Hedging Against the Interest-rate Risk by Measuring the Yield-curve Movement

By adopting the polynomial interpolation method, we propose an approach to hedge against the interest-rate risk of the default-free bonds by measuring the nonparallel movement of the yield-curve, such as the translation, the rotation and the twist. The empirical analysis shows that our hedging strategies are comparable to traditional duration-convexity strategy, or even better when we have more suitable hedging instruments on hand. The article shows that this strategy is flexible and robust to cope with the interest-rate risk and can help fine-tune a position as time changes.Comment: 12 pages, 2 tables, 5 figure

Translational Regulation of Environmental Adaptation in Bacteria

Bacteria must rapidly respond to both intracellular and environmental changes to survive. One critical mechanism to rapidly detect and adapt to changes in environmental conditions is control of gene expression at the level of protein synthesis. At each of the three major steps of translation—initiation, elongation, and termination—cells use stimuli to tune translation rate and cellular protein concentrations. For example, changes in nutrient concentrations in the cell can lead to translational responses involving mechanisms such as dynamic folding of riboswitches during translation initiation or the synthesis of alarmones, which drastically alter cell physiology. Moreover, the cell can fine-tune the levels of specific protein products using programmed ribosome pausing or inducing frameshifting. Recent studies have improved understanding and revealed greater complexity regarding long-standing paradigms describing key regulatory steps of translation such as start-site selection and the coupling of transcription and translation. In this review, we describe how bacteria regulate their gene expression at the three translational steps and discuss how translation is used to detect and respond to changes in the cellular environment. Finally, we appraise the costs and benefits of regulation at the translational level in bacteria

Generic Algorithm to Predict the Speed of Translational Elongation: Implications for Protein Biogenesis

Synonymous codon usage and variations in the level of isoaccepting tRNAs exert a powerful selective force on translation fidelity. We have developed an algorithm to evaluate the relative rate of translation which allows large-scale comparisons of the non-uniform translation rate on the protein biogenesis. Using the complete genomes of Escherichia coli and Bacillus subtilis we show that stretches of codons pairing to minor tRNAs form putative sites to locally attenuate translation; thereby the tendency is to cluster in near proximity whereas long contiguous stretches of slow-translating triplets are avoided. The presence of slow-translating segments positively correlates with the protein length irrespective of the protein abundance. The slow-translating clusters are predominantly located down-stream of the domain boundaries presumably to fine-tune translational accuracy with the folding fidelity of multidomain proteins. Translation attenuation patterns at highly structurally and functionally conserved domains are preserved across the species suggesting a concerted selective pressure on the codon selection and species-specific tRNA abundance in these regions

The key parameters that govern translation efficiency

Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. Here, we study a stochastic model describing the traffic flow of ribosomes along the mRNA (namely, the inhomogeneous $\ell$-TASEP), and identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes, and efficiency of ribosome usage. By analyzing a continuum limit of the model, we obtain closed-form expressions for stationary currents and ribosomal densities, which agree well with Monte Carlo simulations. Furthermore, we completely characterize the phase transitions in the system, and by applying our theoretical results, we formulate design principles that detail how to tune the key parameters we identified to optimize translation efficiency. Using ribosome profiling data from S. cerevisiae, we shows that its translation system is generally consistent with these principles. Our theoretical results have implications for evolutionary biology, as well as synthetic biology.Comment: To appear in Cell Systems. 32 pages, 10 figures, 1 tabl

Upstream ORFs influence translation efficiency in the parasite Trypanosoma cruzi

It is generally accepted that the presence of ORFs in the 5′ untranslated region of eukaryotic transcripts modulates the production of proteins by controlling the translation initiation rate of the main CDS. In trypanosomatid parasites, which almost exclusively depend on post-transcriptional mechanisms to regulate gene expression, translation has been identified as a key step. However, the mechanisms of control of translation are not fully understood. In the present work, we have annotated the 5′UTRs of the Trypanosoma cruzi genome both in epimastigotes and metacyclic trypomastigotes and, using a stringent classification approach, we identified putative regulatory uORFs in about 9% of the analyzed 5′UTRs. The translation efficiency (TE) and translational levels of transcripts containing putative repressive uORFs were found to be significantly reduced. These findings are supported by the fact that proteomic methods only identify a low number of proteins coded by transcripts containing repressive uORF. We additionally show that AUG is the main translation initiator codon of repressive uORFs in T. cruzi. Interestingly, the decrease in TE is more pronounced when the uORFs overlaps the main CDS. In conclusion, we show that the presence of the uORF and features such as initiation codon and/or location of the uORFs may be acting to fine tune translation levels in these parasites

Improving the objective function in minimum error rate training

In Minimum Error Rate Training (MERT), the parameters of an SMT system are tuned on a certain evaluation metric to improve translation quality. In this paper, we present empirical results in which parameters tuned on one metric (e.g. BLEU) may not lead to optimal scores on the same metric. The score can be improved significantly by tuning on an entirely different metric (e.g. METEOR, by 0.82 BLEU points or 3.38% relative improvement on WMT08 English–French dataset). We analyse the impact of choice of objective function in MERT and further propose three combination strategies of different metrics to reduce the bias of a single metric, and obtain parameters that receive better scores (0.99 BLEU points or 4.08% relative improvement) on evaluation metrics than those tuned on the standalone metric itself

Transverse modes for flat inter-bunch wakes

If inter-bunch wake fields are flat, i.e. their variations over a bunch length can be neglected, all coherent modes have the same coupled-bunch structure, provided the bunches can be treated as identical by their inner qualities (train theorem). If a flat feedback is strong enough, the transverse modes are single-bunch, provided the inter-bunch wakes are also flat (damper theorem).Comment: 2 pages, 1 formula, no figure

A Novel BiLevel Paradigm for Image-to-Image Translation

Image-to-image (I2I) translation is a pixel-level mapping that requires a large number of paired training data and often suffers from the problems of high diversity and strong category bias in image scenes. In order to tackle these problems, we propose a novel BiLevel (BiL) learning paradigm that alternates the learning of two models, respectively at an instance-specific (IS) and a general-purpose (GP) level. In each scene, the IS model learns to maintain the specific scene attributes. It is initialized by the GP model that learns from all the scenes to obtain the generalizable translation knowledge. This GP initialization gives the IS model an efficient starting point, thus enabling its fast adaptation to the new scene with scarce training data. We conduct extensive I2I translation experiments on human face and street view datasets. Quantitative results validate that our approach can significantly boost the performance of classical I2I translation models, such as PG2 and Pix2Pix. Our visualization results show both higher image quality and more appropriate instance-specific details, e.g., the translated image of a person looks more like that person in terms of identity