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The production of the first permanent, publicly available sunflower (Helianthus annuus L.) association mapping population (UGA-SAM1) provided material to test the usability of morphological descriptors for discriminating germplasm accessions. The objective of this study was to evaluate the phenotypic diversity in the UGA-SAM1 population and evaluate the usefulness of qualitative traits for the discrimination of genotypes. The SAM1 population consists of 285 accessions characterized for 20 morphological traits. The Shannon–Weaver diversity index (H′) was used to determine phenotypic diversity, whereas HOMALS (homogeneity analysis by alternating least squares) and association statistics were used to determine discriminative power of the descriptors. Phenotypic diversity was moderately high for the traits (0.74). The highest diversity was found in the less developed genotypes followed by non-oil genotypes. Pronounced associations in individual genotype groups and category traits, most notably for the maturity trait in the restorer line (RHA) oil group, resulted in both high association and diversity index values. The association test proved to be a useful addition to HOMALS analysis for determining the trait discriminative power. Adequate selection of traits used in germplasm evaluation can improve the efficiency of breeding programs, whereas the loss of variability could be lowered if diversity focused traits were used including leaf, seed, and certain flower traits, instead of only focusing on yield and quality.

The production of the first permanent, publicly available sunflower (Helianthus annuus L.) association mapping population (UGA-SAM1) provided material to test the usability of morphological descriptors for discriminating germplasm accessions. The objective of this study was to evaluate the phenotypic diversity in the UGA-SAM1 population and evaluate the usefulness of qualitative traits for the discrimination of genotypes. The SAM1 population consists of 285 accessions characterized for 20 morphological traits. The Shannon-Weaver diversity index (H') was used to determine phenotypic diversity, whereas HOMALS (homogeneity analysis by alternating least squares) and association statistics were used to determine discriminative power of the descriptors. Phenotypic diversity was moderately high for the traits (0.74). The highest diversity was found in the less developed genotypes followed by non-oil genotypes. Pronounced associations in individual genotype groups and category traits, most notably for the maturity trait in the restorer line (RHA) oil group, resulted in both high association and diversity index values. The association test proved to be a useful addition to HOMALS analysis for determining the trait discriminative power. Adequate selection of traits used in germplasm evaluation can improve the efficiency of breeding programs, whereas the loss of variability could be lowered if diversity focused traits were used including leaf, seed, and certain flower traits, instead of only focusing on yield and quality.

Estimation of the level of durum wheat germplasm genetic diversity is important for its classification and efficient use in breeding programmes. The aim of this study was to assess genetic diversity of durum wheat genotypes developed at the Institute of Field and Vegetable Crops in Novi Sad, Serbia, with 26 morphological characteristics based on the International Union for Protection of New Varieties of Plants guidelines. The Shannon diversity index was used as an indicator of morphological diversity and it ranged from 0.283 for glaucosity of lower side of the flag leaf blade and density of hairiness of uppermost node of the culm, to 0.950 for the ear colour, with the mean value of 0.616, indicating a medium to high level of morphological diversity. On average, the diversity was higher for traits relating to generative organs than for those associated with vegetative plant organs. The 21 morphological characteristics were sufficient to distinguish unique profiles of all durum wheat genotypes. The estimation of varietal diversity and identification of morphological characteristics with the highest discriminative power were done by multiple correspondence analysis. The traits that contributed the most to the distinction of genotypes were the ear colouration, length of beak of the lower glume, lower glume shape, ear length of awns at tip relative to ear length and colour of awns. Morphological characterization using the traits with the highest discriminative power could be a useful complementary method for durum wheat germplasm classification and diversity analysis., Ocena genetičke raznolikosti germplazme tvrde pšenice je važna za njenu klasifikaciju i efikasno korišćenje u oplemenjivačkim programma. Cilj ovog rada bio je da se proceni genetička raznolikost genotipova tvrde pšenice stvorenih u Institutu za ratarstvo i povrtarstvo u Novom Sadu, sa 26 morfoloških osobina prema uputstvu Međunarodne unije za zaštitu novih biljnih sorti (UPOV). Šenonov indeks diverziteta je korišćen kao indikator morfološke raznolikosti i kretao se od 0,283 za prevlaku na rukavcu zastavičara i dlakavosti vršne internodije, do 0,950 za boju klasa. Prosečna vrednost Šenonovog indeksa od 0,616 ukazuje na srednje visok nivo morfološke raznolikosti. U proseku, raznovrsnost je bila veća za osobine koje se odnose na generativne biljne organe nego za vegetativne. Utvrđeno je da je 21 morfološka osobina bila dovoljna za razlikovanje jedinstvenih profila svih genotipova tvrde pšenice. Procena sortne raznolikosti i identifikacija morfoloških osobina sa najvećim diskriminativnim vrednostima je urađena pomoću višestruke korespodentne analize. Osobine koje su najviše doprinele razlikovanju genotipova su bile: boja klasa, dužina zadnje strane glume, oblik donje glume, odnos dužine osja i klasa, kao i boja osja. Morfološka karakterizacija pomoću osobina sa najvećim diskriminativnim vrednostima može biti korisna dopunska metoda za klasifikaciju i analizu diverziteta germplazme tvrde pšenice.

Understanding genetic diversity of durum wheat germplasm is essential for its identification and efficient use in breeding programs. The goal of this study was to assess genetic diversity of durum wheat cultivars from the Institute of Field and Vegetable Crops, Serbia, with 26 morphological characteristics based on International Union for Protection of New Varieties of Plants guidelines. The Shannon diversity index was used as an indicator of morphological diversity and it varies from 0.283, for glaucosity of lower side of the flag leaf blade and density of hairiness of uppermost node of the culm, to 0.950, for the ear color, with the mean value of 0.616, indicating a medium to high level of morphological diversity. On average, the diversity was higher for traits relating to generative organs than for those associated with vegetative organs. The 21 morphological characteristics were sufficient to distinguish unique profiles of all durum wheat varieties.

This chapter describes a Support Vector Machine (SVM) for default prediction that features evolutionary model selection. It explains that a genetic algorithm was used as an evolutionary algorithm to optimize the SVM parameters and discusses the importance of discriminative power of classification methods in the quality of default prediction. It reviews the support vector methodology in classification, focusing on classical linear and nonlinear classification for linearly separable and nonseparable scenarios. This chapter also evaluates the application of the SVM on the CreditReform database consisting of 20, 000 solvent and 1000 insolvent German companies in the period from 1996 to 2002.

A novel class of ensembles of linear decision rules is introduced which includes majority voting-based ensembles as a particular case. Based on this general framework, new results are given that state the ability of a subclass to discriminate between two infinite subsets A and B in R n, thus generalizing Mazurov's theorem for two finite sets. © 2013 Pleiades Publishing, Ltd.

AIMS/HYPOTHESIS: We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score. METHODS: Participants (n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls (n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test. RESULTS: Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 (p < 1.00 x 10(-20) vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 x 10(-6) vs null model), but lower discriminative power than model 1 (p = 5.92 x 10(-5)). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 x 10(-9)) and was not statistically different from model 1 (p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 (p = 2.30 x 10(-7) vs null model) and smaller than model 1 (p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model (p < 1.0 x 10(-20)) and model 1 (p = 1.32 x 10(-5)); its ROC AUC was 0.626. CONCLUSIONS/INTERPRETATION: Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabete

AIMS/HYPOTHESIS: We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score. METHODS: Participants (n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls (n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test. RESULTS: Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 (p < 1.00 x 10(-20) vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 x 10(-6) vs null model), but lower discriminative power than model 1 (p = 5.92 x 10(-5)). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 x 10(-9)) and was not statistically different from model 1 (p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 (p = 2.30 x 10(-7) vs null model) and smaller than model 1 (p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model (p < 1.0 x 10(-20)) and model 1 (p = 1.32 x 10(-5)); its ROC AUC was 0.626. CONCLUSIONS/INTERPRETATION: Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabete

AIMS/HYPOTHESIS: We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score. METHODS: Participants (n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls (n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test. RESULTS: Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 (p < 1.00 x 10(-20) vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 x 10(-6) vs null model), but lower discriminative power than model 1 (p = 5.92 x 10(-5)). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 x 10(-9)) and was not statistically different from model 1 (p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 (p = 2.30 x 10(-7) vs null model) and smaller than model 1 (p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model (p < 1.0 x 10(-20)) and model 1 (p = 1.32 x 10(-5)); its ROC AUC was 0.626. CONCLUSIONS/INTERPRETATION: Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabete

AIMS/HYPOTHESIS: We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score. METHODS: Participants (n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls (n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test. RESULTS: Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 (p < 1.00 x 10(-20) vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 x 10(-6) vs null model), but lower discriminative power than model 1 (p = 5.92 x 10(-5)). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 x 10(-9)) and was not statistically different from model 1 (p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 (p = 2.30 x 10(-7) vs null model) and smaller than model 1 (p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model (p < 1.0 x 10(-20)) and model 1 (p = 1.32 x 10(-5)); its ROC AUC was 0.626. CONCLUSIONS/INTERPRETATION: Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabete

AIMS/HYPOTHESIS: We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score. METHODS: Participants (n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls (n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test. RESULTS: Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 (p < 1.00 x 10(-20) vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 x 10(-6) vs null model), but lower discriminative power than model 1 (p = 5.92 x 10(-5)). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 x 10(-9)) and was not statistically different from model 1 (p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 (p = 2.30 x 10(-7) vs null model) and smaller than model 1 (p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model (p < 1.0 x 10(-20)) and model 1 (p = 1.32 x 10(-5)); its ROC AUC was 0.626. CONCLUSIONS/INTERPRETATION: Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabete

The PAMELA experiment is a satellitebased apparatus launched in June 2006. Its core instrument is a magnetic spectrometer, whose high spatial resolution (∼ 3 micron) provides the discriminative power to separate particles and antiparticles. It can measure the momentum and the energy-loss rate of an incident particles, thus allowing to identify higher charges (up to Z ≃ 5). The main goal for PAMELA is a precise measurement of the light antimatter component in cosmic rays (antiprotons, positrons), with unprecedented statistics and over a largely unexplored energy range. The instrument characteristics and the large statistics allow to precisely measure absolute fluxes for various cosmic-ray species up to high energy. Here the He flux analysis above some GeV is presented., QC 20150421