algorithm,and the bag-of-words(BoW)model is used to describe each ground-based cloud image.Finally,support vector machine(SVM)is used for classification and recognition.Based on this design,a nephogram recognition intelligent application is implemented.Experiments show that,compared with other classifiers,our approach has better performance and achieved a recognition rate of 88.1%.

CW-YOLO: joint learning for mask wearing detection in low-light conditions

1 Introduction With the rapid progress of Artificial Intelligence(AI)technology in object detection and face recognition,deep learning methods for face mask wearing detection have become increasingly mature and continuously take into account the needs of efficiency and accuracy.However,these conventional detection methods mostly ignore the complexity of real-world application scenarios,such as extremely darkness and gloomy weather.These unfavorable conditions lead to a series of image degradations that seriously hamper machine vision tasks.Although camera parameter adjustment,auxiliary lighting,or pre-processing enhancement[1]can weaken these negative effects to some extent to promote the detection,they will also result in increased hardware and time costs.