Tree-Soil Interaction Studies on Different Species in Arboretum

2019 ◽  
pp. 209-238
Author(s):  
Bibek Birua ◽  
Sant Kumar Singh ◽  
Prabhat Ranjan Oraon
Keyword(s):  
2001 ◽  
Vol 120 (5) ◽  
pp. A581-A581
Author(s):  
T ANDERSSON ◽  
L ASTRAZENECA ◽  
K ROHSS ◽  
M HASSANALIN

1978 ◽  
Vol 39 (C6) ◽  
pp. C6-367-C6-368 ◽  
Author(s):  
C. W. Kimball ◽  
van Landuyt ◽  
C. Barnett ◽  
G. K. Shenoy ◽  
B. D. Dunlap ◽  
...  

2018 ◽  
Vol 12 (2) ◽  
pp. 181-190 ◽  
Author(s):  
Priya P. Panigrahi ◽  
Ramit Singla ◽  
Ankush Bansal ◽  
Moacyr Comar Junior ◽  
Vikas Jaitak ◽  
...  

BioTech ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Yinhao Du ◽  
Kun Fan ◽  
Xi Lu ◽  
Cen Wu

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.


Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 349
Author(s):  
Sara Tedesco ◽  
Alexander Erban ◽  
Saurabh Gupta ◽  
Joachim Kopka ◽  
Pedro Fevereiro ◽  
...  

In viticulture, grafting is used to propagate Phylloxera-susceptible European grapevines, thereby using resistant American rootstocks. Although scion–rootstock reciprocal signaling is essential for the formation of a proper vascular union and for coordinated growth, our knowledge of graft partner interactions is very limited. In order to elucidate the scale and the content of scion–rootstock metabolic interactions, we profiled the metabolome of eleven graft combination in leaves, stems, and phloem exudate from both above and below the graft union 5–6 months after grafting. We compared the metabolome of scions vs. rootstocks of homografts vs. heterografts and investigated the reciprocal effect of the rootstock on the scion metabolome. This approach revealed that (1) grafting has a minor impact on the metabolome of grafted grapevines when tissues and genotypes were compared, (2) heterografting affects rootstocks more than scions, (3) the presence of a heterologous grafting partner increases defense-related compounds in both scion and rootstocks in shorter and longer distances from the graft, and (4) leaves were revealed as the best tissue to search for grafting-related metabolic markers. These results will provide a valuable metabolomics resource for scion–rootstock interaction studies and will facilitate future efforts on the identification of metabolic markers for important agronomic traits in grafted grapevines.


Fuel ◽  
2021 ◽  
Vol 290 ◽  
pp. 120078
Author(s):  
Jie Yu ◽  
Dingshun Wang ◽  
Lushi Sun
Keyword(s):  

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