Population structure, phenotypic information and association studies in long-generation crops

Poster presented at Generation Challenge Program Annual Research Meeting. Sao Paulo (Brazil), 12-16 Sep. 200

Gene flow risk assessment in centres of crop origin and diversity

Poster presented at Plant Biology & Botany Join Congress. Chicago (USA), 7-11 Jul 200

Solving random homogeneous linear second-order differential equations: a full probabilistic description

[EN] In this paper a complete probabilistic description for the solution of random homogeneous linear second-order differential equations via the computation of its two first probability density functions is given. As a consequence, all unidimensional and two-dimensional statistical moments can be straightforwardly determined, in particular, mean, variance and covariance functions, as well as the first-order conditional law. With the aim of providing more generality, in a first step, all involved input parameters are assumed to be statistically dependent random variables having an arbitrary joint probability density function. Second, the particular case that just initial conditions are random variables is also analysed. Both problems have common and distinctive feature which are highlighted in our analysis. The study is based on random variable transformation method. As a consequence of our study, the well-known deterministic results are nicely generalized. Several illustrative examples are included.This work has been partially supported by the Spanish M. C. Y. T. Grant MTM2013-41765-P.Casabán, M.; Cortés, J.; Romero, J.; Roselló, M. (2016). Solving random homogeneous linear second-order differential equations: a full probabilistic description. Mediterranean Journal of Mathematics. 13(6):3817-3836. https://doi.org/10.1007/s00009-016-0716-6S38173836136Øksendal B.: Stochastic Differential Equations: An Introduction with Applications, 6th edn. Springer, Berlin (2007)Soong T.T.: Random Differential Equations in Science and Engineering. Academic Press, New York (1973)Neckel, T., Rupp, F.: Random Differential Equations in Scientific Computing. Versita, London (2013)Nouri, K., Ranjbar, H.: Mean square convergence of the numerical solution of random differential equations. Mediterr. J. Math. 1–18 (2014). doi: 10.1007/s00009-014-0452-8Villafuerte, L., Chen-Charpentier, B.M.: A random differential transform method: theory and applications. Appl. Math. Lett. 25(10), 1490–1494 (2012). doi: 10.1016/j.aml.2011.12.033Licea, J.A., Villafuerte, L., Chen-Charpentier, B.M.: Analytic and numerical solutions of a Riccati differential equation with random coefficients. J. Comput. Appl. Math. 239, 208–219 (2013). doi: 10.1016/j.cam.2012.09.040Casabán, M.C., Cortés, J.C., Romero, J.V., Roselló, M.D.: Probabilistic solution of random homogeneous linear second-order difference equations. Appl. Math. Lett. 34, 27–32 (2014). doi: 10.1016/j.aml.2014.03.010Santos, L.T., Dorini, F.A., Cunha, M.C.C.: The probability density function to the random linear transport equation. Appl. Math. Comput. 216 (5), 1524–1530 (2010). doi: 10.16/j.amc.2010.03.001El-Tawil, M., El-Tahan, W., Hussein, A.: Using FEM-RVT technique for solving a randomly excited ordinary differential equation with a random operator. Appl. Math. Comput. 187(2), 856–867 (2007). doi: 10.1016/j.amc.2006.08.164Hussein, A., Selim, M.M.: Solution of the stochastic radiative transfer equation with Rayleigh scattering using RVT technique. Appl. Math. Comput. 218(13), 7193–7203 (2012). doi: 10.1016/j.amc.2011.12.088Casabán, M.C., Cortés, J.C., Romero, J.V., Roselló, M.D.: Probabilistic solution of random SI-type epidemiological models using the random variable transformation technique. Commun. Nonlinear Sci. Numer. Simul. 24(1–3), 86–97 (2015). doi: 10.1016/j.cnsns.2014.12.016Casabán, M.C., Cortés, J.C., Romero, J.V., Roselló, M.D.: Determining the first probability density function of linear random initial value problems by the random variable transformation (RVT) technique: a comprehensive study. In: Abstract and Applied Analysis 2014-ID248512, pp. 1–25 (2014). doi: 10.1155/2013/248512Casabán, M.C., Cortés, J.C., Navarro-Quiles, A., Romero, J.V., Roselló, M.D., Villanueva, R.J.: A comprehensive probabilistic solution of random SIS-type epidemiological models using the random variable transformation technique. Commun. Nonlinear Sci. Numer. Simul. 32, 199–210 (2016). doi: 10.1016/j.cnsns.2015.08.009El-Wakil, S.A., Sallah, M., El-Hanbaly, A.M.: Random variable transformation for generalized stochastic radiative transfer in finite participating slab media. Phys. A 435 66–79 (2015). doi: 10.1016/j.physa.2015.04.033Dorini, F.A., Cunha, M.C.C.: On the linear advection equation subject to random fields velocity. Math. Comput. Simul. 82, 679–690 (2011). doi: 10.16/j.matcom.2011.10.008Dhople, S.V., Domínguez-García, D.: A parametric uncertainty analysis method for Markov reliability and reward models. IEEE Trans. Reliab. 61(3), 634–648 (2012). doi: 10.1109/TR.2012.2208299Williams, M.M.R.: Polynomial chaos functions and stochastic differential equations. Ann. Nucl. Energy 33(9), 774–785 (2006). doi: 10.1016/j.anucene.2006.04.005Chen-Charpentier, B.M., Stanescu, D.: Epidemic models with random coefficients. Math. Comput. Model. 52(7/8), 1004–1010 (2009). doi: 10.1016/j.mcm.2010.01.014Papoulis A.: Probability, Random Variables and Stochastic Processes. McGraw-Hill, New York (1991

Descriptors for genetic markers technologies

This document is targeted to researchers using genetic marker technologies to generate and exchange genetic marker data that are standardized and replicable. This initial proposed set of descriptors was reviewed widely by international experts from national research institutions, universities and CGIAR centres, and their comments and contributions were included through several iterations of the document. This first official version of the list is now being published by IPGRI to encourage application of the descriptors to current research projects and to stimulate further refinement of the standards

Molecular markers for genebank management

In the last decade, the use of DNA markers for the study of crop genetic diversity has become routine, and has revolutionized biology. Increasingly, techniques are being developed to more precisely, quickly and cheaply assess genetic variation. These techniques have changed the standard equipment of many labs, and most germplasm scientists are expected to be trained in DNA data generation and interpretation. The rapid growth of new techniques has stimulated this update of IPGRI's Technical Bulletin No. 2, ”Molecular tools in plant genetic resources conservation: a guide to the technologies” (Karp et al. 1997b). Our goal is to update DNA techniques from this publication, to show examples of their applications, and to guide genebank researchers towards ways to maximize their use. This bulletin reviews basic qualities of molecular markers, their characteristics, the advantages and disadvantages of their applications, and analytical techniques, and provides some examples of their use.There is no single molecular approach for many of the problems facing genebank managers, and many techniques complement each other. However, some techniques are clearly more appropriate than others for some specific applications. In an ideal situation, the most appropriate marker(s) can be chosen irrespective of time or funding constraints, but in other cases the choice of marker(s) will depend on constraints of equipment or funds. The purpose of this publication is to explain the characteristics of different markers and guide to their use through a number of real examples that represent well informed choices. What is most important is to choose a marker that can appropriately address well-defined questions through good experimental design, ideally leading to peer-reviewed scientific publications. Experimental design has many definitions depending on the type of question being asked and on the field of science addressed. We use the term here in a very general way to cover all aspects of planning an experiment, including a clear definition of the question being addressed; knowledge of prior studies addressing the question; proper choice of molecular markers and of data used to address the question; knowledge of the characteristics, strengths and weaknesses of the data; sources of unexpected variation in the data; how much data are needed; proper methods to analyze the data; and limits to conclusions you can make from the results. One of the most important considerations before beginning any experiment is to address proper experimental design. Improper experimental design can make the work inconclusive, misleading, insignificant, and most likely unpublishable. Similarly, improvements in experimental design can change an uninspired study to a highly significant one with little to no increase in time and funds. Poor experimental design can waste significant resources and damage the reputation and impact of your genebank. It is beyond the scope of any publication to outline all possible pitfalls that can lead to poorly designed experiments, analyses or conclusions, and different considerations of proper experimental design need to be made in particular fields. This technical bulletin outlines some basic considerations regarding molecular marker types and analyses to lead the reader. There is no substitute, however, for basic knowledge of the biological questions being addressed, knowledge of the taxonomic group under consideration and a thorough literature review to ensure that similar work has not been done before. If limitations of any type hinder genebank and germplasm managers with regards to these factors, collaboration or consultation with experts is well worth the effort. Excellent reviews of methodology and data interpretation are presented in Weising et al. (1995), Hillis et al. (1996), Staub et al. (1996), Hillis (1997), Karp et al. (1997a,b) and Avise (2004). Hamrick and Godt (1997) present a review of isozyme data; Doebley (1992), Clegg (1993b) and Spooner and Lara-Cabrera (2001) present a review of molecular data for plant genetic resources and crop evolution; Bruford and Wayne (1993), Wang et al. (1994), Gupta et al. (1996), Powell et al. (1996a) and Weising et al. (1998) of microsatellite data; Wolfe and Liston (1998) on Polymerase Chain Reaction (PCR) related data. Schlötterer (2004) reviews the history and relative utility of different molecular marker types. Sytsma and Hahn (1997) present reviews of molecular studies in crop and non-crop plants. Some information from Spooner and Lara-Cabrera (2001) for crop diversity studies was used and updated; Spooner et al. (2003) was used for taxonomy studies. An overview of the main marker techniques and their comparative qualities is presented in the section titled, ”Overview of molecular technologies”. Applications of molecular techniques in genebank management and crop breeding are the subject of the following sections. The section titled, ”Future challenges” focuses on the current developments in molecular marker applications and future challenges that could result from these developments. Elements of experimental design are discussed throughout and some basic aspects of data analysis are discussed in ”Genebank management”

Measurements of high-energy neutron-induced fission of natPb and 209Bi

EFNUDAT – Measurements and Models of Nuclear ReactionsThe CERN Neutron Time-Of-Flight (n_TOF) facility is well suited to measure low cross sections as those of neutron-induced fission in subactinides. The cross section ratios of natPb and 209Bi relative to 235U and 238U were measured using PPAC detectors and a fragment coincidence method that allows us to identify the fission events. The present experiment provides first results for neutron-induced fission up to 1 GeV. Good agreement is found with previous experimental data below 200 MeV. The comparison with proton-induced fission indicates that the limiting regime where neutron-induced and proton-induced fission reach equal cross sections is close to 1 Ge

A comprehensive probabilistic solution of random SIS-type epidemiological models using the Random Variable Transformation technique

[EN] This paper provides a complete probabilistic description of SIS-type epidemiological models where all the input parameters (contagion rate, recovery rate and initial conditions) are assumed to be random variables. By applying the Random Variable Transformation technique, the first probability density function, the mean and the variance functions as well as confidence intervals associated to the solution of SIS-type epidemiological models are determined under the general hypothesis that the random inputs have any joint probability density function. The distributions to describe the time until a given proportion of the population remains susceptible and infected are also determined. Lastly, a probabilistic description of the so-called basic reproductive number is included. The theoretical results are applied to an illustrative example showing good fitting.This work has been partially supported by the Ministerio de Economia y Competitividad grants MTM2013-41765-P and TRA2012-36932. Ana Navarro Quiles acknowledges the doctorate scholarship granted by Programa de Ayudas de Investigacion y Desarrollo (PAID), Universitat Politecnica de Valencia.Casabán, M.; Cortés, J.; Navarro-Quiles, A.; Romero, J.; Roselló, M.; Villanueva Micó, RJ. (2016). A comprehensive probabilistic solution of random SIS-type epidemiological models using the Random Variable Transformation technique. Communications in Nonlinear Science and Numerical Simulation. 32:199-210. https://doi.org/10.1016/j.cnsns.2015.08.009S1992103

Twelve years of daily weather descriptions in North America in the eighteenth century (Mexico City, 1775-86)

© 2019 American Meteorological Society. The authors are very grateful to Ana Gavilán and César Paradinas for their help with the transcription of the FZO weather diary. Carlos Ordóñez reviewed the language. This work was supported by the research projects IMDROFLOOD financed by the Water Works 2014 cofunded call of the European Commission and INDECIS, which is part of ERA4CS, an ERA-NET initiated by JPI Climate by the European Union (Grant 690462). Marina Peña-Gallardo was granted by the Spanish Ministry of Economy and Competitiveness (MINECO), and Ahmed El Kenawy was supported by a postdoctoral Juan de la Cierva contract by the Spanish Ministry of Economy and Competitiveness (MINECO). F. Domínguez-Castro, M. C. Gallego, J. M. Vaquero, R. García Herrera, M. Peña-Gallardo, A. El Kenawy, and S. M. Vicente-SerranoDepto. de Física de la Tierra y AstrofísicaFac. de Ciencias FísicasTRUEUnión Europea. H2020Ministerio de Economía y Competitividad (MINECO)JPI Climate by the European Unionpu