Twenty-nine scientists met for the annual Auxological conference, held in Horoměřice near Prague, Czech Republic, and discussed aspects of environment and politics related to human growth using data from various European countries, India, and Sub-Saharan Africa. Exposures to Social, Economic, Political, and Emotional (SEPE) factors were associated with physical growth, health, and aging. Secular changes were associated with socio-economic gradients in conscripts, addressing political views, feelings of political liberation, and illusions of equity and freedom. New BMI charts were presented to better interpret BMI trends over time, and new blood pressure charts. The need for context-specific national screening standards was discussed. Mating preferences were examined in light of cultural ecology and personal ideology, and changes in birth weight and the sex ratio were examined in relation to socio-economic factors, political events, and COVID-19, as well as studies on cognitive and motor functions in preschool children, the influence of fine motor skills, visual attention, and visuomotor coordination on school readiness. Aspects of maturation in adolescent field hockey players were addressed, and obesity and health risks among Czech Artillery soldiers. The influence of alcohol and tobacco use on bone quality was discussed in view of physical activity and body composition, and statistical improvements to visualize causality in association chains to better understand the multiple interactions within biological networks. The meeting also addressed an evolutionary perspective on human pelvic size, work on body height in late Geometric-Roman and Medieval populations, aspects of biotechnology in conjunction with AI.

Background Resource disparities are common in social networks, often driven by competitive interactions. Exploring how interventions like taxation influence these inequalities can reveal mechanisms for more balanced distributions. Objectives This study investigates the effects of a 10% tax rate and redistribution on inequality and resource stability within two network models: the ‘Winner-Loser Model’ which intensifies hierarchies through competitive interactions, and the ‘Null Model’, simulating equal opportunity exchanges. Sample and Methods We used Monte Carlo simulations with agents starting at equal resource levels, interacting under the rules of each model. Taxation effects were measured through Gini coefficients and Lambda stability scores across various network sizes. Results Taxation reduced Gini coefficients in both models, promoting more balanced distributions. Lambda values indicated that taxation improved stability, especially within the ‘Winner-Loser Model’, by diminishing extreme resource accumulation. Conclusions The study demonstrates that while competitive dynamics naturally drive inequality, taxation and redistribution mechanisms can stabilize and reduce disparities. These findings suggest that even simple redistribution can reduce hierarchical resource concentration and counteract extreme inequalities in networked settings.

Background Anterior cruciate ligament rupture (ACL-R) is a common sports injury influenced by biomechanical, anthropometric, environmental, and genetic factors. Collagen gene polymorphisms have been implicated in ACL-R, with a whole-genome sequencing twin study highlighting COL12A1 rs970547 C>T as a variant of interest. However, the additive impact of the anthropometric traits and COL12A1 on ACL-R susceptibility remains unexplored. Objectives To investigate the additive effect of anthropometric traits and COL12A1 rs970547 C>T on ACL-R susceptibility in an Australian and South African cohort with the a priori hypothesis that female T/T carriers were at an increased risk. Sample and Methods The study included ACL-R cases (Australia n = 354; South Africa n = 252) and controls (Australia n = 84; South Africa n = 232). COL12A1 rs970547 C>T SNP was genotyped using TaqMan® assays. Anthropometric traits were sex-stratified/standardised. Logistic regression and principal component analyses were assessed. Results No significant genetic associations were found for COL12A1 rs970547 C>T in the i) individual/combined and ii) male/female cohorts. PCA revealed clustering of anthropometrics in PC1–PC2, with PC3 being driven exclusively by rs970547 in each cohort. Conclusion No associations were noted between the COL12A1 rs970547 T/T genotype and ACL-R risk. PCA, however, indicated that rs970547 may hold biological significance in ACL-R susceptibility, highlighting the complex interplay of genetic and anthropometric traits.

In the second part of this tutorial series, we will discuss the foundations of univariate statistics. We will define statistical terms such as p-value, confidence interval, and effect size. The latter two are often neglected, but they are more informative than the commonly overinterpreted p-value. A p-value expresses the likelihood of observing a difference, or a larger, between a sample and a population parameter, or between two groups, assuming they share the same distribution. In univariate statistics, this difference is between a sample and a known population value. The assumption of equal distributions is the null hypothesis, which may be rejected if the p-value is very small. Importantly, the p-value depends not only on the difference itself but also on the sample size. Large samples can yield very small, highly significant p-values even when the effect size is trivial. In contrast, a confidence interval provides a range for the population parameter based on the sample, giving insight into precision beyond statistical significance. The effect size conveys the magnitude of an effect, which simplifies interpretation and allows to assess the relevance of observed differences. In this review, we explain these terms in the context of univariate statistics, comparing variables of interest from sample data against a known population value such as a mean or proportion. Relevant measures and visualizations include the mean, standard deviation, coefficient of variation, and histograms, barplots, along with effect size measures like Cohen’s d for numerical data and Cohen’s w for categorical data.

Statistics is the key skill required by reseachers to answer scientific questions based on often complex data sets. 9 students and young researchers from Indonesia, South Africa, Spain, Slovakia, and Germany were educated and trained on their own data sets with the aim to publish these results at the 7th International Students Summer School on Human Growth - Data Analysis and Statistics at the Ecological Station Gülpe of the University of Potsdam from July 3rd to 11th 2024.

BACKGROUND:Long-term improvements in physical living conditions correlate with long-term trends in height. AIM:To link temporal characteristics of the secular trend in height with the simultaneous political and economic dynamics. SAMPLE AND METHODS:Height of men of the German Armed Forces born between 1865 and 1975 was correlated with indicators of economic prosperity (GDP), nutrition and health (infant mortality), and indicators of social inhomogeneity (income inequality and household wealth share). The time periods before 1916, between 1916 and 1933, 1947, 1973, and after 1989 were separately analyzed. Coherence analysis was used to assess the changes in the temporal trends. RESULTS:Mean height of young adult men increased by 0.45 mm/year (before 1916), by 2.15 mm/year (1916-1933), by 1.87 mm/year in the early Federal Republic of Germany (FRG) until 1973, by 1.45 mm/year in the late FRG, and by 4 mm/year in East German conscripts after the reunification in 1989. The most substantial height increments occurred in periods of political upheaval and loss of state authority. CONCLUSION:The nonlinear pattern of secular height increments in Germany since the late 19th century suggests that political liberation, hope for a better life, and illusions of equity, freedom, justice, and the expectation of social advancement are associated with competitive growth, strategic growth adjustments, and finally, long-term and substantial secular trends in height.

The St. Nicolas House Analysis (SNHA) is a new graph estimation method for detection of extensive interactions among variables. It operates by ranking absolute bivariate correlation coefficients in descending order thereby creating hierarchic association chains. The latter characterizes dependence structures of interacting variables which can be visualized in a corresponding network graph as a chain of end-to-end connected edges representing direct relationships between the connected nodes. The important advantage of this relatively new approach is that it produces less false positive edges resulting from indirect or transitive associations than expected with standard correlation or linear model-based approaches. Here we aim to improve the detection of ramifications in graphs by addition of different data processing layers to SNHA. They include the combinations of the extensions R-squared gaining(RSG) and linear model check(LMC). SNHA together with these so-called extensions were benchmarked against default SNHA and other reference methods available for the programming language R. In the end combinations of RSG, LMC and Bootstrapping improve SNHA performance across different network types, albeit at the cost of longer computation time.

Background The phenomenon of human migration is multi-dimensional, involving economic, political, cultural and environmental factors; it operates with 'push-pull' dynamics and structures the immigrant population in terms of behaviour, social dynamics, dietary patterns, growth trajectories, reproductive strategies and overall well-being. Since the 1960s, Turkish immigrants have settled mainly in Germany and the Netherlands. Objectives The study aims to understand the changes in anthropometric variables that have occurred among adult Turkish immigrants due to migration, as well as to identify the factors affecting their body image and height. Sample The cross-sectional study included a random sample (aged 18-65 years) of 190 Turkish immigrants (73 males, 117 females) living in Germany and the Netherlands, and 278 non-migrant individuals (120 males, 158 females) living in Turkey. Methods Anthropometric data on height, weight, circumferences, and skinfold thickness were collected, and a body image survey was administered. In addition to descriptive statistics, a principal component analysis (PCA) and a linear regression model were conducted. Results No statistical difference was found between the height of Turkish immigrants in Germany and the Netherlands, and those living in Turkey for males. Overweight was more prevalent in Turkish immigrant groups than in those living in Turkey. Comparison of Turkish immigrants and non-immigrants showed that both male and female living in Europe had higher body image scores. Conclusion The height differences between Turkish immigrants and those still living in Turkey may result from the insufficient integration of immigrants into the new society. It was found that educational level positively affects the height of both sexes in both Turkish immigrants and non-immigrants.

For many researchers in the field of human biology, statistics is a foreign territory where they feel uncomfortable because of statistical vocabulary used and the many different ways of analyzing their data. In a series of five short review articles, I will try to help these researchers to understand the basic principles of data preparation, descriptive and inferential statistics to guide them in analyzing their data. The review series should be seen as a complement to the summer school lectures of the International Summer School of the University of Potsdam in Gülpe in the state of Brandenburg, Germany. In this first tutorial we discuss the role of statistics in research and the role why a statistical tool like R should be used and what are the main considerations before we actually should start analyzing the data.

Background Serial public health data may or may not reflect living conditions, political background and/or certain targeted health interventions. Yet, the effect of political events and/or health interventions that only last for a few years or a single legislative period may be difficult to analyze as restricting the range of the variables, i.e. the time interval within which the data were obtained, restricts the power of correlation analyzes. Objectives to provide a method to eliminate linear trends from serially obtained data and to visualize agreement between these data. Method We combine information of both the X- and the Y-axis and assess the agreement (coherence) between variables by clockwise (positive correlation) or anticlockwise (negative correlation) rotation of the coordinates. We provide an illustrative historic example of the coherence of infant mortality as an indicator of public health and body height in Germany between 1885 and 1995. Results Calculating coherence between correlating variables eliminates linear trends and leaves residuals that correspond to local correlation coefficients. The substantial changes in the coherence pattern of infant mortality and height exemplify the changes in the interaction between these variables during the transition from late feudalism to modern democracy. Conclusion Assessing coherence patterns enables a sensitive assessment of inhomogeneity and temporal trend changes in serially obtained correlated variables.

The St. Nicolas House algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.

Twenty-seven scientists met for the annual Auxological conference held at Aschau, Germany, to particularly discuss the interaction between social factors and human growth, and to highlight several topics of general interest to the regulation of human growth. Humans are social mammals. Humans show and share personal interests and needs, and are able to strategically adjust size according to social position, with love and hope being prime factors in the regulation of growth. In contrast to Western societies, where body size has been shown to be an important predictor of socioeconomic status, egalitarian societies without formalized hierarchy and material wealth-dependent social status do not appear to similarly integrate body size and social network. Social network structures can be modeled by Monte Carlo simulation. Modeling dominance hierarchies suggests that winner-loser effects play a pivotal role in robust self-organization that transcends the specifics of the individual. Further improvements of the St. Nicolas House analysis using re-sampling/bootstrap techniques yielded encouraging results for exploring dense networks of interacting variables. Customized pediatric growth references, and approaches towards a Digital Rare Disease Growth Chart Library were presented. First attempts with a mobile phone application were presented to investigate the associations between maternal pre-pregnancy overweight, gestational weight gain, and the child’s future motor development. Clinical contributions included growth patterns of individuals with Silver-Russell syndrome, and treatment burden in children with growth hormone deficiency. Contributions on sports highlighted the fallacy inherent in disregarding the biological maturation status when interpreting physical performance outcomes. The meeting explored the complex influence of nutrition and lifestyle on menarcheal age of Lithuanian girls and emphasized regional trends in height of Austrian recruits. Examples of the psychosocial stress caused by the forced migration of modern Kyrgyz children and Polish children after World War II were presented, as well as the effects of nutritional stress during and after World War I. The session concluded with a discussion of recent trends in gun violence affecting children and adolescents in the United States, and aspects of life history theory using the example of "Borderline Personality Disorder." The features of this disorder are consistent with the notion that it reflects a "fast" life history strategy, with higher levels of allostatic load, higher levels of aggression, and greater exposure to both childhood adversity and chronic stress. The results were discussed in light of evolutionary guided research. In all contributions presented here, written informed consent was obtained from all participants in accordance with institutional Human investigation committee guidelines in accordance with the Declaration of Helsinki amended October 2013, after information about the procedures used.

Background: National Health Surveys have been part of national health services in many countries, but their data are summary com- pilations and commonly used only for describing trends in health and living conditions. Aim: Tostatisticallydisclosenetworksofinteractingvariableswithin National Health Survey data. Sample and methods: We used anthropometric, educational, environ- mental and economic information of people of Sikkim, West Bengal, Telangana, and Gujarat, India, obtained by the Fifth Indian National Family Health Survey (NFHS-5).We applied a new statistical approach labeled as “St. Nicholas House Analysis” (SNHA). SNHA ranks absolute bivariate correlation coef- ficients in descending order according to magnitude. The method creates hierarchic “association chains” of correlation coefficients de- fined by sequences where reversing the start and end point does not alter the ordering of elements. Association chains characterize de- pendence structures within networks of extensively interacting variables. Results: SNHA disclosed fundamental differences in the network of anthropometric, educational, environmental and economic variables of the people of Sikkim, and the people of West Ben- gal, Telangana and Gujarat. Whereas relevant interactions among these variables were largely absent in the people of Sikkim, the variables formed characteristic star-shaped networks with wealth quintile and the possession of motorcycles in a strong central position, in the people of West Bengal, Telangana and Gujarat. Conclusion: Depicting association chains within net- works of extensively interacting variables such as health survey data appears to be a promising statisti- cal tool for disentangling the effects of environmen- tal circumstances, education, and social, economic, political and emotional (SEPE) factors on human growth.

Animal societies are structured of dominance hierarchy (DH). DH can be viewed as networks and analyzed by graph theory. We study the impact of state-dependent feedback (winner-loser effect) on the emergence of local dominance structures after pairwise contests between initially equal-ranking members (equal resource-holding-power, RHP) of small and large social groups. We simulated pairwise agonistic contests between individuals with and without a priori higher RHP by Monte-Carlo-method. Random pairwise contests between equal-ranking competitors result in random dominance structures (‘Null variant’) that are low in transitive triads and high in pass along triads; whereas state-dependent feedback (‘Winner-loser variant’) yields centralized ‘star’ structured DH that evolve from competitors with initially equal RHP and correspond to hierarchies that evolve from keystone individuals. Monte-Carlo simulated DH following state-dependent feedback show motif patterns very similar to those of a variety of natural DH, suggesting that state-dependent feedback plays a pivotal role in robust self-organizing phenomena that transcend the specifics of the individual. Self-organization based on state-dependent feedback leads to social structures that correspond to those resulting from pre-existing keystone individuals. As the efficiency of centralized social networks benefits both, the individual and the group, centralization of social networks appears to be an important evolutionary goal.

Twenty-four scientists met for the annual Auxological conference held at Krobielowice castle, Poland, to discuss the diverse influences of the environment and of social behavior on growth following last year’s focus on growth and public health concerns (Hermanussen et al., 2022b). Growth and final body size exhibit marked plastic responses to ecological conditions. Among the shortest are the pygmoid people of Rampasasa, Flores, Indonesia, who still live under most secluded insular conditions. Genetics and nutrition are usually considered responsible for the poor growth in many parts of this world, but evidence is accumulating on the prominent impact of social embedding on child growth. Secular trends not only in the growth of height, but also in body proportions, accompany the secular changes in the social, economic and political conditions, with major influences on the emotional and educational circumstances under which the children grow up (Bogin, 2021). Aspects of developmental tempo and aspects of sports were discussed, and the impact of migration by the example of women from Bangladesh who grew up in the UK. Child growth was considered in particular from the point of view of strategic adjustments of individual size within the network of its social group. Theoretical considerations on network characteristics were presented and related to the evolutionary conservation of growth regulating hypothalamic neuropeptides that have been shown to link behavior and physical growth in the vertebrate species. New statistical approaches were presented for the evaluation of short term growth measurements that permit monitoring child growth at intervals of a few days and weeks.

The Summer School in Gülpe (Ecological Station of the University of Potsdam) offers an exceptional learning opportunity for students to apply their knowledge and skills to real-world problems. With the guidance of experienced human biologists, statisticians, and programmers, students have the unique chance to analyze their own data and gain valuable insights. This interdisciplinary setting not only bridges different research areas but also leads to highly valuable outputs. The progress of students within just a few days is truly remarkable, especially when they are motivated and receive immediate feedback on their questions, problems, and results. The Summer School covers a wide range of topics, with this year’s focus mainly on two areas: understanding the impact of socioeconomic and physiological factors on human development and mastering statistical techniques for analyzing data such as changepoint analysis and the St. Nicolas House Analysis (SNHA) to visualize interacting variables. The latter technique, born out of the Summer School’s emphasis on gaining comprehensive data insights and understanding major relationships, has proven to be a valuable tool for researchers in the field. The articles in this special issue demonstrate that the Summer School in Gülpe stands as a testament to the power of practical learning and collaboration. Students who attend not only gain hands-on experience but also benefit from the expertise of professionals and the opportunity to engage with peers from diverse disciplines.

The Western honey bee Apis mellifera, which provides about 90% of commercial pollination, is under threat from diverse abiotic and biotic factors. The ectoparasitic mite Varroa destructor vectoring deformed wing virus (DWV) has been identified as the main biotic contributor to honey bee colony losses worldwide, while the role of the microsporidium Nosema ceranae is still controversially discussed. In an attempt to solve this controversy, we statistically analyzed a unique data set on honey bee colony health collected from a cohort of honey bee colonies over 15 years and comprising more than 3000 data sets on mite infestation levels, Nosema spp. infections, and winter losses. Multivariate statistical analysis confirms that V. destructor is the major cause of colony winter losses. Although N. ceranae infections are also statistically significantly correlated with colony losses, determination of the effect size reveals that N. ceranae infections are of no or low biological relevance.

Background: Network models are useful tools for researchers to simplify and understand investigated systems. Yet, the assessment of methods for network construction is often uncertain. Random resampling simulations can aid to assess methods, provided synthetic data exists for reliable network construction. Objectives: We implemented a new Monte Carlo algorithm to create simulated data for network reconstruction, tested the influence of adjusted parameters and used simulations to select a method for network model estimation based on real-world data. We hypothesized, that reconstructs based on Monte Carlo data are scored at least as good compared to a benchmark. Methods: Simulated data was generated in R using the Monte Carlo algorithm of the mcgraph package. Benchmark data was created by the huge package. Networks were reconstructed using six estimator functions and scored by four classification metrics. For compatibility tests of mean score differences, Welch’s t-test was used. Network model estimation based on real-world data was done by stepwise selection. Samples: Simulated data was generated based on 640 input graphs of various types and sizes. The real-world dataset consisted of 67 medieval skeletons of females and males from the region of Refshale (Lolland) and Nordby (Jutland) in Denmark. Results: Results after t-tests and determining confidence intervals (CI95%) show, that evaluation scores for network reconstructs based on the mcgraph package were at least as good compared to the benchmark huge. The results even indicate slightly better scores on average for the mcgraph package. Conclusion: The results confirmed our objective and suggested that Monte Carlo data can keep up with the benchmark in the applied test framework. The algorithm offers the feature to use (weighted) un- and directed graphs and might be useful for assessing methods for network construction.

Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4+T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.