No abstract

Objective. To evaluate the efficiency of low-level laser therapy on the distal osseous defects of the mandibular second molar (M2) after the adjacent impacted third molar (M3) extraction. Methods. A total of 59 clinic cases were screened out, whose M3 were impacted and the distal alveolar bone of M2 had been destroyed horizontally. They were randomly divided into 2 groups based on whether they would have laser irradiation or not after M3 extraction. Then, postoperative complications of the 2 groups were compared. The alveolar bone level distal to M2 was established before and 3 to 6 months after M3 extraction by radiographic evaluation, which was compared between two groups. Results. The incidence of severe pain and mouth-opening limitation was significantly lower in the LLLT group than that in the control group. The amount of bone formation in the LLLT group was higher than that in the control group 3 months after the operation, and the difference was statistically significant. But the difference was not statistically significant 6 months after surgery. Conclusion. LLLT may alleviate postoperative complications and improve early osteogenesis. It is a viable option for use in the treatment of osseous defects distal to mandibular second molars following extraction of impacted third molars.

Fake news detection has become a significant topic based on the fast-spreading and detrimental effects of such news. Many methods based on deep neural networks learn clues from claim content and message propagation structure or temporal information, which have been widely recognized. However, such models (i) ignore the fact that information quality is uneven in propagation, which makes semantic representations unreliable. (ii) Most models do not fully leverage spatial and temporal structure in combination. (iii) Finally, internal decision-making processes and results are non-transparent and unexplained. In this study, we develop a trust-aware evidence reasoning and spatiotemporal feature aggregation model for more interpretable and accurate fake news detection. Specifically, we first design a trust-aware evidence reasoning module to calculate the credibility of posts based on a random walk model to discover high-quality evidence. Next, from the perspective of spatiotemporal structure, we design an evidence-representation module to capture the semantic interactions granularly and enhance the reliable representation of evidence. Finally, a two-layer capsule network is designed to aggregate the implicit bias in evidence while capturing the false portions of source information in a transparent and interpretable manner. Extensive experiments on two benchmark datasets indicate that the proposed model can provide explanations for fake news detection results, as well as can achieve better performance, boosting 3.5% in F1-score on average.

Fake news detection has become a significant topic based on the fast-spreading and detrimental effects of such news. Many methods based on deep neural networks learn clues from claim content and message propagation structure or temporal information, which have been widely recognized. However, firstly, such models ignore the fact that information quality is uneven in propagation, which makes semantic representations unreliable. Additionally, most models do not fully leverage spatial and temporal structures in combination. Finally, internal decision-making processes and results are non-transparent and unexplained. In this study, we developed a trust-aware evidence reasoning and spatiotemporal feature aggregation model for more interpretable and accurate fake news detection. Specifically, we first designed a trust-aware evidence reasoning module to calculate the credibility of posts based on a random walk model to discover high-quality evidence. Next, from the perspective of spatiotemporal structure, we designed an evidence-representation module to capture the semantic interactions granularly and enhance the reliable representation of evidence. Finally, a two-layer capsule network was designed to aggregate the implicit bias in evidence while capturing the false portions of source information in a transparent and interpretable manner. Extensive experiments on two benchmark datasets indicate that the proposed model can provide explanations for fake news detection results, and can also achieve better performance, boosting the F1-score 3.5% on average.