Sichuan University

About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.

Prognostic significance of tumor-associated macrophages in solid tumor: a meta-analysis of the literature.

Abstract: Purpose Tumor associated macrophages (TAMs) are considered with the capacity to have both negative and positive effects on tumor growth. The prognostic value of TAM for survival in patients with solid tumor remains controversial.

Mn2+ Dopant‐Controlled Synthesis of NaYF4:Yb/Er Upconversion Nanoparticles for in vivo Imaging and Drug Delivery

Abstract: Pure dark red emission (650-670 nm) of NaYF(4):Yb/Er upconversion nanoparticles (UCNPs) is achieved by manganese ions (Mn(2+)) doping. In addition, the Mn(2+)-doping can also control the crystalline phase and size of the resulting UCNPs simultaneously. Drug delivery studies suggest the promise of these UCNPs as drug carriers for intracellular drug delivery and eventually as a multifunctional nanoplatform for simultaneous diagnosis and therapy.

The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Abstract: Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Abstract: We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthens the expressive power of our model for handling more complications The weighted-memory mechanism not only helps us avoid the labor-intensive feature engineering work, but also provides a tailor-made memory for different opinion targets of a sentence We examine the merit of our model on four datasets: two are from SemEval2014, ie reviews of restaurants and laptops; a twitter dataset, for testing its performance on social media data; and a Chinese news comment dataset, for testing its language sensitivity The experimental results show that our model consistently outperforms the state-of-the-art methods on different types of data