Crossing Lilium Orientals of different ploidy creates Fusarium-resistant hybrid

Oriental hybrid lily is of great commercial value, but it is susceptible to Fusarium disease that causes a significant loss to the production. A diploid Oriental hybrid resistant to Fusarium, Cai-74, was diploidized from triploid obtained from the offspring of tetraploid (from ‘Star Fighter’) and diploid (‘Con Amore’, ‘Acapulco’) by screening the hybrids of different cross combinations following inoculating Fusarium oxysporum to the tissue cultured plantlets in a greenhouse. By analyzing saponins content in bulbs of a number of lily genotypes with a known Fusarium resistance, it was found that the mutant Cai-74 had a much higher content of saponin than its parents. Highly resistant wild _L. dauricum_ had the highest level (4.59mg/g), followed by the resistant Cai-74 with 4.01mg/g. The resistant OT cultivars ‘Conca d’or’ and ‘Robina’ had a higher saponins content (3.70 mg/g) and 2.83 mg/g, than the susceptible Oriental lily cultivars ‘Sorbonne’, ‘Siberia’ and ‘Tiber’. The hybrid Cai-74 had a different karyotype compared with the normal Lilium Oriental hybrid cultivars. The results suggested that Cai-74 carries a chromosomal variation correlated to Fusarium resistance. Cai-74 might be used as a genetic resource for breeding of Fusarium resistant cultivars of Oriental hybrid lilies

One stone, two birds: A lightweight multidimensional learned index with cardinality support

Innovative learning based structures have recently been proposed to tackle index and cardinality estimation tasks, specifically learned indexes and data driven cardinality estimators. These structures exhibit excellent performance in capturing data distribution, making them promising for integration into AI driven database kernels. However, accurate estimation for corner case queries requires a large number of network parameters, resulting in higher computing resources on expensive GPUs and more storage overhead. Additionally, the separate implementation for CE and learned index result in a redundancy waste by storage of single table distribution twice. These present challenges for designing AI driven database kernels. As in real database scenarios, a compact kernel is necessary to process queries within a limited storage and time budget. Directly integrating these two AI approaches would result in a heavy and complex kernel due to a large number of network parameters and repeated storage of data distribution parameters. Our proposed CardIndex structure effectively killed two birds with one stone. It is a fast multidim learned index that also serves as a lightweight cardinality estimator with parameters scaled at the KB level. Due to its special structure and small parameter size, it can obtain both CDF and PDF information for tuples with an incredibly low latency of 1 to 10 microseconds. For tasks with low selectivity estimation, we did not increase the model's parameters to obtain fine grained point density. Instead, we fully utilized our structure's characteristics and proposed a hybrid estimation algorithm in providing fast and exact results