Mapping of quantitative trait loci for fiber and lignin contents from an interspecific cross Oryza sativa×Oryza rufipogon

Rice straw is always regarded as a by-product of rice production,but it could be a significant energy source for ruminant animals.Knowledge of the genetic variation and genetic architecture of cell wall traits will facilitate rice breeders by improving relevant traits through selective breeding and genetic engineering.The common wild rice,Oryza rufipogon Griff.,which is considered to be the progenitor of Oryza sativa,has been widely utilized for the identification of genes of agronomic importance for rice genetic improvement.In the present study,the mapping of quantitative trait loci (QTLs) for acid detergent fiber (ADF),neutral detergent fiber (NDF),acid detergent lignin (ADL),and ADL/NDF ratio was carried out in two environments using a backcrossed inbred line (BIL) population derived from a cross between the recurrent parent Xieqingzao B (XB) and an accession of Dongxiang wild rice (DWR).The results indicated that all four traits tested were continuously distributed among the BILs,but many BILs showed transgressive segregation.A total of 16 QTLs were identified for the four traits,but no QTLs were in common in two environments,suggesting that environment has dramatic effects on fiber and lignin syntheses.Compared to the QTL positions for grain yield-related traits,there were no unfavorable correlations between grain yield components and cell wall traits in this population.The QTLs identified in this study are useful for the development of dual-purpose rice varieties that are high in grain yield and are also high in straw quality.

Reverse Furthest Neighbors Query in Road Networks

Given a road network G = （V, E）, where V（E） denotes the set of vertices（edges） in G, a set of points of interest P and a query point q residing in G, the reverse furthest neighbors （RFNR） query in road networks fetches a set of points p ∈ P that take q as their furthest neighbor compared with all points in P ∪ {q}. This is the monochromatic RFNR （MRFNR） query. Another interesting version of RFNR query is the bichromatic reverse furthest neighbor （BRFNR） query. Given two sets of points P and Q, and a query point q ∈ Q, a BRFNR query fetches a set of points p ∈ P that take q as their furthest neighbor compared with all points in Q. This paper presents efficient algorithms for both MRFNR and BRFNR queries, which utilize landmarks and partitioning-based techniques. Experiments on real datasets confirm the efficiency and scalability of proposed algorithms.