Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Guided by the socio-environmental theoretical framework, this study examined factors associated with life satisfaction experienced by older Chinese adults living in rural communities. The data used in this study were extracted from the Sample Survey on Aged Population in Urban/Rural China conducted by the China Research Center on Aging in 2000. This study included 10,084 rural older adults in mainland China. In this study 60.2 % of rural older adults were satisfied with their lives. Results from a multinomial logistic regression analysis showed that life satisfaction reported by rural older Chinese adults was significantly related to education, financial resources, self-rated health, financial support from children, satisfaction with children's support, house sitting for their children, visiting neighbors, and being invited to dinner by neighbors. Research and policy implications of these findings are also discussed.

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The ability to integrate environmental and developmental signals with physiological responses is critical for plant survival. How this integration is done, particularly through posttranscriptional control of gene expression, is poorly understood. Previously, it was found that the 30 kD subunit of Arabidopsis cleavage and polyadenylation specificity factor (AtCPSF30) is a calmodulin-regulated RNA-binding protein. Here we demonstrated that mutant plants (oxt6) deficient in AtCPSF30 possess a novel range of phenotypes – reduced fertility, reduced lateral root formation, and altered sensitivities to oxidative stress and a number of plant hormones (auxin, cytokinin, gibberellic acid, and ACC). While the wild-type AtCPSF30 (C30G) was able to restore normal growth and responses, a mutant AtCPSF30 protein incapable of interacting with calmodulin (C30GM) could only restore wild-type fertility and responses to oxidative stress and ACC. Thus, the interaction with calmodulin is important for part of AtCPSF30 functions in the plant. Global poly(A) site analysis showed that the C30G and C30GM proteins can restore wild-type poly(A) site choice to the oxt6 mutant. Genes associated with hormone metabolism and auxin responses are also affected by the oxt6 mutation. Moreover, 19 genes that are linked with calmodulin-dependent CPSF30 functions, were identified through genome-wide expression analysis. These data, in conjunction with previous results from the analysis of the oxt6 mutant, indicate that the polyadenylation factor AtCPSF30 is a regulatory hub where different signaling cues are transduced, presumably via differential mRNA 3′ end formation or alternative polyadenylation, into specified phenotypic outcomes. Our results suggest a novel function of a polyadenylation factor in environmental and developmental signal integration.

Recent increases in railway patronage worldwide have created pressure on rolling stock and railway infrastructure through the demand to improve the capacity and punctuality of the whole system, and this demand must also be balanced with reducing life-cycle costs. Condition monitoring is seen as a significant contributor in achieving this. The emphasis of this article is on the use of sensors mounted on rolling stock to monitor the condition of infrastructure and the rolling stock itself. This is set in the context of modern rolling stock being fitted with high-capacity communication buses and multiple sensors, resulting in the potential for advanced processing of collected data. This article brings together linked research that uses a similar set of rolling stock sensors, and discusses: general usage and benefits, a track defect detection method, running gear condition monitoring, and absolute train speed detection.