The I subunit of magnesium-chelatase (CHLI) is encoded by two genes in Arabidopsis (Arabidopsis thaliana), CHLI1 and CHLI2. Conflicting results have been reported concerning the functions of the two proteins. We show here that the chli1/chli1 chli2/chli2 double knockout mutant was albino. Comparison with the pale-green phenotype of a chli1/chli1 single knockout mutant indicates that CHLI2 could support some chlorophyll biosynthesis in the complete absence of CHLI1. Real-time quantitative reverse transcription-polymerase chain reaction showed that CHLI2 was expressed at a much lower level than CHLI1. The chli1/chli1 chli2/chli2 double mutant could be fully rescued by expressing a transgene of CHLI2 driven by the CHLI1 promoter. These results suggest that differences between CHLI1 and CHLI2 lie mostly in their expression levels. Furthermore, both the chli1/chli1 and chli2/chli2 single knockout mutants had lower survival rates during de-etiolation than the wild type, suggesting that both genes are required for optimal growth during de-etiolation. In addition, we show that a semidominant chli1 mutant allele and the chli1/chli1 chli2/chli2 double mutant accumulated Lhcb1 transcripts when treated with the herbicide norflurazon, indicating that knocking out the CHLI activity causes the genome-uncoupled phenotype.

Based on this study, it is proposed that IbMADS1 is an important integrator at the initiation of tuberization. As a result, the initiation and development of tuberous roots seems to be well regulated by a network involving a MADS-box gene in which such hormones as jasmonic acid and cytokinins may act as trigger factors.

ABSTRACT. Spatial distribution of greenhouse gases (GHGs) concentration in the atmosphere is important in determining the atmosphere's radioactive absorbtion and global warming. Reducing uncertainties in understanding the spatial distribution of GHGs concentration in the atmosphere have particular meaning in climate modeling and projection of future climate scenarios. In this study, the vertical distribution of GHGs concentration in the atmosphere is deduced and the relevant uncertainty is analyzed by a fuzzy set method. This method was applied in a case study to examine the vertical distribution of CO 2 concentration in the atmosphere. Results indicate that uncertainties in projection of GHGs emissions and global surface temperature have played important roles on vertical distribution of CO 2 concentration in the atmosphere. This has particular meaning for study of relation between CO 2 distribution and global warming.

In the diagnosis of hepatic diseases, “Contrast-Enhanced Computerized Tomography” (CECT) and “Non-Contrast CT” (NCT) are usually simultaneously adopted. In this paper, a system consisting of a fuzzy diagnosis engine and a feature quantizer, which extracts hepatic features from CECT and NCT images, is proposed for assisting hepatic disease diagnosis. Compared with existing methods, this paper differs in two folds. First, a more complete feature set composed of not only lesion textures, but also lesion morphological structure and lesion contrast to normal tissues is used. These features are described through mathematical models built inside the feature quantizer and served as the input of fuzzy diagnosis engine. Second, because of the use of the fuzzy diagnosis engine, the system is intrinsically with the capability of storing rules and may infer and adapt its rules according to learning data. Furthermore, uncertainty associated with disease diagnosis can be appropriately taken into considerations. The system has been tested using 131 sets of image data, which are to be classified into 4 types of diseases: liver cyst, hepatoma, cavernous hemagioma and metastatic liver tumor. Experimental results indicate that among these test data 78% of them are accurately classified as one type, while the remaining 22% of data are classified as more than one types of diseases. Even so, within these 22% of multiple-classified data, the correct type is always included in the output in each test, showing a promise of the system.