Soybean base saturation stress: Selecting populations for multiple traits using multivariate statistics

2021 ◽  
Author(s):  
Ariane de Andrea Pantaleão ◽  
Larissa Pereira Ribeiro Teodoro ◽  
Leandris Argentel Martínez ◽  
Jorge Gonzáles Aguilera ◽  
Cid Naudi Silva Campos ◽  
...  
Keyword(s):  
Multivariate Statistics ◽  
Base Saturation ◽  
Saturation Stress ◽  
Multiple Traits

High‐throughput phenotyping of soybean genotypes under base saturation stress conditions

2021 ◽  
Author(s):  
Sinomar Moreira Andrade ◽  
Larissa Pereira Ribeiro Teodoro ◽  
Fábio Henrique Rojo Baio ◽  
Cid Naudi Silva Campos ◽  
Cassiano Garcia Roque ◽  
...  
Keyword(s):  
High Throughput ◽  
Stress Conditions ◽  
Base Saturation ◽  
Saturation Stress ◽  
Soybean Genotypes ◽  
High Throughput Phenotyping

Multivariate Statistics with Psychiatric Applications

1973 ◽  
Vol 18 (7) ◽  
pp. 325-326
Author(s):  
STANLEY A. MULAIK
Keyword(s):  
Multivariate Statistics

Review of Multivariate Statistics With Emphasis on Regression

2010 ◽  
Author(s):  
Lonnie K. Stevans
Keyword(s):  
Multivariate Statistics

Fluctuating Asymmetry as a Bioindicator of Stress: Comparing Efficacy of Analyses Involving Multiple Traits

2000 ◽  
Vol 155 (1) ◽  
pp. 101
Author(s):  
Leung ◽  
Forbes ◽  
Houle
Keyword(s):  
Fluctuating Asymmetry ◽  
Multiple Traits

Exploring good cycling cities using multivariate statistics

2019 ◽  
Vol 40 (4) ◽  
pp. 526-543 ◽  
Author(s):  
Andrew J. Collins ◽  
Craig A. Jordan ◽  
R. Michael Robinson ◽  
Caitlin Cornelius ◽  
Ross Gore
Keyword(s):  
Multivariate Statistics

scholarly journals The inhibitory potency of isoxazole-curcumin analogue for the management of breast cancer: A comparative in vitro and molecular modeling investigation

2021 ◽  
Author(s):  
Fiona C. Rodrigues ◽  
N. V. Anil Kumar ◽  
Gangadhar Hari ◽  
K. S. R. Pai ◽  
Goutam Thakur
Keyword(s):  
Breast Cancer ◽  
Herbal Remedy ◽  
Cancer Cell Line ◽  
Heterocyclic Ring ◽  
Regulatory Role ◽  
Multiple Traits ◽  
One Pot ◽  
Anticancer Efficacy ◽  
Improved Stability

AbstractCurcumin, a potent phytochemical derived from the spice element turmeric, has been identified as a herbal remedy decades ago and has displayed promise in the field of medicinal chemistry. However, multiple traits associated with curcumin, such as poor bioavailability and instability, limit its effectiveness to be accepted as a lead drug-like entity. Different reactive sites in its chemical structure have been identified to incorporate modifications as attempts to improving its efficacy. The diketo group present in the center of the structural scaffold has been touted as the group responsible for the instability of curcumin, and substituting it with a heterocyclic ring contributes to improved stability. In this study, four heterocyclic curcumin analogues, representing some broad groups of heterocyclic curcuminoids (isoxazole-, pyrazole-, N-phenyl pyrazole- and N-amido-pyrazole-based), have been synthesized by a simple one-pot synthesis and have been characterized by FTIR, 1H-NMR, 13C-NMR, DSC and LC–MS. To predict its potential anticancer efficacy, the compounds have been analyzed by computational studies via molecular docking for their regulatory role against three key proteins, namely GSK-3β—of which abnormal regulation and expression is associated with cancer; Bcl-2—an apoptosis regulator; and PR which is a key nuclear receptor involved in breast cancer development. One of the compounds, isoxazole-curcumin, has consistently indicated a better docking score than the other tested compounds as well as curcumin. Apart from docking, the compounds have also been profiled for their ADME properties as well as free energy binding calculations. Further, the in vitro cytotoxic evaluation of the analogues was carried out by SRB assay in breast cancer cell line (MCF7), out of which isoxazole-curcumin (IC50–3.97 µM) has displayed a sevenfold superior activity than curcumin (IC50–21.89 µM). In the collation of results, it can be suggested that isoxazole-curcumin behaves as a potential lead owing to its ability to be involved in a regulatory role with multiple significant cancer proteins and hence deserves further investigations in the development of small molecule-based anti-breast cancer agents. Graphic abstract

Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil

2021 ◽  
Vol 193 (3) ◽  
Author(s):  
Jéssica Bandeira de Melo Carvalho Passos ◽  
David Bruno de Sousa Teixeira ◽  
Jasmine Alves Campos ◽  
Rafael Petruceli Coelho Lima ◽  
Elpídio Inácio Fernandes-Filho ◽  
...  
Keyword(s):  
Quality Assessment ◽  
River Basin ◽  
Multivariate Statistics ◽  
Southeastern Brazil ◽  
Doce River

scholarly journals Penalized partial least squares for pleiotropy

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Camilo Broc ◽  
Therese Truong ◽  
Benoit Liquet
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Association Studies ◽  
A Priori ◽  
Simulated Data ◽  
Real Data ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Multiple Traits ◽  
Application Fields

Abstract Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level. Results Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers. Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

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