Comprehensible Visualization of Multidimensional Data: Sum of Ranking Differences-Based Parallel Coordinates

A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The fewer intersections between the variables, the more similar they are and the clearer the figure becomes. Therefore, the visualization depends on what techniques are used to order the variables. The key idea is to employ the SRD method to measure the degree of similarity of the variables, establishing a distance-based order. The distances between the axes are not uniformly distributed in the proposed visualization; their closeness reflects similarity, according to their SRD value. The proposed algorithm identifies false similarities through an iterative approach, where the angles between the SRD values determine which side a variable is plotted. Visualization of the algorithm is provided by MATLAB/Octave source codes. The proposed tool is applied to study how the sources of greenhouse gas emissions can be grouped based on the statistical data of the countries. A comparison to multidimensional scaling (MDS)-based ordering is also given. The use case demonstrates the applicability of the method and the synergies of the incorporation of the SRD method into parallel coordinates.

From Sampling to Analysis: How to Achieve the Best Sample Throughput via Sampling Optimization and Relevant Compound Analysis Using Sum of Ranking Differences Method?

The determination of an optimal volatile sampling procedure is always a key question in analytical chemistry. In this paper, we introduce the application of a novel non-parametric statistical method, the sum of ranking differences (SRD), for the quick and efficient determination of optimal sampling procedures. Different types of adsorbents (Porapak Q, HayeSep Q, and Carbotrap) and sampling times (1, 2, 4, and 6 h) were used for volatile collections of lettuce (Lactuca sativa) samples. SRD identified 6 h samplings as the optimal procedure. However, 1 or 4 h sampling with HayeSep Q and 2 h sampling with Carbotrap are still efficient enough if the aim is to reduce sampling time. Based on our results, SRD provides a novel way to not only highlight an optimal sampling procedure but also decrease evaluation time.

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection

Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over “traditional” pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether then-ary and binary indices rank the data in the same way. We demonstrate the use of the newn-ary similarity metrics ont-distributed stochastic neighbor embedding (t-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at:https://github.com/ramirandaq/MultipleComparisons

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: Theory and characteristics†

Quantification of the similarity of objects is a key concept in many areas of computational science. This includes cheminformatics, where molecular similarity is usually quantified based on binary fingerprints. While there is a wide selection of available molecular representations and similarity metrics, there were no previous efforts to extend the computational framework of similarity calculations to the simultaneous comparison of more than two objects (molecules) at the same time. The present study bridges this gap, by introducing a straightforward computational framework for comparing multiple objects at the same time and providing extended formulas for as many similarity metrics as possible. In the binary case (i.e. when comparing two molecules pairwise) these are naturally reduced to their well-known formulas. We provide a detailed analysis on the effects of various parameters on the similarity values calculated by the extended formulas. The extended similarity indices are entirely general and do not depend on the fingerprints used. Two types of variance analysis (ANOVA) help to understand the main features of the indices: (i) ANOVA of mean similarity indices; (ii) ANOVA of sum of ranking differences (SRD). Practical aspects and applications of the extended similarity indices are detailed in the accompanying paper: Miranda-Quintana et al. J Cheminform. 2021. 10.1186/s13321-021-00504-4. Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons.

Regional development potentials of Industry 4.0: Open data indicators of the Industry 4.0+ model

This paper aims to identify the regional potential of Industry 4.0 (I4.0). Although the regional background of a company significantly determines how the concept of I4.0 can be introduced, the regional aspects of digital transformation are often neglected with regard to the analysis of I4.0 readiness. Based on the analysis of the I4.0 readiness models, the external regional success factors of the implementation of I4.0 solutions are determined. An I4.0+ (regional Industry 4.0) readiness model, a specific indicator system is developed to foster medium-term regional I4.0 readiness analysis and foresight planning. The indicator system is based on three types of data sources: (1) open governmental data; (2) alternative metrics like the number of I4.0-related publications and patent applications; and (3) the number of news stories related to economic and industrial development. The indicators are aggregated to the statistical regions (NUTS 2), and their relationships analyzed using the Sum of Ranking Differences (SRD) and Promethee II methods. The developed I4.0+ readiness index correlates with regional economic, innovation and competitiveness indexes, which indicates the importance of boosting regional I4.0 readiness.

Functional handmade minions - consumers and experienced tasters sensory evaluation of the new product

The study examines consumer sensory preferences of 12 different handmade pastry products in the form of minions, made of rice and flaxseed flour, tapioca starch with natural taste ingredients and with addition of prebiotic (inulin), herbs and other ingredients. The sensory evaluation was performed by professionals (experienced tasters). Preferred minion flavour was tested at group of 324 consumers (hotel guests) of different nationality. ANOVA and t-test were performed to reveal differences in attitudes related to socio-demographic characteristics of the consumers. Also, determination of taste preferences according to consumer nationality was examined as an additional consumer care aspect. The evaluation of equality of the samples? average rates, as well as the groups of minions, is done by parametric or nonparametric model of variance analysis. Principal component analysis (PCA) was applied in order to group the investigated minions regarding their sensory properties, while the sum of ranking differences (SRD) was used to determine the minions with the best sensory properties. Consumers and experienced tasters have almost the same opinion about the sensory quality of minions, which indicates that assessment of the consumer can be considered as a representative opinion in the near future. Such functional food - minions could be widely used as a substitute for the most common commercial sweets rich in sugar and fat.

The analysis of chromatographic behavior of homoandrostane derivatives in reversed-phase ultra-high performance liquid chromatography

Homoandrostane derivatives, as compounds with significant bioactivity, were studied in terms of their chromatographic behavior in reversed-phase ultra-high performance liquid chromatography (RP-UHPLC). In the present study, five androstane derivatives from the series of homoandrostanes were analyzed, including: 3?-hydroxy-17-oxa-17a-homoandrost-5-en-16-one, 3?,5?-dihydroxy-17- oxa-17a-homoandrostane-6,16-dione, 17-oxa-5?,6?-epoxy-17a-homoandrostane-3,16-dione, 5?-hydroxy- 17-oxa-17a-homoandrostane-6,16-dione-3?-yl acetate and 3?-hydroxy-17-oxa-5?,6?-epoxy-Dhomoandrostan- 16-one. The compounds were analyzed by applying methanol-water mobile phases with different volume fractions of methanol, as a polar protic solvent, and logk0 parameters of each compound were determined. The outstanding correlations between in silico logP descriptors and logk0 parameters were obtained, as well as between in silico logD descriptors and logk0 parameters. The logk0 parameters are very well correlated with polar surface area (PSA) descriptor as well. The studied compounds and lipophilicity descriptors (including the chromatographic lipophilicity parameters - logk0) were clustered applying hierarchical cluster analysis (HCA) in the form of clustered heat map known as double dendrogram. Furthermore, the sum of ranking differences (SRD) method was used for the ranking of the lipophilicity measures of the analyzed homoandrostane derivatives so the most suitable lipophilicity measures of this series of compounds can be selected.