Separate loci underlie resistance to root infection and leaf scorch during soybean sudden death syndrome

Soybean [Glycine max (L.) Merr.] cultivars show differences in their resistance to both the leaf scorch and root rot of sudden death syndrome (SDS). The syndrome is caused by root colonization by Fusarium virguliforme (ex. F. solani f. sp. glycines). Root susceptibility combined with reduced leaf scorch resistance has been associated with resistance to Heterodera glycines HG Type 1.3.6.7 (race 14) of the soybean cyst nematode (SCN). In contrast, the rhg1 locus underlying resistance to Hg Type 0 was found clustered with three loci for resistance to SDS leaf scorch and one for root infection. The aims of this study were to compare the inheritance of resistance to leaf scorch and root infection in a population that segregated for resistance to SCN and to identify the underlying quantitative trait loci (QTL). “Hartwig”, a cultivar partially resistant to SDS leaf scorch, F. virguliforme root infection and SCN HG Type 1.3.6.7 was crossed with the partially susceptible cultivar “Flyer”. Ninety-two F5-derived recombinant inbred lines and 144 markers were used for map development. Four QTL found in earlier studies were confirmed. One contributed resistance to leaf scorch on linkage group (LG) C2 (Satt277; P = 0.004, R 2 = 15%). Two on LG G underlay root infection at R8 (Satt038; P = 0.0001 R 2 = 28.1%; Satt115; P = 0.003, R 2 = 12.9%). The marker Satt038 was linked to rhg1 underlying resistance to SCN Hg Type 0. The fourth QTL was on LG D2 underlying resistance to root infection at R6 (Satt574; P = 0.001, R 2 = 10%). That QTL was in an interval previously associated with resistance to both SDS leaf scorch and SCN Hg Type 1.3.6.7. The QTL showed repulsion linkage with resistance to SCN that may explain the relative susceptibility to SDS of some SCN resistant cultivars. One additional QTL was discovered on LG G underlying resistance to SDS leaf scorch measured by disease index (Satt130; P = 0.003, R 2 = 13%). The loci and markers will provide tagged alleles with which to improve the breeding of cultivars combining resistances to SDS leaf scorch, root infection and SCN HG Type 1.3.6.7

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p