Sunflower Hybrid Breeding: From Markers to Genomic Selection

In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi, or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare. Integrative approaches combining omic technologies (genomics, transcriptomics, proteomics, metabolomics and phenomics) using bioinformatic tools will facilitate the identification of target genes and markers for complex traits and will give a better insight into the mechanisms behind the traits

Intelligibility and Acoustic Characteristics of Clear and Conversational Speech in Telugu (A South Indian Dravidian Language)

The overall goal of this study is to examine the intelligibility differences of clear and conversational speech and also to objectively analyze the acoustic properties contributing to these differences. Seventeen post-lingual stable sensory-neural hearing impaired listeners with an age range of 17–40 years were recruited for the study. Forty Telugu sentences spoken by a female Telugu speaker in both clear and conversational speech styles were used as stimuli for the subjects. Results revealed that mean scores of clear speech were higher (mean = 84.5) when compared to conversational speech (mean = 61.4) with an advantage of 23.1% points. Acoustic properties revealed greater fundamental frequency (f0) and intensity, longer duration, higher consonant–vowel ratio (CVR) and greater temporal energy in clear speech

A reliability index extrapolation method for separable limit states

When the limit state function (or performance function) of a structure can be written as the difference of a capacity function and a response function that are expressed in terms of independent sets of random variables (i.e., when the limit state function has a separable form), efficient simulation based techniques (e.g., Separable Monte Carlo Simulation method) can be used to predict the reliability of the structure. The accuracies of these simulation based techniques, on the other hand, diminishes as the structural reliability increases. This paper proposes a reliability index extrapolation method to predict reliability of a highly safe structure that has a separable limit state function. In this method, the standard deviations of the random variables that contribute to the capacity function are artificially inflated by using a scale parameter to obtain various (smaller) scaled reliability index values (that can be predicted accurately with small number of samples). The standard deviations of the random variables that contribute to the response function are kept unchanged in order to use the same response values in prediction of various scaled reliability indices. Then, least square regression is used to build a relationship between the standard deviation scale parameter and scaled reliability index values. Finally, an extrapolation is performed to estimate the actual (higher) reliability index. The accuracy of the proposed method is evaluated through reliability assessment of mathematical and structural mechanics example problems as well as a reliability based design optimization problem. It is found that the proposed method can provide reasonable accuracy for high reliability index estimations with only 1000 response function evaluations

Speech Style Effects on Local and Non-local Coarticulation in French

International audienc