Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig\'s method. To compare the influence of various normalization methods in both the EWM and Hellwig\'s method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries\' realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max-min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig\'s method. The results are compared with the outcome obtained by Hellwig\'s method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig\'s method results.

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Background: One of the vital issues in promoting the sustainable tourism industry in developing countries, including Azerbaijan, is the well-grounded selection of tourism sites. Applying traditional approaches as a solution to this task, does not provide a relevant result in all cases in these countries due to local specifics of the tourism, the incompleteness of statistical data, the high-level uncertainty of the internal and external environment, and the questionable reliability of the available information. Methods: Since the statistical data are limited, and conventional formalization tools used for uncertainty description do not consider the reliability degree of the data, it is suggested to make decisions based on the Z-extension of fuzzy logic. A Delphi panel with the expert group is conducted to obtain the information required for the model development. Fuzzy Z-information-based TOPSIS and PROMETHEE methods are applied for the problem solution. Within these approaches Z-number-based procedures of the decision matrix normalization, defining the distance between solutions and the preference function, and swing weights determination are realized. Direct computations with Z-numbers are implemented.  Results: By applying Z-number-based multi-criteria decision-making methods, five potential regions of Azerbaijan have been evaluated for six criteria. The criteria reflect government policy to the development of the regions, economical, geographical, environmental factors, and infrastructure of the locations. Derived solutions are comparable in sense of sites ranking, and similar results were obtained using both methods. Direct calculations allow obtaining results based on the linguistic Z-evaluations of experts without distorting transformations. Conclusion: The managerial decision-making problems in the tourism sector, raised due to the aforementioned barriers, can be successfully resolved by applying Z-number-based multi-criteria approaches. The obtained results allow increasing a range of the decision-making tasks under a high degree of uncertainty to be solved for sustainable development studies and other areas.

Despite the recent advances in the standardization of untargeted metabolomics workflows, there is still a lack of attention to specific data treatment strategies that require deep knowledge of the biological problem and need to be applied after a well-thought out process to understand the effect of the practice. One of those strategies is data normalization. Data-driven assumptions are critical especially addressing unwanted variation present in the biological model as it can be the case in heterogeneous tissues, cells with different sizes or biofluids with different concentrations. Chronic kidney disease (CKD) is a widespread disorder affecting kidney structure and function. Animal models are being developed to be able to get valuable insights into the etiopathogenesis of the condition and effect of the treatments. Moreover, diagnosis and disease staging still require defining appropriate biomarkers. Untargeted metabolomics has the potential to deal with those challenges. Renal fibrosis is one of the consequences of kidney injury which greatly affects the concentration of metabolites in the same quantity of sample. To overcome this challenge, several data normalization strategies have been applied, following a multilevel normalization method with the overall aim of focussing on the relevant biological information and reducing the influence of disturbing factors. A comprehensive evaluation of the performance of the normalization strategies, both on methods assessing the intragroup variation and on the impact on differential analysis, is provided. Finally, we present evidence of the importance of biological-model-driven guided normalization methods and discuss multiple criteria that need to be taken into consideration to obtain robust and reliable data. Special concern is transmitted on the misleading conclusions that might be the consequence of inappropriate data pre-treatment solutions applied for untargeted methods. Graphical abstract.

Rediscoveries are not uncommon. However, sometimes they can be more significant than confirmatory or extensions of existing findings, although many authors today refer to them as discoveries in their own right. This has led to papers repeatedly rehearsing the expression \"we show here for the first time…\". When a finding has opened up a whole new field of research, this is more in line with a true discovery. When particular attention is drawn to such an event by editorials in widely read journals, such as Nature, its importance is bolstered. But if it turns out to be a rediscovery, the implications are considerable and the problem has to be brought to the attention not only of those in the same field of research, but to a wider audience to put the record straight. Consequently, acknowledgment of those who made the original discovery needs to be equally well publicised. A short discussion is presented of ways we might reduce the many claims of \"new\" discoveries that seem to be of considerable significance but are in fact rediscoveries.

Although several ant species are important targets for the development of molecular control strategies, only a few studies focus on identifying and validating reference genes for quantitative reverse transcription polymerase chain reaction (RT-qPCR) data normalization. We provide here an extensive study to identify and validate suitable reference genes for gene expression analysis in the ant Atta sexdens, a threatening agricultural pest in South America. The optimal number of reference genes varies according to each sample and the result generated by RefFinder differed about which is the most suitable reference gene. Results suggest that the RPS16, NADH and SDHB genes were the best reference genes in the sample pool according to stability values. The SNF7 gene expression pattern was stable in all evaluated sample set. In contrast, when using less stable reference genes for normalization a large variability in SNF7 gene expression was recorded. There is no universal reference gene suitable for all conditions under analysis, since these genes can also participate in different cellular functions, thus requiring a systematic validation of possible reference genes for each specific condition. The choice of reference genes on SNF7 gene normalization confirmed that unstable reference genes might drastically change the expression profile analysis of target candidate genes.

Studies have pointed to a trend in Western societies toward the normalization of homosexuality and emerging \"post-gayness\" among young people, who no longer consider their sexual identity meaningful in defining themselves. This article takes a closer look at the Dutch case where tolerance is regarded as a national virtue, while society remains heteronormative. In 38 interviews with Dutch same-sex-attracted young people, we investigated the labels they used to describe their sexual orientation to reveal what they can tell us about normalization, tolerance, and heteronormativity. In their labeling strategies, participants de-emphasized their sexual identity, othered, and reinforced the hetero/homo binary. They preferred labels without connotations to gender expression. While post-gay rhetoric was ideologically appealing, its use was not an outcome of their sexual orientation having become insignificant; it rather enabled them to produce normality. We discuss the findings against the backdrop of \"Dutch tolerance,\" which rests on an ideology of normality.

BACKGROUND: Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess global gene expression changes on the RNA level (transcriptome). While advances in high-throughput RNA-sequencing (RNA-seq) technologies allow for inexpensive data generation, accurate post-processing and normalization across samples is required to eliminate any systematic noise introduced by the biochemical and/or technical processes. Existing methods thus either normalize on selected known reference genes that are invariant in expression across the experiment, assume that the majority of genes are invariant, or that the effects of up- and down-regulated genes cancel each other out during the normalization.
RESULTS: Here, we present a novel method, moose2 , which predicts invariant genes in silico through a dynamic programming (DP) scheme and applies a quadratic normalization based on this subset. The method allows for specifying a set of known or experimentally validated invariant genes, which guides the DP. We experimentally verified the predictions of this method in the bacterium Escherichia coli, and show how moose2 is able to (i) estimate the expression value distances between RNA-seq samples, (ii) reduce the variation of expression values across all samples, and (iii) to subsequently reveal new functional groups of genes during the late stages of DNA damage. We further applied the method to three eukaryotic data sets, on which its performance compares favourably to other methods. The software is implemented in C++ and is publicly available from http://grabherr.github.io/moose2/.
CONCLUSIONS: The proposed RNA-seq normalization method, moose2 , is a valuable alternative to existing methods, with two major advantages: (i) in silico prediction of invariant genes provides a list of potential reference genes for downstream analyses, and (ii) non-linear artefacts in RNA-seq data are handled adequately to minimize variations between replicates.

In case-control studies of the human microbiome, the goal is to evaluate whether cases differ from controls in the microbiome composition of a particular body habitat and which taxa are responsible for the differences. These studies leverage sequencing technology and spectroscopy that provide new measurements of the microbiome.
Three challenges in conducting reproducible microbiome research using a case-control design are compensating for differences in observed and actual microbial community composition, detecting \"rare\" taxa in microbial communities, and choosing properly powered analysis methods. The significance of each challenge, evaluation of commonly held views, analysis of unanswered questions, and suggestions of strategies for solutions are discussed.
Understanding the effects of these choices on case-control analyses has been underappreciated, with an implicit assumption that further advances in technology will address all the current shortcomings.
It is recommended that research on the human microbiome include positive and negative control experiments to provide insight into bias, contamination, and technical variation. Research protocols such as these may afford a better opportunity to make quantitative and qualitative adjustments to data, thereby reducing the risk of falsely positive results, increasing power to discover true disease determinants, and enhancing interpretation across studies.

The combination of food quality standard and soil-plant transfer models can be used to derive critical limits of heavy metals for agricultural soils. In this paper, a robust methodology is presented, taking the variations of plant species and cultivars and soil properties into account to derive soil thresholds for lead (Pb) applying species sensitivity distribution (SSD). Three species of root vegetables (four cultivars each for radish, carrot, and potato) were selected to investigate their sensitivity differences for accumulating Pb through greenhouse experiment. Empirical soil-plant transfer model was developed from carrot New Kuroda grown in twenty-one soils covering a wide variation in physicochemical properties and was used to normalize the bioaccumulation data of non-model cultivars. The relationship was then validated to be reliable and would not cause over-protection using data from field experimental sites and published independent studies. The added hazardous concentration for protecting 95% of the cultivars not exceeding the food quality standard (HC5add) were then calculated from the Burr Type III function fitted SSD curves. The derived soil Pb thresholds based on the added risk approach (total soil concentration subtracting the natural background part) were presented as continuous or scenario criteria depending on the combination of soil pH and CEC.