BackgroundPlant cell walls are complex structures that full-fill many diverse functions during plant growth and development. It is therefore not surprising that thousands of gene products are involved in cell wall synthesis and maintenance. However, functional association for the majority of these gene products remains obscure. One useful approach to infer biological associations is via transcriptional coordination, or co-expression of genes. This approach has proved useful for several biological processes. Nevertheless, combining co-expression with other large-scale measurements may improve the biological inferences.ResultsIn this study, we used a combined approach of co-expression and cell wall metabolomics to obtain new insight into cell wall synthesis in rice. We initially created a weighted gene co-expression network from publicly available datasets, and then established a comprehensive cell wall dataset by determining cell wall compositions from 29 tissues that almost cover the whole life cycle of rice. We subsequently combined the datasets through the conversion of co-expressed gene modules into eigen-vectors, representing expression profiles for the genes in the modules, and performed comparative analyses against the cell wall contents. Here, we made three major discoveries. First, we confirmed our approach by finding primary and secondary wall cellulose biosynthesis modules, respectively. Second, we found co-expressed modules that strongly correlated with re-organization of the secondary cell walls and with modifications and degradation of hemicellulosic structures. Third, we inferred that at least one module is likely to play a regulatory role in the production of G-rich lignification.ConclusionsHere, we integrated transcriptomic associations and cell wall metabolism and found that certain co-expressed gene modules are positively correlated with distinct cell wall characteristics. We propose that combining multiple data-types, such as coordinated transcription and cell wall analyses, may be a useful approach to glean new insight into biological processes. The combination of multiple datasets, as illustrated here, can further improve the functional inferences that typically are generated via a single type of datasets. In addition, our data extend the typical co-expression approach to allow deeper insight into cell wall biology in rice.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-596) contains supplementary material, which is available to authorized users.

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A majority of research on mobile-assisted language learning focuses on traditional English language learners: thus, little attention has been paid to older adult learners. The purpose of the study is to explore the learning experiences of Chinese older adults using the free and popular English learning mobile apps, Duolingo/Hello English, Baicizhan, and Liulishuo, in a self-directed learning (SDL) context. A 17-week sequential mixed-methods study was designed. 55 older adults from age 45 to 85 participated. The informed grounded theory was used and Saldana's coding techniques for qualitative analysis. Quantitative data were analyzed using descriptive statistics and paired sample t-tests. Findings demonstrate that older adults persisted in learning using mobile apps for 17 weeks and increased their vocabulary significantly. Finally, a transformational learning model called MISAPP was created based on the empirical data and the SDL theory.

This study investigated the effects of different types of captions on English as a Foreign Language Learners’ (EFL) vocabulary learning and comprehension. Eighty students in a Chinese university participated. Students were divided into four groups with two classes of freshmen, one class of juniors, and one class of graduate students. Each group watched four video clips with four caption conditions: L1 Chinese, L2 English, dual (L1 and L2), and no captions. The order and caption conditions were counterbalanced. The purpose of the study was to find which caption condition is more effective for EFL learners. Four by four mixed ANOVAs were used to compare the differences among the four conditions and groups. Results indicated that students’ performances were statistically significantly different across captions and class levels. In general, students in L1, L2, and dual captions statistically outperformed the no caption condition in vocabulary and comprehension. Results of the effects of L1, L2, and dual captions on vocabulary learning and comprehension were mixed. The pedagogical implications of using authentic TV series and multimedia captions were discussed.

The significance in constructing a driving style identification model for open-pit mine truck drivers is to reduce diesel consumption and improve training. First, we developed a driving behavior and mining truck condition monitoring system for an open-pit mine. Under heavy-load and no-load conditions of a mining truck, based on the same experimental truck and haulage road, the data of driving behavior and truck status of different drivers were collected. The driving style characteristic parameters of mining trucks under heavy-load and no-load conditions were constructed through Pearson correlation analysis. Using a k-means clustering algorithm, driving style can be divided into three types: normal type, soft type, and aggressive type, and we verified the validity of this driving style classification with a box plot. On this basis, the parameters of random forest, k-nearest neighbor, support vector machine, and neural network models were optimized and the accuracy was compared through a cross-validation grid search, and then a driving style identification model based on the random forest method was finally proposed. Driving style parameter weight values were obtained based on the Gini coefficient. Last, the fuel consumption characteristics of different driving styles were calculated. The results show that the driving style identification models based on random forest can effectively identify different driving styles when the mining truck is operating under heavy load and no load, and the overall accuracy of the model is 95.39% and 90.74% respectively. The fuel consumption of the aggressive driving style was the largest and was 10% higher than the average fuel consumption. The research results provide data support and new ideas for operation training and fuel-saving driving of mining trucks in open-pit mines.