Cytochrome P450 aromatase in grey mullet: cDNA and promoter isolation; brain, pituitary and ovarian expression during puberty.

In a study towards elucidating the role of aromatases during puberty in female grey mullet, the cDNAs of the brain (muCyp19b) and ovarian (muCyp19a) aromatase were isolated by RT-PCR and their relative expression levels were determined by quantitative real-time RT-PCR. The muCyp19a ORF of 1515 bp encoded 505 predicted amino acid residues, while that of muCyp19b was 1485 bp and encoded 495 predicted amino acid residues. The expression level of muCyp19b significantly increased in the brain as puberty advanced; however, its expression level in the pituitary increased only slightly with pubertal development. In the ovary, the muCyp19a expression level markedly increased as puberty progressed. The promoter regions of the two genes were also isolated and their functionality evaluated in vitro using luciferase as the reporter gene. The muCyp19a promoter sequence (650 bp) contained a consensus TATA box and putative transcription factor binding sites, including two half EREs, an SF-1, an AhR/Arnt, a PR and two GATA-3s. The muCyp19b promoter sequence (2500 bp) showed consensus TATA and CCAAT boxes and putative transcription binding sites, namely: a PR, an ERE, a half ERE, a SP-1, two GATA-binding factor, one half GATA-1, two C/EBPs, a GRE, a NFkappaB, three STATs, a PPAR/RXR, an Ahr/Arnt and a CRE. Basal activity of serially deleted promoter constructs transiently transfected into COS-7, [alpha]T3 and TE671 cells demonstrated the enhancing and silencing roles of the putative transcription factor binding sites. Quinpirole, a dopamine agonist, significantly reduced the promoter activity of muCyp19b in TE671. The results suggest tissue-specific regulation of the muCyp19 genes and a putative alternative promoter for muCyp19b.

A digital imaging technique to quantify the incidence of internal browning in potato (Solanum tuberosum) tubers

Internal browning disorders, including brown fleck (BF), in potato (Solanum tuberosum) tubers greatly reduce tuber quality, but the causes are not well understood. This is due, in part, to the highly variable data provided by visual value-based rating systems. A digital imaging technique was developed to quantify accurately the incidence of internal browning in potato tubers. Images of tuber sections were scanned using a flatbed scanner and digitally enhanced to highlight tuber BF lesions, and the area of affected tissue calculated using pixel quantification software. Digital imaging allowed for the determination of previously unused indices of the incidence and severity of internal browning in potato tubers. Statistical analysis of the comparison between digitally derived and visual-rating BF data from a glasshouse experiment showed that digital data greatly improved the delineation of treatment effects. The F-test probability was further improved through square root or logarithmic data transformations of the digital data, but not of the visual-rating data. Data from a field experiment showed that the area of tuber affected by BF and the number of small BF lesions increased with time and with increase in tuber size. The results from this study indicate that digital imaging of internal browning disorders of potato tubers holds much promise in determining their causes that heretofore have proved elusive.

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Multispectral imaging and genomic mapping

Identifying QTLs linked to malting and protein levels in barley using multispectral image data.