Functional characterization of two plant type I MADS-box genes in Arabidopsis thaliana : AGL40 and AGL62 : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Plant Biology at Massey University, Palmerston North, New Zealand

MADS-box transcription factors (TF) are a family of evolutionary conserved genes found across various eukaryotic species. Characterized by the conserved DNA binding MADS-box domain. MADS-box TF has been shown to play various roles in developmental processes. MADS-box genes can be based on MADS-box structural motifs divided into type I and type II lineages. In plants very limited functional characterization have been achieved with type I genes MADS-box genes. In this project we attempted to functionally characterize 2 closely related members of the type I lineage MADS-box genes AGL40 and AGL62 and give further support to the hypothesis that plant type I MADS-box genes are also crucial to normal plant development. Based on our expression domain characterization assay using AGL62: GUS fusion construct, we have shown expression of AGL62 in various tissues but especially strong in developing seeds, pollen and seedling roots and shoots. The web based microarray data suggesting that AGL62 may have a function in seed, pollen and seedling development backed up this result. Interestingly when we carried out PCR based genotyping with segregating population of heterozygous AGL62 T-DNA insertion lines (agl62/+) to identify the homozygous T-DNA insertion lines we detected no homozygous T-DNA insertion line indicating loss-of-function of AGL62 may be lethal to plant. With reference to the AGL62 expression in pollen, seed and seedling root and shoot, we carried out phenotypic assay on each of these tissues in agl62/+ background to investigate whether there was any phenotypic defect observed. Significant reduction in number of seeds was observed in agl62/+ indicating possible role of AGL62 in seed development. Our microscopic observation of seeds from agl62/+ plants showed defective embryos and confirmed that AGL62 plays a role in seed development. Our data on AGL62 is the first report that confirms AGL62's involvement in plant development and can be a ground work for further works on functional characterization of other members of plant type I MADS-box genes

The kinetic mechanism of bacterial ribosome recycling.

Bacterial ribosome recycling requires breakdown of the post-termination complex (PoTC), comprising a messenger RNA (mRNA) and an uncharged transfer RNA (tRNA) cognate to the terminal mRNA codon bound to the 70S ribosome. The translation factors, elongation factor G and ribosome recycling factor, are known to be required for recycling, but there is controversy concerning whether these factors act primarily to effect the release of mRNA and tRNA from the ribosome, with the splitting of the ribosome into subunits being somewhat dispensable, or whether their main function is to catalyze the splitting reaction, which necessarily precedes mRNA and tRNA release. Here, we utilize three assays directly measuring the rates of mRNA and tRNA release and of ribosome splitting in several model PoTCs. Our results largely reconcile these previously held views. We demonstrate that, in the absence of an upstream Shine-Dalgarno (SD) sequence, PoTC breakdown proceeds in the order: mRNA release followed by tRNA release and then by 70S splitting. By contrast, in the presence of an SD sequence all three processes proceed with identical apparent rates, with the splitting step likely being rate-determining. Our results are consistent with ribosome profiling results demonstrating the influence of upstream SD-like sequences on ribosome occupancy at or just before the mRNA stop codon

Incremental Skip-gram Model with Negative Sampling

This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives. Existing methods of neural word embeddings, including SGNS, are multi-pass algorithms and thus cannot perform incremental model update. To address this problem, we present a simple incremental extension of SGNS and provide a thorough theoretical analysis to demonstrate its validity. Empirical experiments demonstrated the correctness of the theoretical analysis as well as the practical usefulness of the incremental algorithm