A gentle introduction to mixture modeling using physical fitness performance data

This chapter provides a non-technical introduction to mixture modeling for sport and exercise sciences researchers. Although this method has been around for quite some time, it is still underutilized in sport and exercise research. The data set used for this illustration consists of a sample of 10,000 students who annually completed physical fitness tests for 7 years in Singapore. First, we illustrate latent profile analyses (LPA). Next, we illustrate how to include covariates in LPA and how to test the invariance of LPA solutions across groups, as well as over time using latent transition analyses. Following that, we illustrate the estimation of mixture regression models to identify subgroups of participants differing from one another at the levels of the relations among constructs. Finally, a growth mixture modeling example is shown to identify subgroups of participants following distinct longitudinal trajectories

Quantum transport through a DNA wire in a dissipative environment

Electronic transport through DNA wires in the presence of a strong dissipative environment is investigated. We show that new bath-induced electronic states are formed within the bandgap. These states show up in the linear conductance spectrum as a temperature dependent background and lead to a crossover from tunneling to thermal activated behavior with increasing temperature. Depending on the strength of the electron-bath coupling, the conductance at the Fermi level can show a weak exponential or even an algebraic length dependence. Our results suggest a new environmental-induced transport mechanism. This might be relevant for the understanding of molecular conduction experiments in liquid solution, like those recently performed on poly(GC) oligomers in a water buffer (B. Xu et al., Nano Lett 4, 1105 (2004)).Comment: 5 pages, 3 figure

Activation of gene expression by detergent-like protein domains

The mechanisms by which transcriptional activation domains (tADs) initiate eukaryotic gene expression have been an enigma for decades because most tADs lack specificity in sequence, structure, and interactions with targets. Machine learning analysis of data sets of tAD sequences generated in vivo elucidated several functionality rules: the functional tAD sequences should (i) be devoid of or depleted with basic amino acid residues, (ii) be enriched with aromatic and acidic residues, (iii) be with aromatic residues localized mostly near the terminus of the sequence, and acidic residues localized more internally within a span of 20–30 amino acids, (iv) be with both aromatic and acidic residues preferably spread out in the sequence and not clustered, and (v) not be separated by occasional basic residues. These and other more subtle rules are not absolute, reflecting absence of a tAD consensus sequence, enormous variability, and consistent with surfactant-like tAD biochemical properties. The findings are compatible with the paradigm-shifting nucleosome detergent mechanism of gene expression activation, contributing to the development of the liquid-liquid phase separation model and the biochemistry of near-stochastic functional allosteric interactions

Effective action and density functional theory

The effective action for the charge density and the photon field is proposed as a generalization of the density functional. A simple definition is given for the density functional, as the functional Legendre transform of the generator functional of connected Green functions for the density and the photon field, offering systematic approximation schemes. The leading order of the perturbation expansion reproduces the Hartree-Fock equation. A renormalization group motivated method is introduced to turn on the Coulomb interaction gradually and to find corrections to the Hartree-Fock and the Kohn-Sham schemes.Comment: New references and a numerical algorithm added, to appear in Phys. Rev. B. 30 pages, no figure

Principal-Oscillation-Pattern Analysis of Gene Expression

Principal-oscillation-pattern (POP) analysis is a multivariate and systematic technique for identifying the dynamic characteristics of a system from time-series data. In this study, we demonstrate the first application of POP analysis to genome-wide time-series gene-expression data. We use POP analysis to infer oscillation patterns in gene expression. Typically, a genomic system matrix cannot be directly estimated because the number of genes is usually much larger than the number of time points in a genomic study. Thus, we first identify the POPs of the eigen-genomic system that consists of the first few significant eigengenes obtained by singular value decomposition. By using the linear relationship between eigengenes and genes, we then infer the POPs of the genes. Both simulation data and real-world data are used in this study to demonstrate the applicability of POP analysis to genomic data. We show that POP analysis not only compares favorably with experiments and existing computational methods, but that it also provides complementary information relative to other approaches

Observational constraints on Horava-Lifshitz cosmology

We use observational data from Type Ia Supernovae (SNIa), Baryon Acoustic Oscillations (BAO), and Cosmic Microwave Background (CMB), along with requirements of Big Bang Nucleosynthesis (BBN), to constrain the cosmological scenarios governed by Horava-Lifshitz gravity. We consider both the detailed and non-detailed balance versions of the gravitational sector, and we include the matter and radiation sectors. We conclude that the detailed-balance scenario cannot be ruled out from the observational point of view, however the corresponding likelihood contours impose tight constraints on the involved parameters. The scenario beyond detailed balance is compatible with observational data, and we present the corresponding stringent constraints and contour-plots of the parameters. Although this analysis indicates that Horava-Lifshitz cosmology can be compatible with observations, it does not enlighten the discussion about its possible conceptual and theoretical problems.Comment: 11 pages, 6 figures, version published in JCA

Celebrating Cercignani's conjecture for the Boltzmann equation

Cercignani's conjecture assumes a linear inequality between the entropy and entropy production functionals for Boltzmann's nonlinear integral operator in rarefied gas dynamics. Related to the field of logarithmic Sobolev inequalities and spectral gap inequalities, this issue has been at the core of the renewal of the mathematical theory of convergence to thermodynamical equilibrium for rarefied gases over the past decade. In this review paper, we survey the various positive and negative results which were obtained since the conjecture was proposed in the 1980s.Comment: This paper is dedicated to the memory of the late Carlo Cercignani, powerful mind and great scientist, one of the founders of the modern theory of the Boltzmann equation. 24 pages. V2: correction of some typos and one ref. adde

Magnetoplasmonic design rules for active magneto-optics

Light polarization rotators and non-reciprocal optical isolators are essential building blocks in photonics technology. These macroscopic passive devices are commonly based on magneto-optical Faraday and Kerr polarization rotation. Magnetoplasmonics - the combination of magnetism and plasmonics - is a promising route to bring these devices to the nanoscale. We introduce design rules for highly tunable active magnetoplasmonic elements in which we can tailor the amplitude and sign of the Kerr response over a broad spectral range

Transcriptomic Changes Due to Cytoplasmic TDP-43 Expression Revel Dysregulation of Histone Transcripts and Nuclear Chromatin

AR DNA-binding protein 43 (TDP-43) is normally a nuclear RNA-binding protein that exhibits a range of functions including regulation of alternative splicing, RNA trafficking, and RNA stability. However, in amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP), TDP-43 is abnormally phosphorylated, ubiquitinated, and cleaved, and is mislocalized to the cytoplasm where it forms distinctive aggregates. We previously developed a mouse model expressing human TDP-43 with a mutation in its nuclear localization signal (ΔNLS-hTDP-43) so that the protein preferentially localizes to the cytoplasm. These mice did not exhibit a significant number of cytoplasmic aggregates, but did display dramatic changes in gene expression as measured by microarray, suggesting that cytoplasmic TDP-43 may be associated with a toxic gain-of-function. Here, we analyze new RNA-sequencing data from the ΔNLS-hTDP-43 mouse model, together with published RNA-sequencing data obtained previously from TDP-43 antisense oligonucleotide (ASO) knockdown mice to investigate further the dysregulation of gene expression in the ΔNLS model. This analysis reveals that the transcriptomic effects of the overexpression of the ΔNLS-hTDP-43 transgene are likely due to a gain of cytoplasmic function. Moreover, cytoplasmic TDP-43 expression alters transcripts that regulate chromatin assembly, the nucleolus, lysosomal function, and histone 3’ untranslated region (UTR) processing. These transcriptomic alterations correlate with observed histologic abnormalities in heterochromatin structure and nuclear size in transgenic mouse and human brains