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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.
Background: In the animal kingdom body size is often linked to dominance and subsequently the standing in social hierarchy. Similarly, human growth has been associated and linked to socioeconomic factors, including one’s social status. This has already been proposed in the early 1900s where data on young German school girls from different social strata have been compared.
Objectives: This paper aims to summarize and analyze these results and make them accessible for non-German speakers. The full English translation of the historic work of Dikanski (Dikanski, 1914) is available as a supplement. Further, this work aims to compare the historical data with modern references, to test three hypotheses: (1) higher social class is positively associated with body height and weight, (2) affluent people from the used historical data match modern references in weight and height and (3) weight distributions are skewed in both modern and historical populations.
Methods: Comparison of historical data from 1914 with WHO and 1980s German data. The data sets, for both body weight and height for 6.0- and 7.0-year-old girls, were fitted onto centile curves and quantile correlation coefficients were calculated.
Results: In historical data social status is positively associated with body height and weight while both are also normally distributed, which marks a significant difference to modern references.
Conclusion: Social status is positively associated with height, signaling social dominance, making children of affluent classes taller. Children from the historical data do not reach the average height of modern children, even under the best environmental conditions. The children of the upper social class were not skewed in weight distribution, although they had the means to become as obese as modern children.
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.