PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.

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.

The end of the secular trend in Norway: spatial trends in body height of Norwegian conscripts in the 19th, 20th and 21st century.

Aim: We aimed to examine the distribution and secular changes of conscript body height in the geographic network of Norway since 1878 and to study its association with the degree of urbanization, and population density. Material and methods: Data on body height of Norwegian military conscripts were provided by the Statistics Norway Department (SSB). The sample comprised eight cohorts with the following measurement years: 1st 1877, 1878 and 1880, 2nd 1895-1897, 3rd 1915-1917, 4th 1935-1937, 5th 1955-1957, 6th 1975-1977, 7th 1995-1997, and 8th 2009-2011. For determining neighborhood correlations, a network was created consisting of neighboring counties, sharing a common border. Results: Average body height of Norwegian men increased by 10.9 cm between 1878 and 2010, but this trend was heterogeneous. Some counties increased by more than 1 cm per decade (Finmark) others by only 7 mm per decade (Sor-Trondelag). Urban counties and counties with higher population density showed stronger height trends than rural counties. The largest spread in body height between the various counties was observed in 1936 when for the first time people living in the more urban counties got taller than rural people. The height advantage of urban counties however, disappeared after 1996. At this time, also the secular trend in height had come to a halt. The secular trend in height had become obvious after the dissolution of the union between Norway and Sweden in 1905 and World War I, and was strongest between 1936 and 1956. During this period maximum between-county heterogeneity in height existed with body height differences of more than 6 cm between the tallest and the shortest county. The end of this period was characterized by social democratic reforms that flattened the income distribution, eliminated poverty, and ensured social services after World War II. Conclusion: The temporal coincidence between the trends in height, the degree of urbanization and the onset of the political transition of Norway from a Swedish province into an independent democratic wealthy modern European state after World War I and particularly after World War II, and the abatement of this trend after this period of transition had stabilized, suggest social and political components interfering with the regulation of physical growth in humans.

Spatial conscript body height correlation of Norwegian districts in the 19th century.

ABSTRACT: Background: We investigated height of Norwegian conscripts in view of the hypothesis of a "community effect on height" using autocorrelation analysis of district heights within a time-span of 20 years at the end of the 19th century and correlations between neighboring districts at this time. Material and methods: After digitalizing available body height data of Norwegian draftees in 1877-1878, 1880 (averaged as 1878), and 1895-1897 (averaged as 1896) we calculated the magnitude of autocorrelation of body height within the same municipality at different time points. Furthermore, we generated three different neighborhood networks, (1) based on Euclidean distances, (2) a minimum spanning tree build on those distances, (3) a network founded on real world road connections. The networks were used to determine the correlation between body height of neighboring districts depending on the number of edges required to connect two municipalities. Results: The autocorrelation value for body heights was around r = 0.5 (for all p < 0.001) in the years 1878 and 1896. The correlation between neighboring districts varied in the Euclidean distance based network between 0.47 and 0.27 approximately for both years in a sorted order, descending from nearest (0-50 km) to farthest (150-200 km, for all p < 0.001). First order neighbors in the minimum spanning tree network correlation was 0.36 in 1878 and 0.42 in 1896 (for all p < 0.001). The values of neighbor correlation in the road connection based network ranged in 1878 from 0.42 (first order neighbors) to 0.17 (forth order neighbors, for all p < 0.01) and in 1896 from 0.46 (first order neighbors) to 0.12 (forth order neighbors, for all p < 0.05). Conclusion: This initial study of Norwegian conscript height data from the 19th century showed significant medium sized effects for the within district autocorrelation between 1878 and 1896 as well as medium neighborhood correlation, slightly lower in comparison to a recent study regarding Swiss conscripts. Digitalizing more data from other years in this and later time spans as well as using older road and ship connections instead of the actual road data might stabilize and improve those findings.