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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.
PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments
(2022)
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.
Identification of a super-functional Tfh-like subpopulation in murine lupus by pattern perception
(2020)
Dysregulated cytokine expression by T cells plays a pivotal role in the pathogenesis of autoimmune diseases. However, the identification of the corresponding pathogenic subpopulations is a challenge, since a distinction between physiological variation and a new quality in the expression of protein markers requires combinatorial evaluation. Here, we were able to identify a super-functional follicular helper T cell (Tfh)-like subpopulation in lupus-prone NZBxW mice with our binning approach "pattern recognition of immune cells (PRI)". PRI uncovered a subpopulation of IL-21(+) IFN-gamma(high) PD-1(low) CD40L(high) CXCR5(-) Bcl-6(-) T cells specifically expanded in diseased mice. In addition, these cells express high levels of TNF-alpha and IL-2, and provide B cell help for IgG production in an IL-21 and CD40L dependent manner. This super-functional T cell subset might be a superior driver of autoimmune processes due to a polyfunctional and high cytokine expression combined with Tfh-like properties.
Life history theory predicts that experiencing stress during the early period of life will result in accelerated growth and earlier maturation. Indeed, animal and some human studies documented a faster pace of growth in the offspring of stressed mothers. Recent advances in epigenetics suggest that the effects of early developmental stress might be passed across the generations. However, evidence for such intergenerational transmission is scarce, at least in humans. Here we report the results of the study investigating the association between childhood trauma in mothers and physical growth in their children during the first months of life. Anthropometric and psychological data were collected from 99 mothers and their exclusively breastfed children at the age of 5 months. The mothers completed the Early Life Stress Questionnaire to assess childhood trauma. The questionnaire includes questions about the most traumatic events that they had experienced before the age of 12 years. Infant growth was evaluated based on the anthropometric measurements of weight, length, and head circumference. Also, to control for the size of maternal investment, the composition of breast milk samples taken at the time of infant anthropometric measurements was investigated. The children of mothers with higher early life stress tended to have higher weight and bigger head circumference. The association between infant anthropometrics and early maternal stress was not affected by breast milk composition, suggesting that the effect of maternal stress on infant growth was independent of the size of maternal investment. Our results demonstrate that early maternal trauma may affect the pace of growth in the offspring and, in consequence, lead to a faster life history strategy. This effect might be explained via changes in offspring epigenetics.
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.
Human growth data analysis and statistics – the 5th Gülpe International Student Summer School
(2023)
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.
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.
No correlation between short term weight gain and lower leg length gain in healthy German children
(2020)
Background:
Length-for-age is considered the indicator of choice in monitoring the long-term impact of chronic nutritional deficiency. Aim: We hypothesized that short term increments of body weight cross-correlate with increments of the lower leg length.
Sample and methods:
We re-analyzed the association between weekly measurements of weight and of lower leg length in 34 healthy German children, aged 2.9-15.9 years. The data are a subset of measurements originally published in 1988 (Hermanussen et al. 1988a). As the growth measurements were often not equally spaced in time due to interposed holidays and illness, the incremental rates for weight and lower leg length were smoothed using spline functions. Autocorrelation and cross-correlation functions were calculated for weight increments and lower leg length increments.
Results:
Height and weight increments are pulsatile. Autocorrelations indicated that mini growth spurts occur at irregular intervals. Lack of cross-correlations between weight and lower leg length indicated that mini spurts in weight gain do not coincide with mini spurts in length gain even when considering lag times of up to 10 weeks. Short term changes of weight gain and lower leg length gain in healthy children show no temporal association.
In nature, dominance is often shown by body size; even in humans many studies report that social status is associated with body height. In today's society, educational status is an important factor for social classification. Since growing children do not have their own educational or social status, they are often affected by the status of their parents. Therefore, the question appears, whether parental educational status measurably affects the growth of a child. If so, is this explainable by the nutritional factors? To test this hypothesis, seven different Indian data sets where reexamined using the St. Nicolas House Analysis. The results show a direct association between parental education and body height (hSDS) of the child, but there was no influence of parental education on the nutritional status. We conclude that education has a direct effect on height that is not mediated via nutrition.