BIOBREED – a new project on marker assisted population breeding in wheat with resistance to common bunt

The paper describes the BIOBREED project

Common bunt resistant wheat composite cross populations

Utilising diverse populations instead of single line varieties is expected to lead to a number of advantages in cereal production. These include reduced epidemics of plant diseases, improved weed competition and better exploitation of soil nutrients, resulting in improved yield stability. However, a number of challenges must be met before diverse wheat populations can be introduced into commercial wheat production: one of these is the development of breeding technologies based on mass selection which enable breeders and farmers to improve specific traits in populations and maintain diversity at the same time. BIOBREED is a project started in Denmark in 2011 to meet these challenges for wheat population breeding. The project is focusing on the development of tools and methods for mass selection of traits relevant for organic and low input production, where it is expected that the highest benefits of utilizing diverse populations can be achieved. BIOBREED focuses on three main aspects of wheat population breeding for organic and low input production systems: i) common bunt (caused by Tilletia caries) resistance, ii) selection for improved protein content and iii) the influence on population diversity of different selection pathways. Selection for common bunt resistance in wheat composite cross populations 33 crosses were made between 23 common bunt resistant winter wheat varieties in order to generate two populations. Progeny of all crosses was bulked in the F 3 to constitute the first population Pop.No.Sel. Prior to the creation of the second population Pop.Sel, the F 3 of the parental crosses was sown as head-rows with common bunt infection. Only lines that showed resistance to common bunt were used to create Pop.Sel. in generation F 4 . Afterwards the two populations were grown with and without inoculation with common bunt in order to i) select for bunt resistance and ii) to be able to compare the effect on diversity of this selection step. Preliminary results show a higher level of common bunt resistance in Pop.Sel in the first year. Single seed sorting for protein content Prior to sowing the F 5 seed of the population Pop.Sel, the seed were sorted individually for protein content using a BoMill IQ Grain Quality Sorter 1002S. The fraction of seeds containing the 10% highest and another fraction containing the 10% lowest protein content were selected. The four populations, Pop.No.Sel, Pop.Sel, and Pop.Sel.high. Protein and Pop.Sel.low.Protein and the parental lines were sown in a randomized complete block yield trial at two locations in Denmark in order to assess their yield and quality parameters such as protein content and baking quality of the parents and there derived populations. Results are expected in the summer 2013. Diversity of wheat composite cross populations. The practical question of “how much diversity is needed in populations?” has not been answered yet. BIOBREED will aim to to quantify the levels of diversity in wheat composite cross populations after the different selection steps i) cultivation with and without common bunt inoculum, and ii) sorting for single protein content. In a fist attempt SSR markers will be used to describe the influence these different selection pathways will have on the population diversity. 90 SSR markers—about two markers per chromosome arm—will be used to describe the initial genetic diversity of the 23 parental lines. F 6 seed of the different populations will be analysed with the same markers and population diversity after different selection pathways will be quantified

Association Mapping for Common Bunt Resistance in Wheat

Common bunt, caused by Tilletia caries and T. foetida, is a fungal disease of wheat world wide. Infection, occurring via seed borne teliospores, is generally controlled by the application of seed treatments prior to sowing. Farming systems like organic agriculture with a very limited range of organic seed treatments available rely heavily on common bunt resistance genes within wheat. In the framework of the BIOBREED project an association study in winter wheat was conducted, aiming at the identification of genetic loci linked to resistance towards common bunt in wheat. 152 European wheat cultivars were phenotyped for their resistance reaction for the two consecutive years 2011/12 at Agrologica research station at Mariager. Infection was scored as percent infected ears. The scorings were log-transformed to fit a disease scoring scale ranging from 1 to 9. The association analysis was performed for each year separately as well as for the mean scoring of the two years. The wheat cultivars were genotyped with DArT markers, yielding 1832 polymorphic loci. The association analysis was conducted using the computer program Genstat, with the ASReml module. Minimun allele frequency for the association analysis was set to 0.07. 13 out of the total of1832 marker in our study were linked to common bunt resistance in wheat (-log10(P) >3). These marker are located on 8 out of the 21 wheat chromosomes. Comparisons of these findings with other published results are difficult since only very little is known about the chromosomal location of common bunt resistance genes/QTL in wheat. Chromosome 2B was previously reported to carry gene(s) for common bunt resistance. Findings of our analysis are in accordance with this: 4 of the linked marker resided on this chromosome. Further, another two linked marker were found on chromosome 2D, another chromosome previously reported to carry common bunt resistance genes. Our study shows the possibilities of finding makers linked to common bunt resistance in wheat, and of using these markers for marker assisted selection of wheat cultivars tailored for the needs of organic agriculture

Industrial practitioners' mental models of adversarial machine learning

Although machine learning is widely used in practice, little is known about practitioners' understanding of potential security challenges. In this work, we close this substantial gap and contribute a qualitative study focusing on developers' mental models of the machine learning pipeline and potentially vulnerable components. Similar studies have helped in other security fields to discover root causes or improve risk communication. Our study reveals two facets of practitioners' mental models of machine learning security. Firstly, practitioners often confuse machine learning security with threats and defences that are not directly related to machine learning. Secondly, in contrast to most academic research, our participants perceive security of machine learning as not solely related to individual models, but rather in the context of entire workflows that consist of multiple components. Jointly with our additional findings, these two facets provide a foundation to substantiate mental models for machine learning security and have implications for the integration of adversarial machine learning into corporate workflows, decreasing practitioners' reported uncertainty, and appropriate regulatory frameworks for machine learning security

The JKind Model Checker

JKind is an open-source industrial model checker developed by Rockwell Collins and the University of Minnesota. JKind uses multiple parallel engines to prove or falsify safety properties of infinite state models. It is portable, easy to install, performance competitive with other state-of-the-art model checkers, and has features designed to improve the results presented to users: inductive validity cores for proofs and counterexample smoothing for test-case generation. It serves as the back-end for various industrial applications.Comment: CAV 201

Quantitative information flow under generic leakage functions and adaptive adversaries

We put forward a model of action-based randomization mechanisms to analyse quantitative information flow (QIF) under generic leakage functions, and under possibly adaptive adversaries. This model subsumes many of the QIF models proposed so far. Our main contributions include the following: (1) we identify mild general conditions on the leakage function under which it is possible to derive general and significant results on adaptive QIF; (2) we contrast the efficiency of adaptive and non-adaptive strategies, showing that the latter are as efficient as the former in terms of length up to an expansion factor bounded by the number of available actions; (3) we show that the maximum information leakage over strategies, given a finite time horizon, can be expressed in terms of a Bellman equation. This can be used to compute an optimal finite strategy recursively, by resorting to standard methods like backward induction.Comment: Revised and extended version of conference paper with the same title appeared in Proc. of FORTE 2014, LNC

Quantitative information flow, with a view

We put forward a general model intended for assessment of system security against passive eavesdroppers, both quantitatively ( how much information is leaked) and qualitatively ( what properties are leaked). To this purpose, we extend information hiding systems ( ihs ), a model where the secret-observable relation is represented as a noisy channel, with views : basically, partitions of the state-space. Given a view W and n independent observations of the system, one is interested in the probability that a Bayesian adversary wrongly predicts the class of W the underlying secret belongs to. We offer results that allow one to easily characterise the behaviour of this error probability as a function of the number of observations, in terms of the channel matrices defining the ihs and the view W . In particular, we provide expressions for the limit value as n → ∞, show by tight bounds that convergence is exponential, and also characterise the rate of convergence to predefined error thresholds. We then show a few instances of statistical attacks that can be assessed by a direct application of our model: attacks against modular exponentiation that exploit timing leaks, against anonymity in mix-nets and against privacy in sparse datasets

Long term monitoring of bright TeV Blazars with the MAGIC telescope

The MAGIC telescope has performed long term monitoring observations of the bright TeV Blazars Mrk421, Mrk501 and 1ES1959+650. Up to 40 observations, 30 to 60 minutes each have been performed for each source evenly distributed over the observable period of the year. The sensitivity of MAGIC is sufficient to establish a flux level of 25% of the Crab flux for each measurement. These observations are well suited to trigger multiwavelength ToO observations and the overall collected data allow an unbiased study of the flaring statistics of the observed AGNs.Comment: 4 pages, 4 figures, to appear in the proceedings of the 30th International Cosmic Ray Conference, Merida, July 200