Short and narrow flag leaf1, a GATA zinc finger domain-containing protein, regulates flag leaf size in rice (Oryza sativa).

BACKGROUND: The flag leaf of rice (Oryza sativa L.) is an important determinant of plant type characteristics and grain yield. Identification of flag leaf mutants of rice is crucial to elucidate the molecular mechanism of flag-leaf development, and for exploitation of rice germplasm resources. RESULTS: In this study, we describe a mutant designated short and narrow flag leaf 1 (snfl1). Histological analysis showed that the length of epidermal cells and number of longitudinal veins were decreased in the flag leaf of the snfl1 mutant. Map-based cloning indicated that a member of the GATA family of transcription factors is a candidate gene for SNFL1. A single-nucleotide transition at the last base in the single intron of snfl1 led to variation in alternative splicing and early termination of translation. Complemented transgenic plants harbouring the candidate SNFL1 gene rescued the snfl1 mutant. Analysis of RT-PCR and the SNFL1 promoter by means of a GUS fusion expression assay showed that abundance of SNFL1 transcripts was higher in the culm, leaf sheath, and root. Expression of the SNFL1-GFP fusion protein in rice protoplasts showed that SNFL1 was localized in nucleus. CONCLUSIONS: We conclude that SNFL1 is an important regulator of leaf development, the identification of which might have important implications for future research on GATA transcription factors.

Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network.

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.