3,850 research outputs found

    Genetic and Molecular Characterization of Temperate and Tropical Forage Maize Inbred Lines

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    The livestock feeding in the Central highland of Mexico is based on harvest, grazing and annual forage conservation, being forage maize the most important silage crop (AlarcĂłn, 1995). Even though forage maize is extensively bred in Europe, USA and Asia since 1900\u27s, this started in Mexico in the 1960\u27s, and little is known about the genetic diversity in both agronomic and nutritive value traits. Our breeding program goals are to analyze combining ability of biomass and quality predictors and to study the genetic relationship of inbred lines between lowland tropical and temperate races from Mesa Central, by genetic and molecular approaches

    Genetic and Molecular Characterization of Temperate and Tropical Forage Maize Inbred Lines

    Get PDF
    Livestock feeding in the Central highland of Mexico is based on harvest, grazing and annual forage conservation, with forage maize being the most important silage crop (Alarcón, 1995). Even though forage maize is extensively bred in Europe, USA and Asia since the 1900’s, this started in Mexico only in the 1960’s, and little is known about genetic diversity in both agronomic and nutritive value traits. Our breeding program goals are to analyze combining ability of biomass and quality predictors and to study the genetic relationship of inbred lines between lowland tropical and temperate races from Mesa Central, by genetic and molecular approaches

    A stable elemental decomposition for dynamic process optimization

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    AbstractIn Cervantes and Biegler (A.I.Ch.E.J. 44 (1998) 1038), we presented a simultaneous nonlinear programming problem (NLP) formulation for the solution of DAE optimization problems. Here, by applying collocation on finite elements, the DAE system is transformed into a nonlinear system. The resulting optimization problem, in which the element placement is fixed, is solved using a reduced space successive quadratic programming (rSQP) algorithm. The space is partitioned into range and null spaces. This partitioning is performed by choosing a pivot sequence for an LU factorization with partial pivoting which allows us to detect unstable modes in the DAE system. The system is stabilized without imposing new boundary conditions. The decomposition of the range space can be performed in a single step by exploiting the overall sparsity of the collocation matrix but not its almost block diagonal structure. In order to solve larger problems a new decomposition approach and a new method for constructing the quadratic programming (QP) subproblem are presented in this work. The decomposition of the collocation matrix is now performed element by element, thus reducing the storage requirements and the computational effort. Under this scheme, the unstable modes are considered in each element and a range-space move is constructed sequentially based on decomposition in each element. This new decomposition improves the efficiency of our previous approach and at the same time preserves its stability. The performance of the algorithm is tested on several examples. Finally, some future directions for research are discussed

    Mapping the incidence of rape reported to law enforcement in New Mexico and the availability of services to survivors.

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    Presented at: 2016 Annual Conference of the New Mexico Public Health Association; April 12-13, 2016; Las Cruces, NM.https://digitalrepository.unm.edu/prc-posters-presentations/1023/thumbnail.jp

    Engaging men for a sexual violence-free nation: a cross-sectional study of U.S.-based organizations.

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    Presented at: American Public Health Association 2016 Annual Meeting & Expo; October 29-November 2, 2016; Denver, CO.https://digitalrepository.unm.edu/prc-posters-presentations/1024/thumbnail.jp

    Non-Minimal Chaotic Inflation, Peccei-Quinn Phase Transition and non-Thermal Leptogenesis

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    We consider a phenomenological extension of the minimal supersymmetric standard model (MSSM) which incorporates non-minimal chaotic inflation, driven by a quadratic potential in conjunction with a linear term in the frame function. Inflation is followed by a Peccei-Quinn phase transition, based on renormalizable superpotential terms, which resolves the strong CP and mu problems of MSSM and provide masses lower than about 10^12 GeV for the right-handed (RH) (s)neutrinos. Baryogenesis occurs via non-thermal leptogenesis, realized by the out-of-equilibrium decay of the RH sneutrinos which are produced by the inflaton's decay. Confronting our scenario with the current observational data on the inflationary observables, the light neutrino masses, the baryon asymmetry of the universe and the gravitino limit on the reheat temperature, we constrain the strength of the gravitational coupling to rather large values (~45-2950) and the Dirac neutrino masses to values between about 1 and 10 GeV.Comment: Final versio

    MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

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    Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections, are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.Comment: 19 pages, 5 figure

    Experimental study of formwork tightness as a function of rheological properties of SCC

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    Several studies relating formwork pressure to rheology exist, however the relationship between rheology and leakage through formwork joints remains to be investigated. In practice, standard documents are used to define formwork tightness requirements, typically using a qualitative approach. To try bridge this gap in knowledge, we developed a test set-up to study tightness of formwork joints under pressure as a function of varying rheological properties. Coupled with standard rheology tests, this new test set-up provides means of linking flow rate, formwork pressure, flow area, and the rheological properties. The study seeks to provide insight on measurable governing parameters and thus inform formwork tightness requirements in a more quantifiable manner. This paper presents a test set-up designed to study the flow of fresh paste through small openings. It highlights a preliminary study on the pressure-driven flow of limestone paste through a bottom orifice in a cylindrical container. While this new device may not be directly representative of the actual conditions in formwork, it provides a good base for a fundamental study that can then be extrapolated to a more representative test operation. Preliminary results show a linear relationship between the flow rate and the applied pressure. The results also show that increasing the flow area by a factor of 2.33 had a higher impact than an increase in yield stress and viscosity by a factor of 2.54 and 3.80 respectively. However, more tests need to be carried out to obtain clear trends

    Quantum scale invariance, cosmological constant and hierarchy problem

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    We construct a class of theories which are scale invariant on quantum level in all orders of perturbation theory. In a subclass of these models scale invariance is spontaneously broken, leading to the existence of a massless dilaton. The applications of these results to the problem of stability of the electroweak scale against quantum corrections, to the cosmological constant problem and to dark energy are discussed.Comment: 6 pages, replaced with journal versio
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