The status of legal framework for management of fisheries

The success of fisheries management depends very much on the effectiveness of the legal framework in use. A historical perspective of the fisheries of Lake Victoria, Uganda, suggests that fisheries management has not been very successful. This chapter reviews the legal framework with a view to assessing its impact on fisheries management on Lake Victoria, Uganda

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.

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.

Policy as a struggle for meaning: disentangling knowledge translation across international health contexts

Over the last decade, research in medical science has focused on knowledge translation and diffusion of best practices to enable improved health outcomes. However, there has been less attention given to the role of policy in influencing the translation of best practice across different national contexts. This paper argues that the underlying set of public discourses of healthcare policy significantly influences its development with implications for the dissemination of best practices. Our research uses Critical Discourse Analysis to examine the policy discourses surrounding the treatment of stroke across Canada and the U.K. It focuses in specific on how concepts of knowledge translation, user empowerment, and service innovation construct different accounts of the health service in the two countries. These findings provide an important yet overlooked starting point for understanding the role of policy development in knowledge transfer and the translation of science into health practice. © 2011 Operational Research Society. All rights reserved.

N-gram posterior probability confidence measures for statistical machine translation: An empirical study

We report an empirical study of n-gram posterior probability confidence measures for statistical machine translation (SMT). We first describe an efficient and practical algorithm for rapidly computing n-gram posterior probabilities from large translation word lattices. These probabilities are shown to be a good predictor of whether or not the n-gram is found in human reference translations, motivating their use as a confidence measure for SMT. Comprehensive n-gram precision and word coverage measurements are presented for a variety of different language pairs, domains and conditions. We analyze the effect on reference precision of using single or multiple references, and compare the precision of posteriors computed from k-best lists to those computed over the full evidence space of the lattice. We also demonstrate improved confidence by combining multiple lattices in a multi-source translation framework. © 2012 The Author(s).

A Structured Prediction Approach for Statistical Machine Translation

We propose a new formally syntax-based method for statistical machine translation. Transductions between parsing trees are transformed into a problem of sequence tagging, which is then tackled by a search- based structured prediction method. This allows us to automatically acquire transla- tion knowledge from a parallel corpus without the need of complex linguistic parsing. This method can achieve compa- rable results with phrase-based method (like Pharaoh), however, only about ten percent number of translation table is used. Experiments show that the structured pre- diction approach for SMT is promising for its strong ability at combining words.

In vitro selected peptides bind with thymidylate synthase mRNA and inhibit its translation

Thymidylate synthase (TS), an essential enzyme for catalyzing the biosynthesis of thymidylate, is a critical therapeutic target in cancer therapy. Recent studies have shown that TS functions as an RNA-binding protein by interacting with two different sequences on its own mRNA, thus, repressing translational efficiency. In this study, peptides binding TS RNA with high affinity were isolated using mRNA display from a large peptide library (>10(13) different sequences). The randomized library was subjected up to twelve rounds of in vitro selection and amplification. Comparing the amino acid composition of the selected peptides (12th round, R12) with those from the initial random library (round zero, R0), the basic and aromatic residues in the selected peptides were enriched significantly, suggesting that these peptide regions might be important in the peptide-TS mRNA interaction. Categorizing the amino acids at each random position based on their physicochemical properties and comparing the distributions with those of the initial random pool, an obvious basic charge characteristic was found at positions 1, 12, 17 and 18, suggesting that basic side chains participate in RNA binding. Secondary structure prediction showed that the selected peptides of R12 pool represented a helical propensity compared with R0 pool, and the regions were rich in basic residues. The electrophoretic gel mobility shift and in vitro translation assays showed that the peptides selected using mRNA display could bind TS RNA specifically and inhibit the translation of TS mRNA. Our results suggested that the identified peptides could be used as new TS inhibitors and developed to a novel class of anticancer agents.

Global Translation Preferred Active Contour

具有全局平移优先属性的主动轮廓更适于目标跟踪。演化轮廓具有的全局平移优先性可以理解为沿轮廓的速度场具有相等的倾向。根据此思想,通过定义在曲线扰动集合上的新内积空间导出了一种简单,具有平移优先的梯度流。新的内积空间由于是通过向H0主动轮廓对应的內积空间引入曲线扰动的方差获得,所以此主动轮廓称为方差主动轮廓。方差主动轮廓是将H0主动轮廓与其对应的平均梯度流通过加权求和获得,而H1主动轮廓则是通过H0主动轮廓与特定类型的核函数进行卷积得到。因此方差主动轮廓实现时更简单和快速。最后给出了H0,H1和方差主动轮廓在频域与时域的分析。

Recuperação de mata ciliar e reserva legal florestal no noroeste paulista.

2009