Quantitative trait loci mapping for resistance to maize streak virus in F2: 3 population of tropical maize

Open Access Article; Published online: 01 Feb 2020Maize streak virus (MSV) continues to be a major biotic constraint for maize production throughout Africa. Concerning the quantitative nature of inheritance of resistance to MSV disease (MSVD), we sought to identify new loci for MSV resistance in maize using F2:3 population. The mapping population was artificially inoculated with viruliferous leafhoppers under screenhouse and evaluated for MSVD resistance. Using 948 DArT markers, we identified 18 quantitative trait loci (QTLs) associated with different components of MSVD resistance accounting for 3.1–21.4% of the phenotypic variance, suggesting that a total of eleven genomic regions covering chromosomes 1, 2, 3, 4, 5 and 7 are probably required for MSVD resistance. Two new genomic regions on chromosome 4 revealed the occurrence of co-localized QTLs for different parameters associated with MSVD resistance. Moreover, the consistent appearance of QTL on chromosome 7 for MSVD resistance is illustrating the need for fine-mapping of this locus. In conclusion, these QTLs could provide additional source for breeders to develop MSV resistance

A Polynomial Time Algorithm for Lossy Population Recovery

We give a polynomial time algorithm for the lossy population recovery problem. In this problem, the goal is to approximately learn an unknown distribution on binary strings of length $n$ from lossy samples: for some parameter $\mu$ each coordinate of the sample is preserved with probability $\mu$ and otherwise is replaced by a `?'. The running time and number of samples needed for our algorithm is polynomial in $n$ and $1/\varepsilon$ for each fixed $\mu>0$. This improves on algorithm of Wigderson and Yehudayoff that runs in quasi-polynomial time for any $\mu > 0$ and the polynomial time algorithm of Dvir et al which was shown to work for $\mu \gtrapprox 0.30$ by Batman et al. In fact, our algorithm also works in the more general framework of Batman et al. in which there is no a priori bound on the size of the support of the distribution. The algorithm we analyze is implicit in previous work; our main contribution is to analyze the algorithm by showing (via linear programming duality and connections to complex analysis) that a certain matrix associated with the problem has a robust local inverse even though its condition number is exponentially small. A corollary of our result is the first polynomial time algorithm for learning DNFs in the restriction access model of Dvir et al

Group Testing with Probabilistic Tests: Theory, Design and Application

Identification of defective members of large populations has been widely studied in the statistics community under the name of group testing. It involves grouping subsets of items into different pools and detecting defective members based on the set of test results obtained for each pool. In a classical noiseless group testing setup, it is assumed that the sampling procedure is fully known to the reconstruction algorithm, in the sense that the existence of a defective member in a pool results in the test outcome of that pool to be positive. However, this may not be always a valid assumption in some cases of interest. In particular, we consider the case where the defective items in a pool can become independently inactive with a certain probability. Hence, one may obtain a negative test result in a pool despite containing some defective items. As a result, any sampling and reconstruction method should be able to cope with two different types of uncertainty, i.e., the unknown set of defective items and the partially unknown, probabilistic testing procedure. In this work, motivated by the application of detecting infected people in viral epidemics, we design non-adaptive sampling procedures that allow successful identification of the defective items through a set of probabilistic tests. Our design requires only a small number of tests to single out the defective items. In particular, for a population of size $N$ and at most $K$ defective items with activation probability $p$, our results show that $M = O(K^2\log{(N/K)}/p^3)$ tests is sufficient if the sampling procedure should work for all possible sets of defective items, while $M = O(K\log{(N)}/p^3)$ tests is enough to be successful for any single set of defective items. Moreover, we show that the defective members can be recovered using a simple reconstruction algorithm with complexity of $O(MN)$.Comment: Full version of the conference paper "Compressed Sensing with Probabilistic Measurements: A Group Testing Solution" appearing in proceedings of the 47th Annual Allerton Conference on Communication, Control, and Computing, 2009 (arXiv:0909.3508). To appear in IEEE Transactions on Information Theor

Factor validation and Rasch analysis of the individual recovery outcomes counter

Objective: The Individual Recovery Outcomes Counter is a 12-item personal recovery self assessment tool for adults with mental health problems. Although widely used across Scotland, limited research into its psychometric properties has been conducted. We tested its' measurement properties to ascertain the suitability of the tool for continued use in its present form.Materials and methods: Anonymised data from the assessments of 1,743 adults using mental health services in Scotland were subject to tests based on principles of Rasch measurement theory, principal components analysis and confirmatory factor analysis.Results: Rasch analysis revealed that the 6-point response structure of the Individual Recovery Outcomes Counter was problematic. Re-scoring on a 4-point scale revealed well ordered items that measure a single, recovery-related construct, and has acceptable fit statistics. Confirmatory factor analysis supported this. Scale items covered around 75% of the recovery continuum; those individuals least far along the continuum were least well addressed.Conclusions: A modified tool worked well for many, but not all, service users. The study suggests specific developments are required if the Individual Recovery Outcomes Counter is to maximise its' utility for service users and provide meaningful data for service providers.*Implications for Rehabilitation*Agencies and services working with people with mental health problems aim to help them with their recovery.*The individual recovery outcomes counter has been developed and is used widely in Scotland to help service users track their progress to recovery.*Using a large sample of routinely collected data we have demonstrated that a number of modifications are needed if the tool is to adequately measure recovery.*This will involve consideration of the scoring system, item content and inclusion, and theoretical basis of the tool