Research in development: learning from the CGIAR Research Program on Aquatic Agricultural Systems

This working paper aims to synthesize and share learning from the experience of adapting and operationalizing the Research in Development(RinD) approach to agricultural research in the five hubs under the The CGIAR Research Program on Aquatic Agricultural Systems. It seeks to share learning about how the approach is working in context and to explore the outcomes it is achieving through initial implementation over 3 ½ years. This learning can inform continuation of agricultural research in the second phase of the CGIAR research programs and will be useful to others aiming to implement research programs that seek to equitably build capacity to innovate in complex social-ecological systems. Each of the chapters in this working paper have shown that RinD has produced a range of outcomes that were often unexpected and broader in scope than might result from other approaches to agricultural research. RinD also produces innovations, and there is evidence that it builds capacity to innovate.

The topological configuration and conformational analysis of mRNA in translation

The theoretical model construction of mRNA hairpin structure and single-stranded structure as well as the simulation studies on RNA structure determined by the X-ray crystal diffraction and nuclear magnetic resonance revealed that in translation, after mRNA being unfolded into single-stranded structure, its topological configuration was closely correlative with the original hairpin structure. The conformational features of single-stranded mRNA appeared as helical regions alternating with curly regions to different extents, which might exert the influence on the folding of nascent polypeptide by various regulating effects including different translational rates.

The relation between translation speed and protein secondary structure

Based on the statistical analysis of 119 human and 92 E. coli proteins it was found that for both human and E. coli, the mRNA sequences consisting of tri-codon and tetra-codon with high translation speed preferably code for alpha helices more than for coils. For beta strand, the preference/ avoidance oscillates with the translation speed. Moreover, the non-homogeneous usages of tri-codon and tetra-codon with different translation speeds in a given secondary structure have also been found. These results cannot be simply explained by the effect of stochastic fluctuation.

The identification and quantification of highly stable 'common hairpin' in the dynamic process of co-transcriptional mRNA folding

In our studies, 88 human mRNA samples were collected from the Integrated Sequence-Structure database and then the dynamic process in co-transcriptional mRNA folding was simulated using the RNAstructure version 4.1 program. Through statistical analyses of the frequencies of occurrence of hairpins, a group of special folding structures-the 'common hairpins'-were identified. These 'common hairpins' have lower energies and occur in all the subsequent folding units that formed in the dynamic folding process. By applying the formulas (1)-(4) of the 'common hairpins' statistical model, 163 'common hairpins' were found, to make up about 7% of the total of 2286 hairpins. Classified studies further show that the 'common hairpins' that were studied may oscillate in the dynamic folding process. However, the hairpin loops of the 'common hairpins' and stems proximal to those 'common hairpins' loops maintain topologically stable structures, while other loops and stems distal to the 'common hairpins' loops are shown to be alterable structures. Strikingly, further studies indicate that the stable structures of these 'common hairpins' may have unbeknown effects on controlling the formation of protein structures in the translation process (unpublished results). (c) 2005 Elsevier B.V. All rights reserved.

Improving statistical machine translation by classifying and generalizing inflected verb forms

This paper introduces a rule-based classification of single-word and compound verbs into a statistical machine translation approach. By substituting verb forms by the lemma of their head verb, the data sparseness problem caused by highly-inflected languages can be successfully addressed. On the other hand, the information of seen verb forms can be used to generate new translations for unseen verb forms. Translation results for an English to Spanish task are reported, producing a significant performance improvement.

An ngram-based statistical machine translation decoder

In this paper we describe MARIE, an Ngram-based statistical machine translation decoder. It is implemented using a beam search strategy, with distortion (or reordering) capabilities. The underlying translation model is based on an Ngram approach, extended to introduce reordering at the phrase level. The search graph structure is designed to perform very accurate comparisons, what allows for a high level of pruning, improving the decoder efficiency. We report several techniques for efficiently prune out the search space. The combinatory explosion of the search space derived from the search graph structure is reduced by limiting the number of reorderings a given translation is allowed to perform, and also the maximum distance a word (or a phrase) is allowed to be reordered. We finally report translation accuracy results on three different translation tasks.

Phrase linguistic classification and generalization for improving statistical machine translation

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed. © 2005 Association for Computational Linguistics.