Results 71 to 80 of about 2,501,985 (169)

VIMENTIN – G-QUADRUPLEX INTERACTION AS A NEW TARGET FOR ANTICANCER THERAPY: LIGANDS SCREENING AND VALIDATION [PDF]

, 2022
reservedLe metastasi sono responsabili della maggior parte dei decessi dei pazienti oncologici. Tuttavia, i meccanismi che regolano questo processo sono ancora poco conosciuti.
PELLEGRINO, FRANCESCA
core  

Analysis of Genotype × Environment Interaction for Grain Yield in Chenopodium spp.

Czech Journal of Genetics and Plant Breeding, 2005
Twenty-seven germplasm lines of Chenopodium spp. were grown in four environments and evaluated for genotype × environment interactions and comparisons between 4 stability parameters viz.
A Bhargava, S Shukla, D Ohri
doaj   +1 more source

Study on the interaction of porphyrin with G-quadruplex DNAs [PDF]

, 2008
Interactions of 5,10,15,20-Tetrakis(N-propylpyridinium-4-yl)-21H,23H-porphyrin (TPrPyP4) with dimer hairpin (G(4)T(4)G(4))2 and parallel four-stranded (TG(4)T)4 G-quadruplex DNAs in Na+-containing buffer were studied.
Zhou, Jun, Han, Gaoyi, Wei, Chunying, Jia, Guoqing, Li, Can, 李灿   +5 more
core   +1 more source

Upstream-radiated rotor–stator interaction noise in mean swirling flow [PDF]

, 2005
A major component of the noise in modern aeroengines is rotor–stator interaction noise generated when the wake from the rotating fan impinges on a stator row downstream.
Cooper, A. J., Peake, N.
core   +1 more source

Estimating phenotypic stability for relevant yield and quality traits in French bean (Phaseolus vulgaris L.) using AMMI analysis

Heliyon
The presence of strong G x E (genotype by environment interaction) is a major hurdle for selecting superior genotypes when genotypes are placed into new and unfamiliar production systems. Genotype or cultivar (s) with high yield potential and having less
Kartik Pramanik, Gouri Shankar Sahu, Gobinda Chandra Acharya, Pradyumna Tripathy, Manasi Dash, A.V.V. Koundinya, Chinmaya Jena, Duvvada Sarath Kumar, Priyadarshani P. Mohapatra, Jyostnarani Pradhan, S. Karubakee, Durga Prasad Moharana   +11 more
doaj   +1 more source

Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Carthamus tinctorius L.). [PDF]

Mol Breed, 2022
Zhao H, Savin KW, Li Y, Breen EJ, Maharjan P, Tibbits JF, Kant S, Hayden MJ, Daetwyler HD.   +8 more
europepmc   +1 more source

A User Study Evaluation of Predictive Formal Modelling at Runtime in Human-Swarm Interaction [PDF]

Formal Modelling is often used as part of the design and testing process of software development to ensure that components operate within suitable bounds even in unexpected circumstances.
Sevegnani, Michele, Soorati, Mohammad, Ramchurn, Sarvapali D., Archibald, B., Schneiders, Eike, Sevegnani, M., Hunt, W., Fischer, Joel E., Fishcer, Joel E., Soorati, Mohammad D., Hunt, William, Schneiders, E., Abioye, Ayodeji O., Archibald, Blair, Naiseh, Mohammad, Soorati, M. D., Gu, Yue, Abioye, A. O., Gu, Y., Ramchurn, S. D., Fischer, J. E.   +20 more
core   +2 more sources

Defining the target population of environments (TPE) for enviromics studies using R based GIS tools

Crop Breeding and Applied Biotechnology
We present an R-based function for defining TPE as GIS-polygons, intended for use in enviromics studies. It offers customizable parameters, such as pixel size, buffer boundaries, and concavity, providing enhanced flexibility for G×E analysis.
Demila D. M. Cruz, Alexandre B. Heinemann, Gustavo E. Marcatti, Rafael T. Resende   +3 more
doaj  

Research on Regional Adaptability and Stability of Maize Hybrids in Mid-to-High Altitude Areas of Yunnan Province Based on GGE Biplot Analysis

Agronomy
Identifying superior genotypes in multi-environment trials is crucial for accelerating cultivar improvement and breeding innovation. This study evaluated the yield potential of 29 maize hybrids (including the control) across 10 trial locations in mid-to ...
Qingyan Zi, Zhilan Ye, Chenyu Ma, Chaorui Liu   +3 more
doaj   +1 more source

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models

The Plant Genome, 2016
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel.
Jaime Cuevas, José Crossa, Víctor Soberanis, Sergio Pérez-Elizalde, Paulino Pérez-Rodríguez, Gustavo de los Campos, O. A. Montesinos-López, Juan Burgueño   +7 more
doaj   +1 more source