@article{HermanussenSchefflerPulunganetal.2023, author = {Hermanussen, Michael and Scheffler, Christiane and Pulungan, Aman B. and Bandyopadhyay, Arup Ratan and Ghosh, Jyoti Ratan and {\"O}zdemir, Ay{\c{s}}eg{\"u}l and Koca {\"O}zer, Ba{\c{s}}ak and Musalek, Martin and Lebedeva, Lidia and Godina, Elena and Bogin, Barry and Tutkuviene, Janina and Budrytė, Milda and Gervickaite, Simona and Limony, Yehuda and Kirchengast, Sylvia and Buston, Peter and Groth, Detlef and R{\"o}sler, Antonia and Gasparatos, Nikolaos and Erofeev, Sergei and Novine, Masiar and Navazo, B{\´a}rbara and Dahinten, Silvia and Gomuła, Aleksandra and Nowak-Szczepańska, Natalia and Kozieł, Sławomir}, title = {Environment, social behavior, and growth}, series = {Human biology and public health}, volume = {1}, journal = {Human biology and public health}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {2748-9957}, doi = {10.52905/hbph2023.1.59}, pages = {14}, year = {2023}, abstract = {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.}, language = {en} } @article{GrothSchefflerHermanussen2023, author = {Groth, Detlef and Scheffler, Christiane and Hermanussen, Michael}, title = {Human growth data analysis and statistics - the 5th G{\"u}lpe International Student Summer School}, series = {Human biology and public health}, volume = {1}, journal = {Human biology and public health}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {2748-9957}, doi = {10.52905/hbph2023.1.70}, pages = {5}, year = {2023}, abstract = {The Summer School in G{\"u}lpe (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 G{\"u}lpe 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.}, language = {en} } @article{HakeBodenbergerGroth2023, author = {Hake, Tim and Bodenberger, Bernhard and Groth, Detlef}, title = {In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks}, series = {Human biology and public health}, volume = {1}, journal = {Human biology and public health}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {2748-9957}, doi = {10.52905/hbph2023.1.63}, pages = {16}, year = {2023}, abstract = {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.}, language = {en} }