Shandong Normal University

About: Shandong Normal University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Catalysis. The organization has 12378 authors who have published 12576 publications receiving 174572 citations.

Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

Abstract: Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user's preferences and targeted item's features. Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.

Mechanisms and Therapeutic Regulation of Pyroptosis in Inflammatory Diseases and Cancer

Abstract: Programmed Cell Death (PCD) is considered to be a pathological form of cell death when mediated by an intracellular program and it balances cell death with survival of normal cells. Pyroptosis, a type of PCD, is induced by the inflammatory caspase cleavage of gasdermin D (GSDMD) and apoptotic caspase cleavage of gasdermin E (GSDME). This review aims to summarize the latest molecular mechanisms about pyroptosis mediated by pore-forming GSDMD and GSDME proteins that permeabilize plasma and mitochondrial membrane activating pyroptosis and apoptosis. We also discuss the potentiality of pyroptosis as a therapeutic target in human diseases. Blockade of pyroptosis by compounds can treat inflammatory disease and pyroptosis activation contributes to cancer therapy.

Epileptic seizure detection based on imbalanced classification and wavelet packet transform

Abstract: Purpose Automatic seizure detection is significant for the diagnosis of epilepsy and the reduction of massive workload for reviewing continuous EEG recordings. Methods Compared with the long non-seizure periods, the durations of the seizure events are much shorter in the continuous EEG recordings. So the seizure detection task can be regarded as an imbalanced classification problem. In this paper, a novel method based on the weighted extreme learning machine (ELM) is proposed for seizure detection with imbalanced EEG data distribution. Firstly, the wavelet packet transform is employed to analyze the EEG data and obtain the time and frequency domain features, and the pattern match regularity statistic (PMRS) is used as the nonlinear feature to quantify the complexity of the EEG time series. After that, the EEG feature vectors are discriminated by the weighted ELM. It can assign different weights for the EEG feature samples according to the class distribution, so that to effectively moderate the bias in performance caused by imbalanced class distribution. Results The metric G-mean which takes into account of both the sensitivity and specificity is used to evaluate the performance of this method. The G-mean of 93.96%, event-based sensitivity of 97.73% and false alarm rate of 0.37/h are yielded on the publicly available EEG dataset. Conclusion The comparison with other detection methods shows the superior performance of this method, which indicates its potential for detecting seizure events in clinical practice. Additionally, much larger amounts of true continuous EEG data will be used to test the proposed method further in the future work.