A saturated consensus linkage map of Picea abies including AFLP, SSR, STS, 5S rDNA and morphological markers

International audienc

transML: A Family of Languages to Model Model Transformations

Proceedings of: 13th International Conference on Model Driven Engineering Languages and Systems, MODELS 2010, Oslo, Norway, October 3-8, 2010Model transformation is one of the pillars of Model-Driven Engineering (MDE). The increasing complexity of systems and modelling languages has dramatically raised the complexity and size of model transformations. Even though many transformation languages and tools have been proposed in the last few years, most of them are directed to the implementation phase of transformation development. However, there is a lack of cohesive support for the other phases of the transformation development, like requirements, analysis, design and testing. In this paper, we propose a unified family of languages to cover the life-cycle of transformation development. Moreover, following an MDE approach, we provide tools to partially automate the progressive refinement of models between the different phases and the generation of code for specific transformation implementation languages.Work funded by the Spanish Ministry of Science (project TIN2008-02081 and grants JC2009-00015,PR2009-0019), the R&Dprogramme of the Madrid Region (project S2009/TIC-1650), and the European Commission’s 7th Framework programme (grants #218575 (INESS), #248864 (MADES))

Evaluación de la composición y calidad del color de vinos tintos Tannat elaborados por maceración pre-fermentativa en caliente

Durante la vendimia 2016 se elaboraron vinos tintos Tannat por maceración pre-fermentativa en caliente (una hora de calentamiento a 60–70 ∘C seguido de una maceración fermentativa de 7 días) y maceración tradicional (maceración fermentativa de 7 días) con el objetivo de evaluar su concentración en compuestos fenólicos y color durante la conservación. Las vinificaciones se realizaron por triplicado en recipientes de 10 litros de capacidad. Se determinó la composición básica del vino, color, concentración de fenoles totales, antocianos, catequinas, proantocianidinas y los índices de ionización, copigmentados y PVPP. Los vinos con maceración pre-fermentativa en caliente presentaron mayor concentración de antocianos y taninos, mayor porcentaje de antocianos ionizados, co-pigmentados y condensados. Adicionalmente, presentaron mayor proporcion de delfinidina, petunidina y peonidina al descube. El calentamiento degrada las enzimas polifenoloxidasas por lo que estas formas antociánicas pudieron haber sido preservadas de la oxidación. Las concentraciones de las diferentes formas antociánicas disminuyeron durante la conservación de los vinos de ambos tratamientos, sin observarse diferencias en el perfil antocianico después de dos años. Los vinos elaborados por maceración pre-fermentativa en caliente presentaron mayor intensidad colorante durante todo el período de evaluación sugiriendo una mayor estabilidad del color

A consistent, scalable model for Eulerian spray modeling

Despite great practical interest in how sprays emanate from fuel injectors, the near-nozzle region has remained a challenge for spray modelers. Recently, Eulerian models have shown promise in capturing the fast gas-liquid interactions in the near field. However, with the inclusion of compressibility, it can be difficult to maintain consistency between the hydrodynamic and thermodynamic variables. In order to resolve numerical inconsistencies that occur in segregated solutions of Eulerian spray model equations as well as to provide good scalability and stability, a new construction of a -Y model is introduced. This construction is built around an IMEX-RK3 algorithm which offers accuracy and efficiency. The new algorithm is compared to an existing implementation for speed and is validated against experimental measurements of spray evolution in order to test the accuracy. The predictions of the new construction are slightly more accurate and, when tested on 256 processors, are 34 times faster.Also this research used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. The authors gratefully acknowledge the computing resources provided on the Texas Advanced Computing Center (TACC) at The University of Texas at Austin that have contributed to the research results reported within this paper URL: http://www.tacc.utexas.edu.Pandal-Blanco, A.; Pastor Enguídanos, JM.; García Oliver, JM.; Baldwin, E.; Schmidt, D. (2016). A consistent, scalable model for Eulerian spray modeling. International Journal of Multiphase Flow. 83:162-171. doi:10.1016/j.ijmultiphaseflow.2016.04.003S1621718

Predominant Role of Nuclear Versus Membrane Estrogen Receptor α in Arterial Protection: Implications for Estrogen Receptor α Modulation in Cardiovascular Prevention/Safety

BACKGROUND: Although estrogen receptor α (ERα) acts primarily as a transcription factor, it can also elicit membrane-initiated steroid signaling. Pharmacological tools and transgenic mouse models previously highlighted the key role of ERα membrane-initiated steroid signaling in 2 actions of estrogens in the endothelium: increase in NO production and acceleration of reendothelialization. METHODS AND RESULTS: Using mice with ERα mutated at cysteine 451 (ERaC451A), recognized as the key palmitoylation site required for ERα plasma membrane location, and mice with disruption of nuclear actions because of inactivation of activation function 2 (ERaAF20 = ERaAF2°), we sought to fully characterize the respective roles of nuclear membrane-initiated steroid signaling in the arterial protection conferred by ERα. ERaC451A mice were fully responsive to estrogens to prevent atheroma and angiotensin II-induced hypertension as well as to allow flow-mediated arteriolar remodeling. By contrast, ERαAF20 mice were unresponsive to estrogens for these beneficial vascular effects. Accordingly, selective activation of nuclear ERα with estetrol was able to prevent hypertension and to restore flow-mediated arteriolar remodeling. CONCLUSIONS: Altogether, these results reveal an unexpected prominent role of nuclear ERα in the vasculoprotective action of estrogens with major implications in medicine, particularly for selective nuclear ERα agonist, such as estetrol, which is currently under development as a new oral contraceptive and for hormone replacement therapy in menopausal women

Sources of mycosporine-like amino acids in planktonic Chlorella-bearing ciliates (Ciliophora)

Mycosporine-like amino acids (MAAs) are a family of secondary metabolites known to protect organisms exposed to solar UV radiation. We tested their distribution among several planktonic ciliates bearing Chlorella isolated from an oligo-mesotrophic lake in Tyrol, Austria. In order to test the origin of these compounds, the MAAs were assessed by high performance liquid chromatography in both the ciliates and their symbiotic algae.Considering all Chlorella-bearing ciliates, we found: (i) seven different MAAs (mycosporine-glycine, palythine, asterina-330, shinorine, porphyra-334, usujirene, palythene); (ii) one to several MAAs per species and (iii) qualitative and quantitative seasonal changes in the MAAs (e.g. in Pelagodileptus trachelioides). In all species tested, concentrations of MAAs were always <1% of ciliate dry weight.Several MAAs were also identified in the Chlorella isolated from the ciliates, thus providing initial evidence for their symbiotic origin. In Uroleptus sp., however, we found evidence for a dietary source of MAAs.Our results suggest that accumulation of MAAs in Chlorella-bearing ciliates represents an additional benefit of this symbiosis and an adaptation for survival in sunlit, UV-exposed waters

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA