HBCR_DMR: A Hybrid Method Based on Beta-Binomial Bayesian Hierarchical Model and Combination of Ranking Method to Detect Differential Methylation Regions in Bisulfite Sequencing Data

DNA methylation is a key epigenetic modification involved in gene regulation, contributing to both physiological and pathological conditions. For a more profound comprehension, it is essential to conduct a precise comparison of DNA methylation patterns between sample groups that represent distinct statuses. Analysis of differentially methylated regions (DMRs) using computational approaches can help uncover the precise relationships between these phenomena. This paper describes a hybrid model that combines the beta-binomial Bayesian hierarchical model with a combination of ranking methods known as HBCR_DMR. During the initial phase, we model the actual methylation proportions of the CpG sites (CpGs) within the replicates. This modeling is achieved through beta-binomial distribution, with parameters set by a group mean and a dispersion parameter. During the second stage, we establish the selection of distinguishing CpG sites based on their methylation status, employing multiple ranking techniques. Finally, we combine the ranking lists of differentially methylated CpG sites through a voting system. Our analyses, encompassing simulations and real data, reveal outstanding performance metrics, including a sensitivity of 0.72, specificity of 0.89, and an F1 score of 0.76, yielding an overall accuracy of 0.82 and an AUC of 0.94. These findings underscore HBCR_DMR's robust capacity to distinguish methylated regions, confirming its utility as a valuable tool for DNA methylation analysis.

Sustainable Health and Wellbeing in the European Union

Background

Altogether, 17 Sustainable Development Goals (SDGs) are an urgent call for action to end poverty, protect the planet, and ensure prosperity for all. Goal 3 is crucial in terms of good health and wellbeing. The main aim of this study is to analyze and evaluate differences among indicators of SDG 3: Sustainable health and wellbeing in the EU countries.

Methods

The status and development of the EU Member States regarding their successes or failures in terms of Goal 3 were subjected to analysis. Altogether, 11 indicators were used to rank the EU countries using the TOPSIS and ranking methods. The ranks were assigned to the countries in two periods. The first period is related to the time from 2010 till 2014, and the second period from 2015 till 2019.

Results

The EU countries achieved a positive development in 10 of 11 indicators that monitor the achievement of the EU in terms of Goal 3. The only variable that changed negatively was the obesity rate. Positivity was observed in the decline of the standardized preventable and treatable mortality, which declined from 317.3 in the first period to 295 in the second period; the drop of the population weighted annual mean concentration of fine particulate PM2.5, from 16.4 to 13.6 μg/m³, and also in the increase of the share of people with good or very good perceived health, which was combined with a decrease of the self-reported unmet need for medical examination and care. The best-rated country in terms of SDG 3 was, in both periods, Sweden, while the worst-rated was Latvia.

Conclusions

Governments and institutions in the EU can intervene to increase the accessibility and quality of the health care system, but every citizen should try to do their best to reduce some of the risk factors, such as smoking or obesity, to try living healthier and to help to achieve higher ambitions in terms of sustainable health and wellbeing.

Homography Ranking Based on Multiple Groups of Point Correspondences

Homography mapping is often exploited to remove perspective distortion in images and can be estimated using point correspondences of a known object (marker). We focus on scenarios with multiple markers placed on the same plane if their relative positions in the world are unknown, causing an indeterminate point correspondence. Existing approaches may only estimate an isolated homography for each marker and cannot determine which homography achieves the best reprojection over the entire image. We thus propose a method to rank isolated homographies obtained from multiple distinct markers to select the best homography. This method extends existing approaches in the post-processing stage, provided that the point correspondences are available and that the markers differ only by similarity transformation after rectification. We demonstrate the robustness of our method using a synthetic dataset and show an approximately 60% relative improvement over the random selection strategy based on the homography estimation from the OpenCV library.

STABILITYSOFT: A new online program to calculate parametric and non-parametric stability statistics for crop traits

PREMISE OF THE STUDY

Access to improved crop cultivars is the foundation for successful agriculture. New cultivars must have improved yields that are determined by quantitative and qualitative traits. Genotype-by-environment interactions (GEI) occur for quantitative traits such as reproductive fitness, longevity, height, weight, yield, and disease resistance. The stability of genotypes across a range of environments can be analyzed using GEI analysis. GEI analysis includes univariate and multivariate analyses with both parametric and non-parametric models.

METHODS AND RESULTS

The program STABILITYSOFT is online software based on JavaScript and R to calculate several univariate parametric and non-parametric statistics for various crop traits. These statistics include Plaisted and Peterson's mean variance component (θ i ), Plaisted's GE variance component (θ (i) ), Wricke's ecovalence stability index (W i 2 ), regression coefficient (b i ), deviation from regression (S di 2 ), Shukla's stability variance (σ i 2 ), environmental coefficient of variance (CV i ), Nassar and Huhn's statistics (S (1) , S (2) ), Huhn's equation (S (3) and S (6) ), Thennarasu's non-parametric statistics (NP (i) ), and Kang's rank-sum. These statistics are important in the identification of stable genotypes; hence, this program can compare and select genotypes across multiple environment trials for a given data set. This program supports both the repeated data across environments and matrix data types. The accuracy of the results obtained from this software was tested on several crop plants.

CONCLUSIONS

This new software provides a user-friendly interface to estimate stability statistics accurately for plant scientists, agronomists, and breeders who deal with large volumes of quantitative data. This software can also show ranking patterns of genotypes and describe associations among different statistics with yield performance through a heat map plot. The software is available at https://mohsenyousefian.com/stabilitysoft/.