Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice

Manual phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale assessment of rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We could explain 30% of the grain yield variance from 2 tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting

Novel digital features feature discriminate between drought resistant and drought sensitive rice under controlled and field conditions

Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future

Novel digital features feature discriminate between drought resistant and drought sensitive rice under controlled and field conditions

Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future

Nanoscale observation of morphological transformation during ageing of silica and silica-alumina

Atomic force microscopy (AFM) was used for in-situ observation of nanoscale morphological transformations during the ageing step in sol-gel synthesis. Silica, alumina and silica-alumina samples with different Si/Al ratios were prepared from inorganic salt precursors and geled at low pH. Silica and silica-alumina samples formed branch-like gel network made of nanometer-sized clusters. During ageing at room temperature, the overall structure of the gel network remained unchanged but the clusters underwent phase transformation, coaslesence, coarsening, fragmentation, as well as dissolution resulting in the internal restructuring of the gel material. Morphological transformation associated with crystallization of pseudo-boehmite phase was observed for the alumina samples. These nanometer-scale processes are expected to play a key role in dictating the material properties of the final sol-gel product

Mixed reforming of simulated gasoline to hydrogen in a BSCFO membrane reactor

Currently, fuel cells are receiving more and more attention as the most promising new power generation technology, and fuel processing by the mixed reforming of liquid hydrocarbons (MRL) with water and oxygen is regarded as a desirable way for fuel cells. In this paper, we developed a new mixed reforming method for hydrogen production by combining a dense ceramic membrane Ba 0.5Sr 0.5Co 0.8Fe 0.2O 3- δ(BSCFO) with a catalyst LiLaNiO/γ-Al 2O 3 in a membrane reactor and reforming a simulated gasoline. During a 500-h long-term test at optimized reaction conditions, all the components in the simulated gasoline converted completely, and around 90% selectivity of CO, around 95% selectivity of H 2 and around 8.0 mL cm -2 min -1 oxygen permeation flux were achieved. This provides a new optional way of hydrogen production for fuel cells. © 2006 Elsevier B.V. All rights reserved