Induction of Apple Cucumber (Cucumis melo) Buds Development by Combination of Kinetin and IAA In B5 (Gamborg) Medium

Research on indirect organogenesis of apple cucumber (Cucumis sp.) Was conducted from June until August 2020. The aim of the study is to get the best results from IAA and Kinetin concentrations on the growth of buds organogenesis to apple cucumber plants (Cucumis sp.) in B5 Gamborg Medium. The method used was an experimental method with nonparametric statistics with 25 treatments that were repeated 5 times and analyzed descriptively by the Kruskal Wallis test. The results showed that growth of apple cucumber shoots at the best combination treatment of IAA and Kinetin that is at concentrations 2,5x10-7 IAA and 3,2x10-5 Kinetin (A3B1) which resulted in appear shoots time at 39 days after initiation (hsi), it can provide the best growth of shoot height of 0,4 cm and the highest number of a shoot is 3. However, treatment with concentration 2,0x10-7 M IAA and 4,4x 10-5 Kinetin (A2B2) resulted in appearing shoots time at 39 days after initiation (hsi) and shoot height of 0,35 cm.

Spatial rank-based multifactor dimensionality reduction to detect gene–gene interactions for multivariate phenotypes

Background Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popular approach for detecting gene–gene interactions that has been extended in various forms to handle binary and continuous phenotypes. However, only few multivariate MDR methods are available for multiple related phenotypes. Current approaches use Hotelling’s T2 statistic to evaluate interaction models, but it is well known that Hotelling’s T2 statistic is highly sensitive to heavily skewed distributions and outliers. Results We propose a robust approach based on nonparametric statistics such as spatial signs and ranks. The new multivariate rank-based MDR (MR-MDR) is mainly suitable for analyzing multiple continuous phenotypes and is less sensitive to skewed distributions and outliers. MR-MDR utilizes fuzzy k-means clustering and classifies multi-locus genotypes into two groups. Then, MR-MDR calculates a spatial rank-sum statistic as an evaluation measure and selects the best interaction model with the largest statistic. Our novel idea lies in adopting nonparametric statistics as an evaluation measure for robust inference. We adopt tenfold cross-validation to avoid overfitting. Intensive simulation studies were conducted to compare the performance of MR-MDR with current methods. Application of MR-MDR to a real dataset from a Korean genome-wide association study demonstrated that it successfully identified genetic interactions associated with four phenotypes related to kidney function. The R code for conducting MR-MDR is available at https://github.com/statpark/MR-MDR. Conclusions Intensive simulation studies comparing MR-MDR with several current methods showed that the performance of MR-MDR was outstanding for skewed distributions. Additionally, for symmetric distributions, MR-MDR showed comparable power. Therefore, we conclude that MR-MDR is a useful multivariate non-parametric approach that can be used regardless of the phenotype distribution, the correlations between phenotypes, and sample size.

Procedures for non-normal data.

This chapter will serve as an introduction to nonparametric statistics with an emphasis on several methods supported by SAS® (Statistical Analysis System). The yield (dozens of ears per hectare) from a new sweetcorn cultivar was used as an example.

WYKORZYSTANIE WSPÓŁCZYNNIKA MORANA DO KLASYFIKACJI OBIEKTÓW PRZESTRZENNYCH NA PRZYKŁADZIE CEN GRUNTÓW ROLNICZYCH

The aim of the work was to show the possibility of using the global Moran statistics for the classification of spatial objects on the example of agricultural land prices The analyses were conducted at the level of communes (in NUTS 3 subregions of the voivodeship) in the years 2004-2012. Apart from the global Moran coefficient and the Moran diagram, nonparametric statistics (one-way ANOVA on ranks) were used in the work.