Correcting for non-periodic behaviour in perturbative experiments: application to heat pulse propagation and modulated gas-puff experiments

This paper introduces a recent innovation in dealing with non-periodic behavior often referred to as transients. These transients can be the result from unforced response due to the initial condition and other drifts which are a source of error when performing and interpreting Fourier analysis on measurement data. Fourier analysis is particularly relevant in system identification used to build feedback controllers and the analysis of various pulsed experiments such as heat pulse propagation studies. The basic idea behind the methodology is that transients are continuous complex-valued smooth functions in the Fourier domain which can be estimated from the Fourier data. Then, these smooth functions can be approximately subtracted from the data such that only periodic components are retained. The merit of the approach is shown in two experimental examples, i.e., heat pulse propagation (core transport analysis) and radiation front movement due to gas puffing. The examples show that the quality of the data is significantly improved such that it allows new interpretation of the results even for non-ideal measurements.</p

Correcting for non-periodic behaviour in perturbative experiments: application to heat pulse propagation and modulated gas-puff experiments

This paper introduces a recent innovation in dealing with non-periodic behavior often referred to as transients in perturbative experiments. These transients can be the result from the unforced response due to the initial condition and other slow trends in the measurement data and are a source of error when performing and interpreting Fourier spectra. Fourier analysis is particularly relevant in system identification used to build feedback controllers and the analysis of various pulsed experiments such as heat pulse propagation studies. The basic idea behind the methodology is that transients are continuous complex-valued smooth functions in the Fourier domain which can be estimated from the Fourier data. Then, these smooth functions can be subtracted from the data such that only periodic components are retained. The merit of the approach is shown in two experimental examples, i.e. heat pulse propagation (core transport analysis) and radiation front movement due to gas puffing in the divertor. The examples show that the quality of the data is significantly improved such that it allows for new interpretation of the results even for non-ideal measurements

Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study.

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results

Functional interactions between Dlx2 and lymphoid enhancer factor regulate Msx2

Dlx2, Lymphoid Enhancer Factor (Lef-1) and Msx2 transcription factors are required for several developmental processes. To understand the control of gene expression by these factors, chromatin immunoprecipitation (ChIP) assays identified Msx2 as a downstream target of Dlx2 and Lef-1. Dlx2 activates the Msx2 promoter in several cell lines and binds DNA as a monomer and dimer. A Lef-1 β-catenin-dependent isoform minimally activates the Msx2 promoter and a Lef-1 β-catenin-independent isoform is inactive, however co-expression of Dlx2 and both Lef-1 isoforms synergistically activate the Msx2 promoter. Co-immunoprecipitation and protein pull-down experiments demonstrate Lef-1 physically interacts with Dlx2. Deletion analyses of the Lef-1 protein reveal specific regions required for synergism with Dlx2. The Lef-1 β-catenin binding domain (βDB) is not required for its interaction with Dlx2. Msx2 can auto-regulate its promoter and repress Dlx2 activation. Msx2 repression of Dlx2 activation is dose-specific and both bind a common DNA-binding element. These transcriptional mechanisms correlate with the temporal and spatial expression of these factors and may provide a mechanism for the control of several developmental processes. We demonstrate new transcriptional activities for Dlx2, Msx2 and Lef-1 through protein interactions and identification of downstream targets

GATA3 Expression Is Decreased in Psoriasis and during Epidermal Regeneration; Induction by Narrow-Band UVB and IL-4

Psoriasis is characterized by hyperproliferation of keratinocytes and by infiltration of activated Th1 and Th17 cells in the (epi)dermis. By expression microarray, we previously found the GATA3 transcription factor significantly downregulated in lesional psoriatic skin. Since GATA3 serves as a key switch in both epidermal and T helper cell differentiation, we investigated its function in psoriasis. Because psoriatic skin inflammation shares many characteristics of epidermal regeneration during wound healing, we also studied GATA3 expression under such conditions