Genetic characterization and linkage disequilibrium mapping of resistance to gray leaf spot in maize (Zea mays L.)

AbstractGray leaf spot (GLS), caused by Cercospora zeae-maydis, is an important foliar disease of maize (Zea mays L.) worldwide, resistance to which is controlled by multiple quantitative trait loci (QTL). To gain insights into the genetic architecture underlying the resistance to this disease, an association mapping population consisting of 161 inbred lines was evaluated for resistance to GLS in a plant pathology nursery at Shenyang in 2010 and 2011. Subsequently, a genome-wide association study, using 41,101 single-nucleotide polymorphisms (SNPs), identified 51 SNPs significantly (P<0.001) associated with GLS resistance, which could be converted into 31 QTL. In addition, three candidate genes related to plant defense were identified, including nucleotide-binding-site/leucine-rich repeat, receptor-like kinase genes similar to those involved in basal defense. Two genic SNPs, PZE-103142893 and PZE-109119001, associated with GLS resistance in chromosome bins 3.07 and 9.07, can be used for marker-assisted selection (MAS) of GLS resistance. These results provide an important resource for developing molecular markers closely linked with the target trait, enhancing breeding efficiency

Dissecting the Genetic Basis Underlying Combining Ability of Plant Height Related Traits in Maize

Maize plant height related traits including plant height, ear height, and internode number are tightly linked with biomass, planting density, and grain yield in the field. Previous studies have focused on understanding the genetic basis of plant architecture traits per se, but the genetic basis of combining ability remains poorly understood. In this study, 328 recombinant inbred lines were inter-group crossed with two testers to produce 656 hybrids using the North Carolina II mating design. Both of the parental lines and hybrids were evaluated in two summer maize-growing regions of China in 2015 and 2016. QTL mapping highlighted that 7 out of 16 QTL detected for RILs per se could be simultaneously detected for general combining ability (GCA) effects, suggesting that GCA effects and the traits were genetically controlled by different sets of loci. Among the 35 QTL identified for hybrid performance, 57.1% and 28.5% QTL overlapped with additive/GCA and non-additive/SCA effects, suggesting that the small percentage of hybrid variance due to SCA effects in our design. Two QTL hotspots, located on chromosomes 5 and 10 and including the qPH5-1 and qPH10 loci, were validated for plant height related traits by Ye478 derivatives. Notably, the qPH5-1 locus could simultaneously affect the RILs per se and GCA effects while the qPH10, a major QTL (PVE &gt; 10%) with pleiotropic effects, only affected the GCA effects. These results provide evidence that more attention should be focused on loci that influence combining ability directly in maize hybrid breeding

Structure-Based Discovery of Highly Selective Phosphodiesterase-9A Inhibitors and Implications for Inhibitor Design

A new series of phosphodiesterase-9 (PDE9) inhibitors that contain a scaffold of 6-amino-pyrazolopyrimidinone have been discovered by a combination of structure-based design and computational docking. This procedure significantly saved load of chemical synthesis and is an effective method for the discovery of inhibitors. The best compound 28 has an IC50 of 21 nM and 3.3 µM respectively for PDE9 and PDE5, and about three orders of magnitude of selectivity against other PDE families. The crystal structure of the PDE9 catalytic domain in complex with 28 has been determined and shows a hydrogen bond between 28 and Tyr424. This hydrogen bond may account for the 860-fold selectivity of 28 against PDE1B, in comparison with about 30-fold selectivity of BAY73-6691. Thus, our studies suggest that Tyr424, a unique residue of PDE8 and PDE9, is a potential target for improvement of selectivity of PDE9 inhibitors

Efficient state representation with artificial potential fields for reinforcement learning

Abstract In the complex tasks environment, efficient state feature learning is a key factor to improve the performance of the agent’s policy. When encountering a similar new environment, reinforcement learning agents usually need to learn from scratch. However, humans naturally have a common sense of the environment and are able to use prior knowledge to extract environmental state features. Although the prior knowledge may not be fully applicable to the new environment, it is able to speed up the learning process of the state feature. Taking this inspiration, we propose an artificial potential field-based reinforcement learning (APF-RL) method. The method consists of an artificial potential field state feature abstractor (APF-SA) and an artificial potential field intrinsic reward model (APF-IR). The APF-SA can introduce human knowledge to accelerate the learning process of the state feature. The APF-IR can generate an intrinsic reward to reduce the invalid exploration and guide the learning of the agent’s policy. We conduct experiments on PySC2 with different mini-games. The experimental results show that the APF-RL method achieves improvement in the learning efficiency compared to the benchmarks

A capacity prediction model for the low porosity fractured reservoirs in the Kuqa foreland basin, NW China

Based on the description of fracture parameters and the relation between fracture development degree and capacity, a capacity prediction method was discussed of low porosity fractured sandstones in the Kuqa region. For coring section, fracture parameters are described directly by the core; for non-coring section, fracture parameters are available through imaging logging data, and after correction by difference between statistical parameters of core description and imaging logging interpretation in the coring section, the fracture parameters can be quantitatively described more accurately. The correlation of fracture seam and capacity is good in the gas well test section in Dabei - Keshen area and it is used to derive a formula of cracks capacity coefficient. Gas well capacity can be predicted by the regression formula of cracks capacity coefficient vs. meters gas production index. Also the models of uniform and non-uniform fracture distribution were established, capacity was analyzed based on the cracks capacity coefficient, results show that in fractured sandstone reservoirs if the total fracture number is constant, the capacity is small when fractures have a uniform distribution, the productivity is bigger when fractures are concentrated in one section. Key words: Kuqa foreland basin, low porosity fractured sandstone, fracture productivity coefficient, capacity prediction, fracture distribution mode