TY - JOUR A1 - Scheffler, Christiane A1 - Rogol, Alan D. A1 - Iancu, Mirela A1 - Hanc, Tomasz A1 - Moelyo, Annang Giri A1 - Suchomlinov, Andrej A1 - Lebedeva, Lidia A1 - Limony, Yehuda A1 - Musalek, Martin A1 - Veldre, Gudrun A1 - Godina, Elena Z. A1 - Kirchengast, Sylvia A1 - Mumm, Rebekka A1 - Groth, Detlef A1 - Tutkuviene, Janina A1 - Böker, Sonja A1 - Ozer, Basak Koca A1 - Navazo, Barbara A1 - Spake, Laure A1 - Koziel, Slawomir A1 - Hermanussen, Michael T1 - Growth during times of fear and emotional stress BT - Proceedings of the 28th Aschauer Soiree, held at Potsdam, Germany, and online, November 14th 2020 JF - Human biology and public health N2 - Twenty-one scientists met for this year’s virtual conference on Auxology held at the University Potsdam, Germany, to discuss child and adolescent growth during times of fear and emotional stress. Growth within the broad range of normal for age and sex is considered a sign of good general health whereas fear and emotional stress can lead to growth faltering. Stunting is a sign of social disadvantage and poor parental education. Adverse childhood experiences affect child development, particularly in families with low parental education and low socioeconomic status. Negative effects were also shown in Indian children exposed prenatally and in early postnatal life to the cyclone Aila in 2009. Distrust, fears and fake news regarding the current Corona pandemic received particular attention though the effects generally appeared weak. Mean birth weight was higher; rates of low, very and extremely low birth weight were lower. Other topics discussed by the participants, were the influences of economic crises on birth weight, the measurement of self-confidence and its impact on growth, the associations between obesity, peer relationship, and behavior among Turkish adolescents, height trends in Indonesia, physiological neonatal weight loss, methods for assessing biological maturation in sportsmen, and a new method for skeletal age determination. The participants also discussed the association between acute myocardial infarction and somatotype in Estonia, rural-urban growth differences in Mongolian children, socio-environmental conditions and sexual dimorphism, biological mortality bias, and new statistical techniques for describing inhomogeneity in the association of bivariate variables, and for detecting and visualizing extensive interactions among variables. KW - stunting KW - birth weight KW - fear KW - emotional stress KW - economy KW - SEPE Y1 - 2021 U6 - https://doi.org/10.52905/hbph.v2.15 SN - 2748-9957 IS - 2 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Hermanussen, Michael A1 - Groth, Detlef A1 - Scheffler, Christiane T1 - Human growth data analyses and statistics BT - The 4th Gülpe International Student Summer School JF - Human biology and public health N2 - Students learn by repetition. Repetition is essential, but repetition needs questioning, and questioning the repertoire belongs to the essential tasks of student education. Guiding students to questioning was and is our prime motive to offer our International Student Summer Schools. The data were critically discussed among the students, in the twilight of Just So Stories, common knowledge, and prompted questioning of contemporary solutions. For these schools, the students bring their own data, carry their preliminary concepts, and in group discussions, they may have to challenge these concepts. Catch-up growth is known to affect long bone growth, but different opinions exist to what extent it also affects body proportions. Skeletal age and dental development are considered appropriate measures of maturation, but it appears that both system develop independently and are regulated by different mechanisms. Body weight distributions are assumed to be skewed, yet, historic data disproved this assumption. Many discussions focused on current ideas of global growth standards as a common yardstick for all populations world-wide, with new statistical tools being developed including network reconstruction and evaluation of the reconstructs to determine the confidence of graph prediction methods. KW - Just so stories KW - Summer Schools KW - questioning solutions KW - repetition Y1 - 2022 U6 - https://doi.org/10.52905/hbph2021.3.29 SN - 2748-9957 VL - 2021 IS - 3, Summer School Supplement PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Hake, Tim A1 - Bodenberger, Bernhard A1 - Groth, Detlef T1 - In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks JF - Human biology and public health N2 - 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. KW - Python KW - correlation KW - network reconstruction KW - bootstrap KW - St. Nicolas House Algorithm Y1 - 2023 U6 - https://doi.org/10.52905/hbph2023.1.63 SN - 2748-9957 VL - 1 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Vi, Son Lang A1 - Trost, Gerda A1 - Lange, Peggy A1 - Czesnick, Hjördis A1 - Rao, Nishta A1 - Lieber, Diana A1 - Laux, Thomas A1 - Gray, William M. A1 - Manley, James L. A1 - Groth, Detlef A1 - Kappel, Christian A1 - Lenhard, Michael T1 - Target specificity among canonical nuclear poly(A) polymerases in plants modulates organ growth and pathogen response JF - PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA N2 - Polyadenylation of pre-mRNAs is critical for efficient nuclear export, stability, and translation of the mature mRNAs, and thus for gene expression. The bulk of pre-mRNAs are processed by canonical nuclear poly(A) polymerase (PAPS). Both vertebrate and higher-plant genomes encode more than one isoform of this enzyme, and these are coexpressed in different tissues. However, in neither case is it known whether the isoforms fulfill different functions or polyadenylate distinct subsets of pre-mRNAs. Here we show that the three canonical nuclear PAPS isoforms in Arabidopsis are functionally specialized owing to their evolutionarily divergent C-terminal domains. A strong loss-of-function mutation in PAPS1 causes a male gametophytic defect, whereas a weak allele leads to reduced leaf growth that results in part from a constitutive pathogen response. By contrast, plants lacking both PAPS2 and PAPS4 function are viable with wild-type leaf growth. Polyadenylation of SMALL AUXIN UP RNA (SAUR) mRNAs depends specifically on PAPS1 function. The resulting reduction in SAUR activity in paps1 mutants contributes to their reduced leaf growth, providing a causal link between polyadenylation of specific pre-mRNAs by a particular PAPS isoform and plant growth. This suggests the existence of an additional layer of regulation in plant and possibly vertebrate gene expression, whereby the relative activities of canonical nuclear PAPS isoforms control de novo synthesized poly(A) tail length and hence expression of specific subsets of mRNAs. Y1 - 2013 U6 - https://doi.org/10.1073/pnas.1303967110 SN - 0027-8424 VL - 110 IS - 34 SP - 13994 EP - 13999 PB - NATL ACAD SCIENCES CY - WASHINGTON ER - TY - JOUR A1 - Hoang, Yen A1 - Gryzik, Stefanie A1 - Hoppe, Ines A1 - Rybak, Alexander A1 - Schädlich, Martin A1 - Kadner, Isabelle A1 - Walther, Dirk A1 - Vera, Julio A1 - Radbruch, Andreas A1 - Groth, Detlef A1 - Baumgart, Sabine A1 - Baumgrass, Ria T1 - PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments JF - Frontiers in immunology N2 - 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. KW - multi-parametric analysis KW - re-analysis KW - combinatorial protein KW - expression KW - high-dimensional cytometry data KW - mass cytometry data KW - pattern perception Y1 - 2022 U6 - https://doi.org/10.3389/fimmu.2022.849329 SN - 1664-3224 VL - 13 PB - Frontiers Media CY - Lausanne ER -