Hierarchical Back-off Modeling of Hiero Grammar based on Non-parametric Bayesian Model

In hierarchical phrase-based machine translation, a rule table is automatically learned by heuristically extracting syn-chronous rules from a parallel corpus. As a result, spuriously many rules are extracted which may be composed of various incorrect rules. The larger rule table incurs more run time for decoding and may result in lower translation quality. To resolve the problems, we propose a hierarchical back-off model for Hiero grammar, an instance of a synchronous context free grammar (SCFG), on the basis of the hierarchical Pitman-Yor process. The model can extract a compact rule and phrase table without resorting to any heuristics by hierarchically backing off to smaller phrases under SCFG. Inference is efficiently carried out using two-step synchronous parsing of Xiao et al., (2012) combined with slice sampling. In our experiments, the proposed model achieved higher or at least comparable translation quality against a previous Bayesian model on various language pairs; German/French/Spanish/Japanese-English. When compared against heuristic models, our model achieved comparable translation quality on a full size German-English language pair in Europarl v7 corpus with significantly smaller grammar size; less than 10 % of that for heuristic model.

Hierarchical Back-off Modeling of Hiero Grammar based on Non-parametric Bayesian Model

In hierarchical phrase-based machine translation, a rule table is automatically learned by heuristically extracting syn-chronous rules from a parallel corpus. As a result, spuriously many rules are extracted which may be composed of various incorrect rules. The larger rule table incurs more run time for decoding and may result in lower translation quality. To resolve the problems, we propose a hierarchical back-off model for Hiero grammar, an instance of a synchronous context free grammar (SCFG), on the basis of the hierarchical Pitman-Yor process. The model can extract a compact rule and phrase table without resorting to any heuristics by hierarchically backing off to smaller phrases under SCFG. Inference is efficiently carried out using two-step synchronous parsing of Xiao et al., (2012) combined with slice sampling. In our experiments, the proposed model achieved higher or at least comparable translation quality against a previous Bayesian model on various language pairs; German/French/Spanish/Japanese-English. When compared against heuristic models, our model achieved comparable translation quality on a full size German-English language pair in Europarl v7 corpus with significantly smaller grammar size; less than 10 % of that for heuristic model.

Photocrosslinkable Artificial Nucleic Acid Probe Based miRNA Biosensor

Molecular recognition elements like enzymes, antibodies, and nucleic acids, which are involved in specific binding, are important components in biosensing technologies. These biomolecular recognition elements are based on molecular interactions such as hydrogen bonding, van der Waals forces, and hydrophobic interactions. However, these interactions are often affected by the solution environment such as pH, temperature, and salt concentration, which are the rate-limiting factors for biosensing applications. In this study, we focused on molecular recognition using photocrosslinkable artificial nucleic acids. Photocrosslinkable artificial nucleic acids can form covalent bonds with target nucleic acids upon photoirradiation after hybridization. The covalent bonds formed are stronger than those in conventional molecular recognition and are not affected by the solution environment. Herein, we propose a biosensing system that combines molecular recognition by photocrosslinkable artificial nucleic acids, isothermal amplification by hybridization chain reaction, and electrochemical detection of miR-21 as the target molecule, which has recently attracted attention as a cancer biomarker. This technology eliminates non-specific binding and enables biosensing measurements with a suppressed background

Construction of Electrochemical Biosensor Using Thionin-modified Electrode for Detecting Progesterone in Cattle Estrus

In the present study, a progesterone (P4) biosensor was developed to detect cattle estrus. Thionin, which has been reported to oxidize steroid hormones, was immobilized on an electrode via 10-carboxy-1-decanethiol with the aim of continuous measurement of P4 in the cattle body. On the screen-printed electrode, Au nanoparticles were electrodeposited on the surface of the carbon electrode to increase surface area of electrode. Finally, the thionin-modified electrode surface was covered with Nafion™. As a result, the influence of contaminants (BSA, L-ascorbic acid) was avoided. The detection range of the prepared sensor for P4 was 1 nM (= n mol/l)–20 nM. When bovine plasma was used as a biological sample, it was confirmed that the current response in i-t measurement increased due to the addition of P4. The fabricated biosensor was able to detect P4 for 4 days. It is expected that the P4 biosensor used in the present study will enable accurate understanding of cattle estrus