Search

With the rapid development of the Internet and mobile information technology, the state has made it clear that "Internet¬+" education has become an important development project for national education. Information technology education is also playing an increasingly important role in the development of students, and online and offline hybrid teaching is a teaching mode that is very suitable for information technology. Online and offline blended teaching is a new teaching model that has emerged in the mobile internet era, and is a combination of online learning and the advantages of traditional teaching. However, there are still problems with online and offline blended teaching such as uncertainty in the use of educational resources, so a more appropriate teaching process system needs to be tapped to achieve better teaching results.

Background Racial disparities in endometrial cancer have been reported in the United States, but trends and the underlying causes are not well understood. We aimed to examine the trends and contributing factors in racial disparities for causes of death among endometrial cancer patients. Method In this population-based cohort study, we identified 139 473 women diagnosed with first, primary endometrial cancer between 1992 to 2018 from the Surveillance, Epidemiology, and End Results Program. We used the "Fine and Gray" method to calculate the cumulative incidence of all-cause and specific-cause death. We used proportional subdistribution hazard (PSH) and cause-specific hazard (CSH) models to quantify the relative risk of Black–White disparities. We performed a mediation analysis to assess the contribution of potential factors to disparities. Results The cumulative incidence of all-cause death decreased in endometrial cancer patients, with estimates at 5 years of 26.72% in 1992-1996 and 22.59% in 2007-2011. Compared with White patients, Black patients persistently had an increased risk of death due to endometrial cancer (PSH hazard ratio [HR] = 2.05, 95% confidence interval [CI] = 1.90 to 2.22; CSH HR = 2.19, 95% CI = 2.00 to 2.40) and causes other than endometrial cancer (PSH HR = 1.23, 95% CI = 1.10 to 1.37; CSH HR = 1.46, 95% CI = 1.31 to 1.63). Grade, histological subtype, surgery utilization, and stage at diagnosis explained 24.4%, 20.1%, 18.4%, and 16.6% of the Black-White disparity in all-cause death, respectively. Conclusions Although the cumulative incidence of all-cause death decreased, the Black–White gaps persisted in patients with endometrial cancer. Grade and histological subtype had the greatest influence. More efforts are needed to address the disparities. [ABSTRACT FROM AUTHOR]

Background. The tumor microenvironment (TME) has gradually entered the vision of researchers and is becoming a vital part of the occurrence of cervical squamous cell carcinoma (CSCC). However, understanding the specific composition of TME still confront enormous challenges, particularly immune and stromal components. Methods. In this study, we performed an unsupervised cluster analysis to determine the immunogenic cell death-associated subtype of CSCC patients. The differences in immune status, genomic alteration, and clinical outcomes between each subtype were compared. Subsequently, we screened vital prognostic factors. The HPA database was employed to verify the protein localization and the expression level between cancer and adjacent tissues. Results. CSCC patients were divided into three subtypes according to the expression of immunogenic cell death-associated genes. Cluster C has the highest survival rate because of the lower activation of tumor-related pathways. The immune score and stromal score of patients with Cluster B were the highest, so it may be considered that stromal tissue inhibits the anti-tumor effect of immunocytes. In addition, we constructed a risk score based on immunogenic cell death-associated genes to screen for vital markers. We systematically revealed the genomic alteration of vital markers. Conclusions. We have established a novel immunogenic cell death-associated risk scoring system in CSCC, and the expression of immunogenic cell death-associated genes may be a valuable biomarker for immunotherapy strategies. Our work may contribute to the development of new immunomodulators and develop new precision immunotherapies for CSCC. [ABSTRACT FROM AUTHOR]

Abstract: Intelligent data searching of network is a hotspot research aspect currently. When the user puts forward a request that the system should provide a right answer from the vast data online, it is a really hard to resolve such problem. Our research work on core algorithm for intelligent searching system is right way for answer those questions. [Copyright &y& Elsevier]

Abstract Object pose estimation from an RGB image has recently become a common problem owing to its widespread application. The advent of convolutional neural networks has impelled significant progress in object detection. However, most available methods do not involve a category-level pose estimation approach. This study presents an end-to-end 6D category-level pose estimation based on a two-stage bounding-box recognition backbone architecture. Our network directly outputs the 6D pose without requiring multiple stages or additional post-processing such as a Perspective-n-Point (PnP). The two-stage CNN architecture and our loss function render multi-task joint training effective and efficient. We improve the pose estimation accuracy by replacing fully connected layers with fully convolutional layers. Fully convolutional networks require fewer parameters and are less susceptible to overfitting. Moreover, we transform the pose estimation problem into classification and regression tasks using our network; these are called Pose-cls and Pose-reg, respectively. We also present qualitative and quantitative results on real data from the SUN RGB-D dataset. The experiments demonstrate the effectiveness of our algorithms compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]