CRISPR/Cas9 in Poplar Lignin Biosynthesis: Advances and Future Prospects

A novel phase retrieval method for hard X-ray in-line phase contrast imaging is presented in this paper, which is based on a parallel hybrid genetic algorithm. Different from the tradition numerical algorithm, our approach is a parallel hybrid intelligent method. Some techniques, such as parallel computing, simulated annealing selection and elitist migration, are all combined together with the genetic algorithm to accelerate the convergence and improve the quality of the final output. The proposed method is validated by numerical simulations on MATLAB platform. Experimental results show that the parallel hybrid GA is a satisfying solution for phase retrieval because of its fast convergence and high speed-up.

Glycosyltransferases and Xylan Biosynthesis in Poplar: Genetic Regulation and Implications for Wood Quality

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is an excellent fast-growing timber species and has significant value in the forestry industry. In order to increase the nitrogen and phosphorus absorption and utilization in Chinese fir, shared differentially expressed genes under low nitrogen and phosphorus stress were screened in this study. Seedling of Chinese fir clone X6 was cultivated in aeroponic system with 3 treatments, namely, low nitrogen (LN), low phosphorus (LP) and a control check (with nitrogen and phosphorus sufficient, CK). After 4 months of treatment, the roots from the LN, LP and CK groups were collected and transcriptome sequencing was done by LC Sciences (USA) using an Illumina platform. When comparing the LN stress group with the CK group, 977 SDGEs were detected, 264 of which had KEGG annotations; 931 SDGEs were detected when comparing the LP stress group with the CK group, of which 189 had KEGG annotations; 297 SDGEs were detected in both the LN stress and LP stress groups, 78 of which had KEGG annotations representing 98 metabolic pathways. Among the 78 selected SDEGs that were differentially expressed under both LN and LP stress conditions, Twenty-one SDEGs were selected based on the metabolic pathways that nitrogen and phosphorus are involved in, these genes are PNR, PSBA, EGLC, GLC, END, BGLU, AMY, A1E, PAL, GOGAT, NIR1, NIR2, C4M, PAL, PRDX6, POX, CCR, CCoAOMT, FDH, CHS and ANR. These genes can potentially be used in breeding to improve both nitrogen and phosphorus utilization efficiency in Chinese fir.

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is an excellent fast-growing timber species and has significant value in the forestry industry. In order to increase the nitrogen and phosphorus absorption and utilization in Chinese fir, shared differentially expressed genes under low nitrogen and phosphorus stress were screened in this study. Seedling of Chinese fir clone X6 was cultivated in aeroponic system with 3 treatments, namely, low nitrogen (LN), low phosphorus (LP) and a control check (with nitrogen and phosphorus sufficient, CK). After 4 months of treatment, the roots from the LN, LP and CK groups were collected and transcriptome sequencing was done by LC Sciences (USA) using an Illumina platform. When comparing the LN stress group with the CK group, 977 SDGEs were detected, 264 of which had KEGG annotations; 931 SDGEs were detected when comparing the LP stress group with the CK group, of which 189 had KEGG annotations; 297 SDGEs were detected in both the LN stress and LP stress groups, 78 of which had KEGG annotations representing 98 metabolic pathways. Among the 78 selected SDEGs that were differentially expressed under both LN and LP stress conditions, Twenty-one SDEGs were selected based on the metabolic pathways that nitrogen and phosphorus are involved in, these genes are PNR, PSBA, EGLC, GLC, END, BGLU, AMY, A1E, PAL, GOGAT, NIR1, NIR2, C4M, PAL, PRDX6, POX, CCR, CCoAOMT, FDH, CHS and ANR. These genes can potentially be used in breeding to improve both nitrogen and phosphorus utilization efficiency in Chinese fir.

Multi-Scale Regulation Mechanisms of Tree Stem Cells: From Molecular Level to Ecosystems