A novel genetic marker of the ovomucoid gene associated with hatchability in Tsaiya ducks (Anas platyrhynchos)

Transcriptome analysis using a cDNA microarray was performed to identify differentially expressed genes that are correlated with hatchability, and a new PCR-RFLP marker of high hatchability among the identified genes was observed. We used the cDNA microarray technique for gene expression profiling of the magnum epithelium of laying Tsaiya ducks, and several regulated genes associated with hatchability were found. The results of real-time PCR and Western blotting analysis confirmed that the mRNA and protein levels of ovomucoid in the magnum epithelium of animals in the low-hatchability group were significantly higher than the levels in the high-hatchability group ( P < 0.05). Primers TovF1 and TovR1, designed according to the ovomucoid EST sequence, were used to amplify genomic DNA samples of different individual Tsaiya ducks, and sequence analysis of the amplified DNA products showed deletion among the ducks from the low-hatchability group. Primers TovF2 and TovR2 were used to perform PCR-RFLP analysis on the amplified DNA products to classify the ducks into +/+, +/- and -/- genotypes. The animals of +/+ and +/- genotypes were identified as having significantly higher hatchability than those of the -/- genotype ( P < 0.05). In contrast, no differences were observed between genotypes in terms of fertility, duration of fertility, egg weight or total number of eggs. Our results indicated that a novel PCR-RFLP marker of high hatchability, an ovomucoid gene polymorphism, can be used as a genetic marker for marker-assisted selection to improve hatchability in Tsaiya ducks

A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector

Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers' segmentation. The developed methodology has been implemented for a private auto insurance company in Iran which classified customers into four “best”, “new”, “risky”, and “uncertain” patterns. Then, association rules among auto insurance services in two most valuable customer segments including “best” and “risky” patterns are discovered and proposed. Finally, some marketing strategies based on the research results are proposed. The authors believe the result of this paper can provide a noticeable capability to the insurer company in order to assess its customers' loyalty in marketing strategy

A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector

Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers' segmentation. The developed methodology has been implemented for a private auto insurance company in Iran which classified customers into four “best”, “new”, “risky”, and “uncertain” patterns. Then, association rules among auto insurance services in two most valuable customer segments including “best” and “risky” patterns are discovered and proposed. Finally, some marketing strategies based on the research results are proposed. The authors believe the result of this paper can provide a noticeable capability to the insurer company in order to assess its customers' loyalty in marketing strategy