BackgroundAnalysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behind the low vaccine coverage and come up with corresponding strategies to improve vaccine uptake.ObjectiveTo propose a machine learning system that is able to extract comprehensive public sentiment on HPV vaccines on Twitter with satisfying performance.MethodWe collected and manually annotated 6,000 HPV vaccines related tweets as a gold standard. SVM model was chosen and a hierarchical classification method was proposed and evaluated. Additional feature sets evaluation and model parameters optimization was done to maximize the machine learning model performance.ResultsA hierarchical classification scheme that contains 10 categories was built to access public opinions toward HPV vaccines comprehensively. A 6,000 annotated tweets gold corpus with Kappa annotation agreement at 0.851 was created and made public available. The hierarchical classification model with optimized feature sets and model parameters has increased the micro-averaging and macro-averaging F score from 0.6732 and 0.3967 to 0.7442 and 0.5883 respectively, compared with baseline model.ConclusionsOur work provides a systematical way to improve the machine learning model performance on the highly unbalanced HPV vaccines related tweets corpus. Our system can be further applied on a large tweets corpus to extract large-scale public opinion towards HPV vaccines.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0120-6) contains supplementary material, which is available to authorized users.

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

Multiscale entropy (MSE) analysis is a novel entropy-based analysis method for quantifying the complexity of dynamic neural signals and physiological systems across multiple temporal scales. This approach may assist in elucidating the pathophysiologic mechanisms of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Using resting-state fNIRS imaging, we recorded spontaneous brain activity from 31 healthy controls (HC), 27 patients with aMCI, and 24 patients with AD. The quantitative analysis of MSE revealed that reduced brain signal complexity in AD patients in several networks, namely, the default, frontoparietal, ventral and dorsal attention networks. For the default and ventral attention networks, the MSE values also showed significant positive correlations with cognitive performances. These findings demonstrated that the MSE-based analysis method could serve as a novel tool for fNIRS study in characterizing and understanding the complexity of abnormal cortical signals in AD cohorts.

Facing fierce competition in the global manufacturing industry, manufacturers have begun developing value-added services to increase their competitiveness. One type of sales strategy that has become prevalent is for companies to offer consumers, a relatively low price for value-added services when consumers buy the services simultaneously with the product and a relatively high price for the same value-added services in the future. Considering the degree of information asymmetry about service quality and the changes of it in a product life cycle, this paper examines the pricing of product and value-added service. Four types of pricing strategies are proposed that depend on the time that consumers choose to buy the value-added service. In addition, the results of our study show that a manufacturer choosing a pricing strategy should consider the degree of information asymmetry and consumers' initial expectations regarding the value-added service quality. When consumers' initial expectations regarding the value-added service quality are high, the manufacturer should choose one of the four pricing strategies that encourage consumers to buy the service simultaneously with the product. When consumers' initial expectations regarding the value-added service quality are low, the optimal strategy for the manufacturer depends on the difference in the degree of information asymmetry.

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