Search

Osteopontin (OPN) is a secreted phosphosylated protein, which is widely found in various tissues and body fluids. OPN involved in various physiological and pathological processes such as bone growth, wound healing and cell adhesion and migration. The integrin αvβ3 is the main receptor for OPN. Recent studies have shown that the high expression level of OPN is closely related to the invasive phenotype and poor prognosis of non-small cell lung cancer (NSCLC) . OPN is expected to become a potential serological biomarker and therapeutic drug target for the disease, which has gradually attracted the attention of researchers at home and abroad. Therefore, this paper presents the structural characteristics of OPN and its correlation with the occurrence, development and treatment prognosis of NSCLC.

The new changes brought about by the information age, such as new media and "Internet+" education, have given new impetus to college teaching. We make full use of computer Information Technology, combining the "Course Partner" platform with the PAD Class, to create a new hybrid teaching mode for ideological and political courses, bringing new ideas and methods to teaching reform. This article takes the course "Introduction to Xi Jinping Thought of Socialism with Chinese Characteristics for a New Era “as an example, applies a new hybrid teaching mode, adheres to the student-centered education concept, and designs the course. By analyzing data from computer network teaching platforms, timely optimization and improvement of teaching and auxiliary content are carried out in teaching, which enhances the interaction between teachers and students in class and students’ learning enthusiasm, effectively improving the teaching effect of ideological and political courses, It further explains that the new hybrid teaching mode combining the "Course Partner" platform with the PAD Class is an economic and efficient teaching mode integrating informatization and “Internet + ”education.

Generating image captions in different languages is worth exploring and essential for non-native speakers. Nevertheless, collecting paired annotation for every language is time-consuming and impractical, particularly for minor languages. To this end, the cross-lingual image captioning task is proposed, which leverages existing image-source caption annotation data and wild unrelated target corpus to generate satisfactory caption in the target language. Current methods perform a two-step translation process of image-to-pivot (source) and pivot-to-target. The distinct two-step process comes with certain caption issues, such as the weak semantic alignment between the image and the generated caption and the generated caption's non-target language style. To address these issues, we propose an end-to-end reinforce learning framework with Visual-linguistic-stylistic Triple Reward named TriR. In TriR, we jointly consider the visual, linguistic, and stylistic alignments to generate factual, fluent, and natural caption in the target language. To be specific, the image-source caption annotation provides factual semantic guidance, whereas the unrelated target corpus guides the language style of generated caption. To achieve this, we construct a visual reward module to measure the cross-modal semantic embedding of image and target caption, a linguistic reward module to measure the cross-linguistic embedding of source and target captions, and a stylistic reward module to imitate the presentation style of target corpus. The TriR can be implemented with either classical CNN-LSTM or prevalent Transformer architecture. Extensive experiments are conducted with four cross-lingual settings, i.e., Chinese-to-English, English-to-Chinese, English-to-German, and English-to-French. Experimental results demonstrate the remarkable superiority of our method, and sufficient ablation experiments validate the beneficial impact of every reward. [ABSTRACT FROM AUTHOR]