Habitat quality affects the condition of Luciobarbus sclateri in the Guadiamar River (SW Iberian Peninsula): Effects of disturbances by the toxic spill of the Aznalcóllar mine

This study analyzes the somatic condition of southern Iberian barbel Luciobarbus sclateri (Günther, 1868) in the Guadiamar River (SW Iberian Peninsula). This river was seriously affected by a toxic spill of about 4 million cubic meters of acidic water and 2 million cubic meters of mud rich in heavy metals. Once the spill removal works concluded, sites affected and unaffected by the accident were sampled to study its effects on the fish fauna. The ecological variables registered were related to water quality, physical state of reaches, ecological quality, resources exploited by fish, and potential intra-specific interactions. From an initial 15 ecological variables, seasonal water flow and pH explained most of the variation in barbel condition. This study shows that the Guadiamar River, 56 months after the accident, is still undergoing a recovery process where, beyond ecological variables, proximity to the affected area is the most influential factor for fish condition. © 2012 Springer Science+Business Media B.V

Global dataset of soil organic carbon in tidal marshes.

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha-1 in the top 30 cm and 231 ± 134 Mg SOC ha-1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies

Parkinson’s disease mouse models in translational research

Animal models with high predictive power are a prerequisite for translational research. The closer the similarity of a model to Parkinson’s disease (PD), the higher is the predictive value for clinical trials. An ideal PD model should present behavioral signs and pathology that resemble the human disease. The increasing understanding of PD stratification and etiology, however, complicates the choice of adequate animal models for preclinical studies. An ultimate mouse model, relevant to address all PD-related questions, is yet to be developed. However, many of the existing models are useful in answering specific questions. An appropriate model should be chosen after considering both the context of the research and the model properties. This review addresses the validity, strengths, and limitations of current PD mouse models for translational research

Using X-grams for speech-to-speech translation

In this paper, a statistical speech-to-speech translation system, developed at TALP during the last months, is presented. By adapting well-known speech recognition techniques to the specific translation setting, th e system is able to integrate speech sign al into a fin ite state tran sdu cer th at tran slates statistically domain-constrained Span ish sen tences into En glish on es

Finite-state-based and phrase-based statistical machine translation

This paper shows the common framework that underlies the translation systems based on phrases or driven by finite state transducers, and summarizes a first comparison between them. In both approaches the translation process is based on pairs of source and target strings of words (segments) related by word alignment. Their main difference comes from the statistical modeling of the translation context. The experimental study has been carried out on an English/Spanish version of the VERBMOBIL corpus. Under the constrain of a monotone composition of translated segments to generate the target sentence, the finite state based translation outperforms the phrase based counterpart

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