Analysis of Erythrocyte Parameters in Multiple and Long-Term Blood Donors from Northern Pomerania (Poland).

BACKGROUND/AIMS: Assessment of the levels of vital blood parameters in donors is essential to evaluate their health status, ensure their suitability for donation, preserve the integrity of the circulatory system, and facilitate comprehensive health monitoring. The aim of our study was to analyse the levels of haemoglobin, haematocrit, erythrocyte count, MCV, MCH, and MCHC in 12 groups of first-time donors and experienced donors of both sexes at the John Paul II Regional Blood Donation and Treatment Centre in Slupsk, northern Poland. The donors were divided into three age groups (18-30 years, 31-45 years, and 46-65 years). METHODS: Using MANOVA multivariate significance tests, we examined the main effects of donor-related factors (age, sex, donor stage) on morphological blood parameters to evaluate different haematological parameters, such as Hb, Ht, RBC, MCV, MCH, and MCHC, and identified statistically significant relationships between all variables. RESULTS: The multivariate analysis of these three main factors showed that the variation in haemoglobin (Hb) levels accounted for 46% of the explained dependence in this statistical model. In particular, approximately half of the variability in the multivariate statistical analysis was attributed to the role of Hb and haematocrit (Ht). In addition, the ß-coefficient values for Hb and Ht were statistically higher in relation to donor sex and donor type (single versus repeat). These ß-coefficient values from our data represent the strength and direction of the relationship between the haematological parameters (Hb and Ht) and the specific donor characteristics. A higher ß-coefficient indicates a stronger influence of donor sex and donor type on these parameters, suggesting that these factors contribute significantly to the variation in the Hb and Ht levels. Based on our results, the comprehensive analysis of the entire statistical model of metabolic biomarkers revealed the following hierarchy: Hb > Ht > MCHC > MCV > RBC > MCH. The results obtained showed strong statistical relationships, as indicated by the high values of the key statistical indicators in our analysis. The coefficient of determination (R²) showed that the model explained a significant proportion of the variance in the data, while the F-test statistic confirmed the significance of the predictors. CONCLUSION: These strong statistical dependencies provided a clear justification for selecting this model over others, as it effectively represented the underlying relationships within the data. These statistics help to assess how well the model matches the actual data, thereby helping to reduce the risks associated with blood donation, optimise donor safety, and maintain the quality and efficiency of blood transfusion services.

High-dimensional multivariate analysis of variance via geometric median and bootstrapping.

The geometric median, which is applicable to high-dimensional data, can be viewed as a generalization of the univariate median used in 1-dimensional data. It can be used as a robust estimator for identifying the location of multi-dimensional data and has a wide range of applications in real-world scenarios. This paper explores the problem of high-dimensional multivariate analysis of variance (MANOVA) using the geometric median. A maximum-type statistic that relies on the differences between the geometric medians among various groups is introduced. The distribution of the new test statistic is derived under the null hypothesis using Gaussian approximations, and its consistency under the alternative hypothesis is established. To approximate the distribution of the new statistic in high dimensions, a wild bootstrap algorithm is proposed and theoretically justified. Through simulation studies conducted across a variety of dimensions, sample sizes, and data-generating models, we demonstrate the finite-sample performance of our geometric median-based MANOVA method. Additionally, we implement the proposed approach to analyze a breast cancer gene expression dataset.

From Wasteland to Bloom: Exploring the Organizational Profiles of Occupational Health and Well-Being Strategies and Their Effects on Employees' Health and Well-Being.

Based on the signaling and conservation of resources theories, this study aims to identify different strategic organizational profiles related to occupational health and well-being (OHWB). Additionally, this study explores how these various organizational profiles impact employees' well-being, specifically in relation to absenteeism, emotional exhaustion, work overload, intention to quit, and job satisfaction. Data were collected from 59 organizations and 2828 employees. The first phase of this study presents the latent profile analysis carried out to identify OHWB organizational profiles. This analysis reveals four organizational profiles that are metaphorically named according to the growth stages of plants (i.e., wasteland, sprouting, budding, and blooming OHWB profiles). The second phase of this study investigates the associations between the latent profiles assigned to the organizations with absenteeism, intention to quit, emotional exhaustion, feelings of work overload, and job satisfaction among their employees using MANOVA. The results show that organizational profiles influence employees' health and well-being. Employees working in organizations with a low OHWB profile, known as the "wasteland profile", tend to report more days of absenteeism, higher levels of emotional exhaustion, greater work overload, and lower job satisfaction. Employees are also more likely to express a greater intention to quit their jobs than those working in organizations with a higher OHWB profile (a "blooming profile"). This study is useful for organizations and practitioners seeking to understand how investing in a health and well-being strategy can benefit their employees.

Iranian and Turkish EFL instructors' Critical Openness, Reflective Skepticism, Innovative Thinking and Accountability: A comparative study.

The increasing interest in Turkish educational programs has led to the increasing interest in educational migration among the younger Iranian generations. Instructors, as the executive part of educational programs, can also play a key role in satisfying students' expectations and educational goals. The aim of this study was to identify and compare the Critical Openness (CO), Reflective Skepticism (RS), innovative thinking (IT), external accountability (EA), and internal accountability (IA) for the Iranian and Turkish English as Foreign Language (EFL) instructors through an online survey with Critical Thinking Theory and the 21st century skill consideration lens. To this end, a convenient sample of Iranian (N = 286) and Turkish (N = 281) EFL instructors were invited to take part in the online survey voluntarily. The scales consisted of the Likert scales of Rosenblatt (2017) [1], Semerci (2007) [2], and Sosu (2013) [3], because the conceptual frameworks were also taken from these studies. In the analysis stage, MANOVA was conducted to compare the results of the online survey between Iranian and Turkish EFL instructors in terms of their level of CO, RS, IT, EA, and IA. The analysis of the collected data uncovered that Turkish EFL instructors got higher scores in CO, RS, EA, and IA than Iranian EFL instructors while Iranian EFL instructors received higher scores in the employment of IT. The implications of the results would suggest collaborations between educational policymakers and teacher training course designers.

New Facts on the Antimicrobial Essential Oil of Satureja kitaibelii.

The objective of the present study was to assess the difference in antimicrobial activity of Satureja kitaibelii Wierzb. ex Heuff. essential oil at three growth stages. In addition, using statistical and chemometric tools, we tried to explain why the essential oil from a certain vegetation stage shows the highest antimicrobial activity. S. kitaibelii essential oils demonstrated minimum inhibitory concentration values from 160 to 10000 µg mL-1 , and minimum microbicidal concentration values from 630 to 20000 µg mL-1 . Geraniol, borneol, limonene and p-cymene are the dominant compounds of S. kitaibelii essential oil. The most abundant compound, geraniol, possesses antimicrobial activity in a range of MIC values from 40 to 5000 µg mL-1 and MMC values from 80 to 10000 µg mL-1 . The highest activity of essential oil for all tested strains of microorganisms was recorded in November. Results of statistical analysis indicate that the percentage of dominant compounds of essential oils does not affect the antibacterial activity of essential oils. Chemometric analyses leads to the conclusion that borneol, spathulenol, caryophyllene oxide and limonene can be the main contributors to the antibacterial activity of essential oil from November and that their mutual ratio is important. These results may represent a new methodological approach for future research on essential oils.

TestDimorph: An R package for analysis of interpopulation sexual dimorphism differences using summary statistics.

OBJECTIVE: The degree of sexual dimorphism in certain traits between males and females differ from one sample to another. Although trait differences by sex are often reported in bioanthropological research, few studies test for statistical significance or make raw data available. TestDimorph is the first R package dedicated to testing and comparing the degree of sexual dimorphism among different samples by leveraging summary statistics. MATERIALS AND METHODS: We provide two approaches of analysis of inter-sample differences in degree of sexual dimorphism: univariate and multivariate for two or more samples. The methods follow upon publications primarily from the AJBA. Within-sex size variability between samples is compared using one-way ANOVA followed by control for multiple pairwise comparisons. In addition, we compute the overlapping area between the density functions of two normal distributions from the mixture intersection index or the non-overlapping area using the dissimilarity index as well as Hedges' g with inferential support using the 95% confidence interval. Finally, we use a multivariate analysis of differences in patterning of sexual dimorphism between samples. RESULTS: We demonstrate various results from applying TestDimorph functions to data supplied with the package. DISCUSSION: The package has many features including functionality for working with summary statistics, simulating data from summary statistics, and the extraction of summary statistics from raw data, so that the entire analysis can be performed through the package.

UPLC-ESI-QTOF-MS assisted targeted metabolomics to study the enrichment of vinca alkaloids and related metabolites in Catharanthus roseus plants grown under controlled LED environment.

Enrichment of pharmaceutically important vinca alkaloids, vinblastine and vincristine, in the leaves of Madagascar periwinkle (Catharanthus roseus) plants through different pre- or postharvest treatments or cultivation conditions, e.g., exposing the plants to UV-irradiation, has been in focus for decades. Controlled LED environment in the visible light range offers the possibility of monitoring the changes in the concentration of metabolites in the vinca alkaloid-related pathway without involving UV-related abiotic stress. In the frame of our targeted metabolomics approach, 64 vinca alkaloids and metabolites were screened with the help of a UPLC-ESI-QTOF-MS instrumental setup from the leaf extracts of C. roseus plants grown in chambers under control (medium light), low light, and high blue / high red/ high far-red conditions. Out of the 14 metabolites that could be assigned either unambiguously with authentic standards or tentatively with high resolution mass spectrometry-based methods, all three dimer vinca alkaloids, that is, 3',4'-anhydrovinblastine, vinblastine and vincristine showed an at least nine-fold enrichment under high blue irradiation when compared with the control conditions: final concentrations of 961 mg kg-1 dry weight, 33.8 mg kg-1 dry weight, and 11.7 mg kg-1 dry weight could be achieved, respectively. As supported by multivariate statistical analysis, the key metabolites of the vinca alkaloid pathway were highly represented among the metabolites that were specifically stimulated by high blue light application.

Can we achieve biomimetic electrospun scaffolds with gelatin alone?

Introduction: Gelatin is a natural polymer commonly used in biomedical applications in combination with other materials due to its high biocompatibility, biodegradability, and similarity to collagen, principal protein of the extracellular matrix (ECM). The aim of this study was to evaluate the suitability of gelatin as the sole material to manufacture tissue engineering scaffolds by electrospinning. Methods: Gelatin was electrospun in nine different concentrations onto a rotating collector and the resulting scaffold's mechanical properties, morphology and topography were assessed using mechanical testing, scanning electron microscopy and white light interferometry, respectively. After characterizing the scaffolds, the effects of the concentration of the solvents and crosslinking agent were statistically evaluated with multivariate analysis of variance and linear regressions. Results: Fiber diameter and inter-fiber separation increased significantly when the concentration of the solvents, acetic acid (HAc) and dimethyl sulfoxide (DMSO), increased. The roughness of the scaffolds decreased as the concentration of dimethyl sulfoxide increased. The mechanical properties were significantly affected by the DMSO concentration. Immersed crosslinked scaffolds did not degrade until day 28. The manufactured gelatin-based electrospun scaffolds presented comparable mechanical properties to many human tissues such as trabecular bone, gingiva, nasal periosteum, oesophagus and liver tissue. Discussion: This study revealed for the first time that biomimetic electrospun scaffolds with gelatin alone can be produced for a significant number of human tissues by appropriately setting up the levels of factors and their interactions. These findings also extend statistical relationships to a form that would be an excellent starting point for future research that could optimize factors and interactions using both traditional statistics and machine learning techniques to further develop specific human tissue.

Data showing that post-traumatic stress symptoms and defense mechanisms change based on perceived stress levels: A multivariate analysis of variance approach during the COVID-19 pandemic.

The present research article provides data about the differences in post-traumatic stress symptoms, and defense mechanisms based on the levels of perceived stress (low, average, high), using a large national sample of 1100 Italian individuals, collected during the first wave of the COVID-19 pandemic. Participants completed an online survey through the Google Form platform, where the Ten-Item Perceived Stress Scale, Impact of Event Scale - Revised, and Forty Item Defense Style Questionnaire were included. First, the cut-offs of the perceived stress scale were calculated by determining the 25th and 75th percentile scores for the sample of this survey. Then, MANOVA analyses were performed, together with ANOVAs and the Bonferroni Post hoc analyses as a follow-up. The dataset (.xlsx) includes the survey scores, while the tables and figures provide the analysed data, where the differences are shown. This data article may provide useful bases for future research on perceived stress and for suggesting associated factors on which focus clinical intervention and preventive programs.

The Relationship among Anxiety, Worry, Perceived Stress, Defense Mechanisms, and High Levels of Post-Traumatic Stress Symptoms: A Discriminant Analytic Approach.

Post-traumatic stress disorder (PTSD) is a pathological condition that may lead to a significant deterioration in the quality of life over time. Therefore, the study of the elements that can characterize the disorder could be considered of great clinical interest and relevance. The aim of the present research was to empirically discriminate the influence of perceived stress, state anxiety, worry, and defense mechanisms (mature, neurotic, and immature) at different levels of post-traumatic stress symptoms. A sample of 1250 participants (69.5% women, 30.5% men; Mage = 34.52, SD = 11.857) completed an online survey including the Impact of Event Scale-Revised, Ten-Item Perceived Stress Scale, Penn State Worry Questionnaire, Forty Item Defense Style Questionnaire, and State-Trait Anxiety Inventory-Form X3. Data were analysed by implementing MANOVA and discriminant analysis. Results showed significant differences in the levels of perceived stress, state anxiety, and worry, as well as neurotic and immature defenses based on the levels of post-traumatic stress symptoms: F(12,2484) = 85.682, p < 0.001; Wilk's Λ = 0.430. Furthermore, these variables discriminate significant accuracy between participants who reported a mild psychological impact and those with a probable presence of PTSD, with perceived stress, which was found to be the best predictor. Classification results indicated that the original grouped cases were classified with 86.3% overall accuracy. Such findings may provide useful insight for clinical practice.

A comparison of the university mathematics learning environment with its high school equivalent.

In light of rising college student debt, many states now offer multiple options for students to earn college credit while still in high school. Concurrent enrollment programs, which allow qualified high school teachers to teach college credit-bearing classes in the high school, are one such option. Because concurrent enrollment classes teach college-level material at college-level rigor, they offer an ideal way to compare the secondary and tertiary learning environments across identical academic expectations. This study sought to compare the university mathematics environment with its concurrent enrollment counterpart. The WIHIC was found to be valid and reliable for the university population. The comparison of 242 students in university classrooms with 260 students in concurrent enrollment classrooms revealed a statistical difference, with the concurrent enrollment setting scoring higher in Involvement, Teacher Support, and Student Cohesion and the university setting scoring higher in Task Orientation. This implies that earning college credit in a secondary setting is a viable, and possibly even preferable, alternative to earning it in a university setting. We examine the discrepancy in scores-particularly the large discrepancy in Task Orientation-and discuss the benefit of the flipped classroom as one path to improving the university learning environment.

Long-Term Effects of Inspiratory Muscle Training in Institutionalized Elderly Women: A Double-Blind Randomized Controlled Trial.

BACKGROUND: Inspiratory muscle training (IMT) has been applied to different populations, but evidence in elderly women and maintenance of improvements achieved during training is scarce. The objective of the study was to evaluate the effectiveness of IMT 6 months after finishing the training period in elderly women. METHODS: A controlled, randomized, double-blind trial was conducted, with allocation concealment performed on 26 institutionalized elderly women distributed into 2 groups: high loads and low loads. Over an 8-week period, an IMT protocol was followed; later, elderly women continued for 6 months with their daily normal activities. Inspiratory strength, expiratory strength, and functional capacity were measured pre-IMT, post-IMT, and 6 months after intervention. RESULTS: After an 8-week training, respiratory strength and functional capacity increased in both groups. Six months after finishing the intervention, these benefits were lost. Inspiratory strength decreased in the high-loads group but was 32% higher than initial values (p = 0.007); in the low-loads group, patients lost 41% (p = 0.015). Improvements were higher in the high-loads group after training (p = 0.000) and follow-up (p = 0.002). Functional capacity was similar to initial values in both groups. CONCLUSION: The improvements achieved with IMT in elderly women disappeared 6 months after the end of training. High loads were more effective to keep inspiratory strength than low loads.

Using regression and Multifactorial Analysis of Variance to assess the effect of ascorbic, citric, and malic acids on spores and activated spores of Alicyclobacillusacidoterrestris.

The type strain of the species Alicyclobacillus acidoterrestris (DSM 3922) and the strain CB1 (accession number: KP144333) were studied in this research to assess the effects of three weak acids (malic, citric, and ascorbic acids), pH (3 or 4), and spore status (spores, and activated spores). Acids were used to prepare 7 different blends, and the blends used to reduce the pH of Malt Extract broth to 3 and 4; then, media were inoculated with spores or activated spores, stored at 45 °C (optimal temperature for A. acidoterrestris growth), and analyzed immediately and after 2 and 7 days. Data were preliminary standardized as increase/reduction of microbial population, compared to the initial concentration, and modelled through two different statistical approaches (multifactorial ANOVA, and multiple regression). Finally, a binary code (0-no growth or reduction of viable count; 1-growth) was used to perform a multiple regression analysis on the growth probability of A. acidoterrestris. Generally, ascorbic acid was the most effective compound, but other acids (e.g., malic acid) could contribute to increase the inactivation ratio; concerning spore status, the highest sensitivity of activated spores suggests that acids probably act during the outgrowing phase. Finally, the two strains showed different trends at pH 3.0, being the type strain the most resistant one.

Isotopologue pattern based data mining for selenium species from HILIC-ESI-Orbitrap-MS-derived spectra.

Automated and specific picking of selenium-containing molecular entities has not been an obvious option for software tools associated with electrospray high-resolution mass spectrometry (MS). In our study, a comprehensive pattern matching approach based on intra-isotopologue distance and isotopologue ratio data was critically evaluated in terms of reproducibility and selenium isotope selection on three samples, including selenized Torula yeast and the selenium hyperaccumulator plant Cardamine violifolia. Hydrophilic interaction liquid chromatography was applied to provide a one-step separation for water soluble metabolites to put an end to the need for either orthogonal setups or poor retention on reversed phase chromatography. Assistance from inductively coupled plasma-MS was taken only for chromatographic verification purposes, and the involvement of absolute mass defect (MD) data in selenometabolite-specific screening was assessed by multivariate statistical tools. High focus was placed on screening efficiency and on the validation of discovered selenized molecules to avoid reporting of artefacts. From the >1000 molecular entries detected, selenium-containing molecules were picked up with a recovery rate of >88% and a false positive rate of <10%. Isotop(ologu)e pairs of 78Se-80Se and 80Se-82Se proved to be the most performant in the detection. On the basis of accurate mass information and hypothetical deamination processes, elemental composition could be proposed for 72 species out of the 75 selenium species encountered without taking into account selenocompound databases. Absolute MD data were used to significantly differentiate a potentially sample-specific subgroup of false positive molecular entities from non-selenized and selenized entities.

Beyond ANOVA and MANOVA for repeated measures: Advantages of generalized estimated equations and generalized linear mixed models and its use in neuroscience research.

In neuroscience research, longitudinal data are often analysed using analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) for repeated measures (rmANOVA/rmMANOVA). However, these analyses have special requirements: The variances of the differences between all possible pairs of within-subject conditions (i.e., levels of the independent variable) must be equal. They are also limited to fixed repeated time intervals and are sensitive to missing data. In contrast, other models, such as the generalized estimating equations (GEE) and the generalized linear mixed models (GLMM), suggest another way to think about the data and the studied phenomenon. Instead of forcing the data into the ANOVAs assumptions, it is possible to design a flexible/personalized model according to the nature of the dependent variable. We discuss some advantages of GEE and GLMM as alternatives to rmANOVA and rmMANOVA in neuroscience research, including the possibility of using different distributions for the parameters of the dependent variable, a better approach for different time length points, and better adjustment to missing data. We illustrate these advantages by showing a comparison between rmANOVA and GEE in a real example and providing the data and a tutorial code to reproduce these analyses in R. We conclude that GEE and GLMM may provide more reliable results when compared to rmANOVA and rmMANOVA in neuroscience research, especially in small sample sizes with unbalanced longitudinal designs with or without missing data.

A New Aculodes Species (Prostigmata: Eriophyidae) Described from an Invasive Weed by Morphological, Morphometric and DNA Barcode Analyses.

A new species of eriophyoid mite, Aculodes marcelli sp. nov., was discovered on cheatgrass, Anisantha tectorum (L.) Nevski (syn. Bromus tectorum L.), an annual grass that is native to Eurasia and Northern Africa. This grass was introduced to North America near the end of the 19th century and now is widespread and associated with the observed increases in the size, frequency, and intensity of wildfires in western N. America. In this paper, A. marcelli sp. nov., is morphologically described and illustrated. Compared with other Aculodes spp., it differs based on morphology and the sequence of the mitochondrial cytochrome oxidase gene, subunit I (MT-CO1). Results of morphometric analysis showed clear differentiation between A. marcelli sp. nov., and the most similar congener, A. altamurgiensis from Taeniatherum caput-medusae. Analysis of MT-CO1 sequence divergence revealed significant levels of genetic variation (17.7%) and supported the results from the morphometric analysis; therefore, it is determined that they are two different species. Aculodes marcelli sp. nov., is a new candidate agent for classical biological control of A. tectorum.

Combined Effects of Temperature and Salinity Affect the Surviv-Al of Asian Green Mussel (Perna viridis) through Digestive and Antioxidant Performance.

Changes in temperature and salinity of the marine environment fluctuate continuously, and the effects of these changes on shellfish survival are significant. In this study, the survival rate of adult Asian green mussels (Perna viridis) was measured after short-term treatments (7 days) for a range of temperature (20 °C, 25 °C, 30 °C) and salinity (23‱, 28‱, 33‱). The digestive (amylase, lipase, trypsin and pepsin activities) and antioxidant performance (SOD and GPX gene expression; SOD, GPX and CAT activities; GSH and MDA Concentration) were measured and MANOVA results were obtained using a generalized linear model between certain factors (temperature (T), salinity (S) and temperature × salinity (T × S)) and survival rate. Both T and T × S significantly affected the survival rate of Asian green mussels. The overall relationship between the survival rate of P. viridis and T and S within the experimental range can be summarised by the equation: ln(Survival rate) = 54.9282 - 2.5627 × T - 3.6180 × S + 0.1857 × T × S + 0.0156 × T2 + 0.0520 × S2 - 0.0012 × T2 × S - 0.0023 × T × S2. The optimal temperature-salinity combination was 23.698 °C/30.760‱. T, S and T × S all had a significant impact on amylase and lipase activities of Asian green mussels. In the hepatopancreas, gill, mantle, adductor muscle, gonad and foot tissues of Asian green mussel, the SOD and GPX gene expression were responsive to temperature-salinity changes. The antioxidant performance of the combinations far from the optimal temperature-salinity combination were significantly improved. Our results suggest that combined temperature and salinity effects have a regular impact on the survival of Asian green mussels and that there is a link between survival and digestive and antioxidant performance.

Multivariate nonparametric methods in two-way balanced designs: performances and limitations in small samples.

Investigations of multivariate population are pretty common in applied researches, and the two-way crossed factorial design is a common design used at the exploratory phase in industrial applications. When assumptions such as multivariate normality and covariance homogeneity are violated, the conventional wisdom is to resort to nonparametric tests for hypotheses testing. In this paper we compare the performances, and in particular the power, of some nonparametric and semi-parametric methods that have been developed in recent years. Specifically, we examined resampling methods and robust versions of classical multivariate analysis of variance (MANOVA) tests. In a simulation study, we generate data sets with different configurations of factor's effect, number of replicates, number of response variables under null hypothesis, and number of response variables under alternative hypothesis. The objective is to elicit practical advice and guides to practitioners regarding the sensitivity of the tests in the various configurations, the tradeoff between power and type I error, the strategic impact of increasing number of response variables, and the favourable performance of one test when the alternative is sparse. A real case study from an industrial engineering experiment in thermoformed packaging production is used to compare and illustrate the application of the various methods.

Putting the Mantel test back together again.

The Mantel test has been widely used in ecology and evolution, but over the last two decades it has been frequently critiqued because results were inconsistent with expectations and there were issues with Type I (false-positive) and Type II (false-negative) error rates. Three-matrix extensions of the Mantel test have been challenged for similar reasons. Even the null hypotheses underlying the Mantel test have been questioned. As a result, use of the Mantel test and its variants has been discouraged or limited to special situations. Here, we examine Mantel test criticisms including the lack of agreement between traditional variable-based Pearson correlations (r) and observation-based Mantel correlations (rm ), and the unusual Type I and Type II error rates. We propose an alternate proximity measure that resolves these issues. We use simulations and examples to contrast Mantel results based on Euclidean distance, squared Euclidean distance, and the simple difference (Diff) with traditional bivariate Pearson correlations. We demonstrate that use of the simple difference in Mantel tests can resolve the underlying problems with poor agreement between bivariate Pearson and Mantel correlations, as well as appropriate Type I and Type II errors (i.e., where r = cor(x,y) and rm = cor(dx ,dy ), if dx = Diff(x) and dy = Diff(y), r = rm ). We also show that the simple difference can provide solutions to issues with partial Mantel tests and distance-based MANOVA. Because our results resolve many of the issues with Mantel tests, we hope that these findings will restore the popularity of the Mantel test.

The multivariate analysis of variance as a powerful approach for circular data.

BACKGROUND: A broad range of scientific studies involve taking measurements on a circular, rather than linear, scale (often variables related to times or orientations). For linear measures there is a well-established statistical toolkit based on linear modelling to explore the associations between this focal variable and potentially several explanatory factors and covariates. In contrast, statistical testing of circular data is much simpler, often involving either testing whether variation in the focal measurements departs from circular uniformity, or whether a single explanatory factor with two levels is supported. METHODS: We use simulations and example data sets to investigate the usefulness of a MANOVA approach for circular data in comparison to commonly used statistical tests. RESULTS: Here we demonstrate that a MANOVA approach based on the sines and cosines of the circular data is as powerful as the most-commonly used tests when testing deviation from a uniform distribution, while additionally offering extension to multi-factorial modelling that these conventional circular statistical tests do not. CONCLUSIONS: The herein presented MANOVA approach offers a substantial broadening of the scientific questions that can be addressed statistically using circular data.