Our motivation for conducting this research is driven by the lack of studies focusing on the acknowledgments sections of published papers. Another motivation is the lack of a study examining the countries and organizations mentioned in the acknowledgments section and their influence—something that cannot be analyzed using a citation or co-authorship relationship. Concentrating on the qualitative aspects of acknowledgments has been limited because of the atypical pattern of the acknowledgment section. Our research aims to identify useful information hidden within the acknowledgment sections of the articles stored in the PubMed Central database and to analyze a map of influence via a country-acknowledgment network. To solve the problems, we use the topic modeling to analyze topics of acknowledgments and conduct a basic network analysis to find the difference in the co-the country network and acknowledgment network. A word-embedding model is used to compare the semantic similarity that exists between the authors and countries extracted from our original dataset. The result of topic modeling suggests that funding has become a critical topic in acknowledgments. The results of network analysis indicate that some large countries work as hubs in terms of both implicitly and explicitly while revealing that some countries such as China do not frequently work with other countries. The word-embedding model built by acknowledgments suggests that the authors frequently referenced in acknowledgments are also likely to be referred to in a similar context. It also implies that the publishing country of a paper has little effect on whether it receives an acknowledgment from any other specific country. Through these results, we conclude that the content in acknowledgments extracted from the papers can be divided into two categories—funding and appreciation. We also find that there is no clear relationship between the publication country and the countries mentioned in the acknowledgment section.
Hyoungsuk Lee, Min-Churl Song, Jung-Chun Suh and Bong-Jun Chang
A reliable steady/transient hydro-elastic analysis is developed for flexible (composite) marine propeller blade design which deforms according to its environmental load (ship speed, revolution speed, wake distribution, etc.) Hydro-elastic analysis based on CFD and FEM has been widely used in the engineering field because of its accurate results however it takes large computation time to apply early propeller design stage. Therefore the analysis based on a boundary element method-Finite Element Method (BEM-FEM) Fluid-Structure Interaction (FSI) is introduced for computational efficiency and accuracy. The steady FSI analysis, and its application to reverse engineering, is designed for use regarding optimum geometry and ply stack design. A time domain two-way coupled transient FSI analysis is developed by considering the hydrodynamic damping ffects of added mass due to fluid around the propeller blade. The analysis makes possible to evaluate blade strength and also enable to do risk assessment by estimating the change in performance and the deformation depending on blade position in the ship’s wake. To validate this hydro-elastic analysis methodology, published model test results of P5479 and P5475 are applied to verify the steady and the transient FSI analysis, respectively. As the results, the proposed steady and unsteady analysis methodology gives sufficient accuracy to apply flexible marine propeller design
Xianlei Dong, Jian Xu, Ying Ding, Chenwei Zhang, Kunpeng Zhang and Min Song
We propose and apply a simplified nowcasting model to understand the correlations between social attention and topic trends of scientific publications.
First, topics are generated from the obesity corpus by using the latent Dirichlet allocation (LDA) algorithm and time series of keyword search trends in Google Trends are obtained. We then establish the structural time series model using data from January 2004 to December 2012, and evaluate the model using data from January 2013. We employ a state-space model to separate different non-regression components in an observational time series (i.e. the tendency and the seasonality) and apply the “spike and slab prior” and stepwise regression to analyze the correlations between the regression component and the social media attention. The two parts are combined using Markov-chain Monte Carlo sampling techniques to obtain our results.
The results of our study show that (1) the number of publications on child obesity increases at a lower rate than that of diabetes publications; (2) the number of publication on a given topic may exhibit a relationship with the season or time of year; and (3) there exists a correlation between the number of publications on a given topic and its social media attention, i.e. the search frequency related to that topic as identified by Google Trends. We found that our model is also able to predict the number of publications related to a given topic.
First, we study a correlation rather than causality between topics’ trends and social media. As a result, the relationships might not be robust, so we cannot predict the future in the long run. Second, we cannot identify the reasons or conditions that are driving obesity topics to present such tendencies and seasonal patterns, so we might need to do “field” study in the future. Third, we need to improve the efficiency of our model by finding more efficient variable selection models, because the stepwise regression method is time consuming, especially for a large number of variables.
This paper analyzes publication topic trends from three perspectives: tendency, seasonality, and correlation with social media attention, providing a new perspective for identifying and understanding topical themes in academic publications.
To the best of our knowledge, we are the first to apply the state-space model to examine the relationships between healthcare-related publications and social media to investigate the relationships between a topic’s evolvement and people’s search behavior in social media. This paper thus provides a new viewpoint in the correlation analysis area, and demonstrates the value of considering social media attention in the analysis of publication topic trends.
Liang Cao, Zhao-min Song, Quan Liu, Jun Sheng, Pei-li Zhao and Xun Peng
Bronchobiliary fistula (BBF) is one of the very rare disease. In this report, we described a BBF case. The case was diagnosed by endoscopic retrograde cholangio-pancreatography (ERCP) and percutaneous transhepatic cholangial drainage (PTCD) examinations, and treated properly. From the diagnosis of this BBF case, a patient with cough, biliptysis, fever and pain, should be considered for diagnosis of BBF.
Ming-hui Li, Yao Xie, Yao Lu, Guo-hua Qiu, Lu Zhang, Ge Shen, Li-wei Zhuang, Ju-long Hu, Jian-ping Dong, Cai-qin Mu, Lei-ping Hu, Li-jun Chen, Xing-hong Li, Min Yang, Yun-zhong Wu, Hui Zhao, Shu-jing Song, Jun Cheng and Dao-zhen Xu
Objective To investigate the effects of individualised treatment with peginterferon alpha-2a (40 kD) plus ribavirin in Chinese patients with CHC.
Methods Total of 297 consecutive Chinese patients were enrolled, including 250 naïve cases and 47 cases who were previously treated. Treatment duration was determined according to viral genotypes, prior treatment history and viral responses at week 4, 12 and 24.
Results Totally, 235 patients (79.1%) completed treatment and 186 (87.3%) achieved SVR. And 219 out of 289 (75.8%) patients achieved HCV RNA negative at week 4 (RVR) and 259 of 276 (93.8%) at week 12. Among the 164 patients with RVR who completed follow-up, 158 (96.3%) achieved SVR. Patients with RVR had lower baseline viral loads than patients without RVR (P = 0.034). The positive predictive value (PPV) of RVR for SVR was 90.7% (OR 2.10 vs. non-RVR, 95% CI: 0.50 - 8.7). Similar outcomes were observed among patients with HCV undetectable at week 12.
Conclusions Complete viral suppression by week 4 is associated with a high rate of treatment success in treatment naïve and experienced patients receiving individualized CHC therapy.