Latent class analysis variable selection

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected variables, and in the other model it does not. A headlong search algorithm is used to explore the model space and select clustering variables. In simulated datasets we found that the method selected the correct clustering variables, and also led to improvements in classification performance and in accuracy of the choice of the number of classes. In two real datasets, our method discovered the same group structure with fewer variables. In a dataset from the International HapMap Project consisting of 639 single nucleotide polymorphisms (SNPs) from 210 members of different groups, our method discovered the same group structure with a much smaller number of SNP

Analysis of Strong-Coupling Parameters for Superfluid 3He

Superfluid $^{3}$He experiments show strong deviation from the weak-coupling limit of the Ginzburg-Landau theory, and this discrepancy grows with increasing pressure. Strong-coupling contributions to the quasiparticle interactions are known to account for this effect and they are manifest in the five $\beta$-coefficients of the fourth order Ginzburg-Landau free energy terms. The Ginzburg-Landau free energy also has a coefficient $g_{z}$ to include magnetic field coupling to the order parameter. From NMR susceptibility experiments, we find the deviation of $g_{z}$ from its weak-coupling value to be negligible at all pressures. New results for the pressure dependence of four different combinations of $\beta$-coefficients, $\beta$_{345}, $\beta$_{12}, $\beta$_{245}, and $\beta$_{5} are calculated and comparison is made with theory.Comment: 6 pages, 2 figures, 1 table. Manuscript prepared for QFS200

Transcription factor Pit-1 affects transcriptional timing in the dual-promoter human prolactin gene

Gene transcription occurs in short bursts interspersed with silent periods, and these kinetics can be altered by promoter structure. The effect of alternate promoter architecture on transcription bursting is not known. We studied the human prolactin (hPRL) gene that contains two promoters, a pituitary-specific promoter that requires the transcription factor Pit-1, and displays dramatic transcriptional bursting activity, and an alternate upstream promoter that is active in non-pituitary tissues. We studied large hPRL genomic fragments with luciferase reporters, and used bacterial artificial chromosome (BAC) recombineering to manipulate critical promoter regions. Stochastic switch mathematical modelling of single-cell time-lapse luminescence image data revealed that the Pit-1-dependent promoter showed longer, higher-amplitude transcriptional bursts. Knockdown studies confirmed that the presence of Pit-1 stabilised and prolonged periods of active transcription. Pit-1 therefore plays an active role in establishing the timing of transcription cycles, in addition to its cell-specific functions

Neutron charge form factor at large q2q^2

The neutron charge form factor $G_{En}(q)$ is determined from an analysis of the deuteron quadrupole form factor $F_{C2}$ data. Recent calculations, based on a variety of different model interactions and currents, indicate that the contributions associated with the uncertain two-body operators of shorter range are relatively small for $F_{C2}$, even at large momentum transfer $q$. Hence, $G_{En}(q)$ can be extracted from $F_{C2}$ at large $q^2$ without undue systematic uncertainties from theory.Comment: 8 pages, 3 figure

IMG 305 - PEMBUNGKUSAN MAKANAN NOV.05.

We discuss the use of Agent-based Modelling for the development and testing of theories about emergent social phenomena in marketing and the social sciences in general. We address both theoretical aspects about the types of phenomena that are suitably addressed with this approach and practical guidelines to help plan and structure the development of a theory about the causes of such a phenomenon in conjunction with a matching ABM. We argue that research about complex social phenomena is still largely fundamental research and therefore an iterative and cyclical development process of both theory and model is to be expected. To better anticipate and manage this process, we provide theoretical and practical guidelines. These may help to identify and structure the domain of candidate explanations for a social phenomenon, and furthermore assist the process of model implementation and subsequent development. The main goal of this paper was to make research on complex social systems more accessible and help anticipate and structure the research process