Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding

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Using an incomplete block design to allocate lines to environments improves sparse genome‐based prediction in plant breeding

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs

Partial Least Squares Enhances Genomic Prediction of New Environments

publishedVersio

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs

Approximate genome-based kernel models for large data sets including main effects and interactions

The rapid development of molecular markers and sequencing technologies has made it possible to use genomic prediction (GP) and selection (GS) in animal and plant breeding. However, when the number of observations (n) is large (thousands or millions), computational difficulties when handling these large genomic kernel relationship matrices (inverting and decomposing) increase exponentially. This problem increases when genomic × environment interaction and multi-trait kernels are included in the model. In this research we propose selecting a small number of lines m(m < n) for constructing an approximate kernel of lower rank than the original and thus exponentially decreasing the required computing time. First, we describe the full genomic method for single environment (FGSE) with a covariance matrix (kernel) including all n lines. Second, we select m lines and approximate the original kernel for the single environment model (APSE). Similarly, but including main effects and G × E, we explain a full genomic method with genotype × environment model (FGGE), and including m lines, we approximated the kernel method with G × E (APGE). We applied the proposed method to two different wheat data sets of different sizes (n) using the standard linear kernel Genomic Best Linear Unbiased Predictor (GBLUP) and also using eigen value decomposition. In both data sets, we compared the prediction performance and computing time for FGSE versus APSE; we also compared FGGE versus APGE. Results showed a competitive prediction performance of the approximated methods with a significant reduction in computing time. Genomic prediction accuracy depends on the decay of the eigenvalues (amount of variance information loss) of the original kernel as well as on the size of the selected lines m.publishedVersio

Clima social familiar y nivel de autoestima de los pacientes con tuberculosis que reciben tratamiento del esquema uno en el Hospital Carlos Lanfranco La Hoz - Puente Piedra

El objetivo de la investigación es “Determinar el nivel de satisfacción de los pacientes hospitalizados con respecto a la calidad del cuidado que brinda el profesional de enfermería y en las Clínicas Maisón de Santé, Lima - 2012” La metodología: Es una investigación aplicada de abordaje cuantitativo, descriptivo, porque pretende relacionar la calidad del cuidado de enfermería y los niveles de satisfacción según opinión de los pacientes hospitalizados en las Clínicas Maisón de Santé. El diseño este estudio es no experimental porque no se va a manipular ninguna de las variables. La población - muestral está conformada por los pacientes hospitalizados de las Clínicas Maisón de Santé provenientes de las tres sedes (Lima, Surco y Chorrillos) de los servicios de medicina y cirugía. La sede Lima está capacitada para poder captar a 30 pacientes por cada servicio del mismo modo la sede de Chorrillos, la sede Surco está capacitada para 24 pacientes de ambos servicios, por lo tanto la población total relativamente está formada por 144. Las resultados obtenidos en el presente trabajo: en base al nivel de satisfacción según la dimensión humano 62% de pacientes manifestaron un nivel alto, como también en la dimensión oportuno del total de encuestados manifestaron un 97% un nivel bajo, por lo consiguiente en la dimensión continuo nos da un 98.9% de satisfacción alta y finalmente en la parte seguro del total de pacientes manifiestan un 62.2 % de satisfacción

Geoda: Distribución de la Celda Unitaria, Composición de los Arrays y Funcionamiento

Nowadays, satellite communications are basic for the human lifestyle. In this way, a smart, conformal and multiarray antenna (GEODA) is being developed in order to receive signals from several satellites simultaneously in the1.7GHz working band. An adaptive beam system is able to follow the signals from the satellite constellation. The complex structure of the antenna is based in similar arrays of triangular shape. These arrays are divided in sub-arrays of three elements called Cells composing the single control element for the arrays main beam direction management. Fifteen cells, forty-five radiating elements, compose each triangular array of the GEODA antenna. In this paper, the working properties and the design of one cell will be shown and discussed

Bayesian multitrait kernel methods improve multienvironment genome-based prediction

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel

Single breath-hold saturation recovery 3D cardiac T1 mapping via compressed SENSE at 3T.

To propose and validate a novel imaging sequence that uses a single breath-hold whole-heart 3D T1 saturation recovery compressed SENSE rapid acquisition (SACORA) at 3T. The proposed sequence combines flexible saturation time sampling, compressed SENSE, and sharing of saturation pulses between two readouts acquired at different RR intervals. The sequence was compared with a 3D saturation recovery single-shot acquisition (SASHA) implementation with phantom and in vivo experiments (pre and post contrast; 7 pigs) and was validated against the reference inversion recovery spin echo (IR-SE) sequence in phantom experiments. Phantom experiments showed that the T1 maps acquired by 3D SACORA and 3D SASHA agree well with IR-SE. In vivo experiments showed that the pre-contrast and post-contrast T1 maps acquired by 3D SACORA are comparable to the corresponding 3D SASHA maps, despite the shorter acquisition time (15s vs. 188s, for a heart rate of 60 bpm). Mean septal pre-contrast T1 was 1453 ± 44 ms with 3D SACORA and 1460 ± 60 ms with 3D SASHA. Mean septal post-contrast T1 was 824 ± 66 ms and 824 ± 60 ms. 3D SACORA acquires 3D T1 maps in 15 heart beats (heart rate, 60 bpm) at 3T. In addition to its short acquisition time, the sequence achieves good T1 estimation precision and accuracy.TFdS has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N722427. CGA is a P-FIS fellow (Instituto deSalud Carlos III). This study was partially supported by the Comunidad de Madrid (S2017/BMD-3867 RENIM-CM) and cofunded with European structural and investment funds. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia, Innovación y Universidades (MCNU) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S

Optical spectroscopic variability of Herbig Ae/Be stars

We analysed 337 multi-epoch optical spectra of 38 Herbig Ae/Be (HAeBe) stars to gain insights into the variability behaviour of the circumstellar (CS) atomic gas. Equivalent widths (EWs) and line fluxes of the Halpha, [OI]6300, HeI5876 and NaID lines were obtained for each spectrum; the Halpha line width at 10% of peak intensity (W10) and profile shapes were also measured and classified. The mean line strengths and relative variabilities were quantified for each star. Simultaneous optical photometry was used to estimate the line fluxes. We present a homogeneous spectroscopic database of HAeBe stars. The lines are variable in practically all stars and timescales, although 30 % of the objects show a constant EW in [OI]6300, which is also the only line that shows no variability on timescales of hours. The HeI5876 and NaID EW relative variabilities are typically the largest, followed by those in [OI]6300 and Halpha. The EW changes can be larger than one order of magnitude for the HeI5876 line, and up to a factor 4 for Halpha. The [OI]6300 and Halpha EW relative variabilities are correlated for most stars in the sample. The Halpha mean EW and W10 are uncorrelated, as are their relative variabilities. The Halpha profile changes in 70 % of the objects. The massive stars in the sample usually show more stable Halpha profiles with blueshifted self-absorptions and less variable 10% widths. Our data suggest multiple causes for the different line variations, but the [OI]6300 and Halpha variability must share a similar origin in many objects. The physical mechanism responsible for the Halpha line broadening does not depend on the amount of emission; unlike in lower-mass stars, physical properties based on the Halpha luminosity and W10 would significantly differ. Our results provide additional support to previous works that reported different physical mechanisms in Herbig Ae and Herbig Be stars.Comment: 10 pages, 5 figures, 2 appendixe