Lipidomic UPLC-MS/MS Profiles of Normal-Appearing White Matter Differentiate Primary and Secondary Progressive Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative inflammatory disease where an autoimmune response to components of the central nervous system leads to a loss of myelin and subsequent neurological deterioration. People with MS can develop primary or secondary progressive disease (PPMS, SPMS) and differentiation of the specific differences in the pathogenesis of these two courses, at the molecular level, is currently unclear. Recently, lipidomics studies using human biofluids, mainly plasma and cerebrospinal fluid, have highlighted a possible role for lipids in the initiation and progression of MS. However, there is a lack of lipidomics studies in MS on CNS tissues, such as normal-appearing white matter (NAWM), where local inflammation initially occurs. Herein, we developed an untargeted reverse phase ultra-performance liquid chromatography time of flight tandem mass spectrometry (RP-UPLC-TOF MSE)-based workflow, in combination with multivariate and univariate statistical analysis, to assess significant differences in lipid profiles in brain NAWM from post-mortem cases of PPMS, SPMS and controls. Groups of eight control, nine PPMS and seven SPMS NAWM samples were used. Correlation analysis of the identified lipids by RP-UPLC-TOF MSE was undertaken to remove those lipids that correlated with age, gender and post-mortem interval as confounding factors. We demonstrate that there is a significantly altered lipid profile of control cases compared with MS cases and that progressive disease, PPMS and SPMS, can be differentiated on the basis of the lipidome of NAWM with good sensitivity, specificity and prediction accuracy based on receiver operating characteristic (ROC) curve analysis. Metabolic pathway analysis revealed that the most altered lipid pathways between PPMS and SPMS were glycerophospholipid metabolism, glycerophosphatidyl inositol (GPI) anchor synthesis and linoleic acid metabolism. Further understanding of the impact of these lipid alterations described herein associated with progression will provide an increased understanding of the mechanisms underpinning progression and highlight possible new therapeutic targets

Estimating Dark Matter Distributions

Thanks to instrumental advances, new, very large kinematic datasets for nearby dwarf spheroidal (dSph) galaxies are on the horizon. A key aim of these datasets is to help determine the distribution of dark matter in these galaxies. Past analyses have generally relied on specific dynamical models or highly restrictive dynamical assumptions. We describe a new, non-parametric analysis of the kinematics of nearby dSph galaxies designed to take full advantage of the future large datasets. The method takes as input the projected positions and radial velocities of stars known to be members of the galaxies, but does not use any parametric dynamical model, nor the assumption that the mass distribution follows that of the visible matter. The problem of estimating the radial mass distribution, M(r) (the mass interior to true radius r), is converted into a problem of estimating a regression function non-parametrically. From the Jeans Equation we show that the unknown regression function is subject to fundamental shape restrictions which we exploit in our analysis using statistical techniques borrowed from isotonic estimation and spline smoothing. Simulations indicate that M(r) can be estimated to within a factor of two or better with samples as small as 1000 stars over almost the entire radial range sampled by the kinematic data. The technique is applied to a sample of 181 stars in the Fornax dSph galaxy. We show that the galaxy contains a significant, extended dark halo some ten times more massive than its baryonic component. Though applied here to dSph kinematics, this approach can be used in the analysis of any kinematically hot stellar system in which the radial velocity field is discretely sampled.Comment: Accepted for publication in The Astrophysical Journa

Regularity of Edge Ideals and Their Powers

We survey recent studies on the Castelnuovo-Mumford regularity of edge ideals of graphs and their powers. Our focus is on bounds and exact values of $\text{ reg } I(G)$ and the asymptotic linear function $\text{ reg } I(G)^q$, for $q \geq 1,$ in terms of combinatorial data of the given graph $G.$Comment: 31 pages, 15 figure

Effects of an exercise and hypocaloric healthy eating intervention on indices of psychological health status, hypothalamic-pituitary-adrenal axis regulation and immune function after early-stage breast cancer : a randomised controlled trial

INTRODUCTION: Many women experience emotional distress, depression and anxiety after a diagnosis of breast cancer. Psychological stress and depression have been associated with hypothalamic-pituitary-adrenal (HPA) axis dysregulation that may adversely affect immune system functioning and impact upon survival. This study investigated the effects of a lifestyle intervention on indices of psychological health status, HPA axis regulation and immune function in overweight women recovering from early-stage breast cancer treatment. METHODS: A total of 85 women treated for breast cancer 3 to 18 months previously were randomly allocated to a 6-month exercise and hypocaloric healthy eating program plus usual care or usual care alone (control group). Women in the intervention group received three supervised exercise sessions per week and individualized dietary advice, supplemented by weekly nutrition seminars. Depressive symptoms (Beck Depression Inventory version II: BDI-II), perceived stress (Perceived Stress Scale: PSS), salivary diurnal cortisol rhythms; inflammatory cytokines (IL-6 and Tumor necrosis factor-α), leukocyte phenotype counts, natural killer (NK) cell cytotoxicity and lymphocyte proliferation following mitogenic stimulation were assessed at baseline and 6-month follow up. RESULTS: Compared with the control group, the intervention group exhibited a reduction in depressive symptoms (adjusted mean difference, 95% confidence intervals (95% CI): -3.12, -1.03 to -5.26; P = 0.004) at the 6-month follow-up but no significant decrease in PSS scores (-2.07, -4.96 to 0.82; P = 0.16). The lifestyle intervention also had a significant impact on diurnal salivary cortisol rhythm compared with usual care alone, as evidenced by an increase in morning salivary cortisol at the 6-month follow-up (P <0.04), indicating a change in HPA axis regulation. Women in the control group had higher total leukocyte, neutrophil and lymphocyte counts in comparison to the intervention group at the 6-month follow-up (P ≤0.05), whereas there was no difference in NK cell counts (P = 0.46), NK cell cytotoxicity (P = 0.85) or lymphocyte proliferation responses (P = 0.11) between the two groups. CONCLUSION: Our results show that the lifestyle intervention resulted in a reduction in depressive symptoms and a normalisation of HPA axis regulation. Such changes could have important implications for long-term survival in women recovering from early-breast cancer treatment. TRIAL REGISTRATION: Current Controlled Trials: ISRCTN08045231

On the weak convergence of stochastic processes without discontinuities of the second kind

Subspaces D α , α > 0, of D [0, 1] are defined and given complete metrics d α which are stronger than the Prokhorov metric. The spaces ( D α d α ) are shown to be separable, and their pre-compact subsets are characterized. A condition which is known to guarantee weak pre-compactness of sets of probability measures over D [0, 1] is shown to also guarantee weak pre-compactness of probability measures over D α for appropriate values of α. Applications are made to the weak convergence of measures induced by stochastic processes, and some examples are included.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47645/1/440_2004_Article_BF00538382.pd

Selection models with monotone weight functions in meta analysis

Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. We adopt the nonparametric approach initially introduced by Dear (1992) but impose that the weight function $w$ is monotonely non-increasing as a function of the $p$-value. Since in meta analysis one typically only has few studies or "observations", regularization of the estimation problem seems sensible. In addition, virtually all parametric weight functions proposed so far in the literature are in fact decreasing. We discuss how to estimate a decreasing weight function in the above model and illustrate the new methodology on two well-known examples. The new approach potentially offers more insight in the selection process than other methods and is more flexible than parametric approaches. Some basic properties of the log-likelihood function and computation of a $p$-value quantifying the evidence against the null hypothesis of a constant weight function are indicated. In addition, we provide an approximate selection bias adjusted profile likelihood confidence interval for the treatment effect. The corresponding software and the datasets used to illustrate it are provided as the R package selectMeta. This enables full reproducibility of the results in this paper.Comment: 15 pages, 2 figures. Some minor changes according to reviewer comment

Inference for bounded parameters

The estimation of signal frequency count in the presence of background noise has had much discussion in the recent physics literature, and Mandelkern [1] brings the central issues to the statistical community, leading in turn to extensive discussion by statisticians. The primary focus however in [1] and the accompanying discussion is on the construction of a confidence interval. We argue that the likelihood function and $p$-value function provide a comprehensive presentation of the information available from the model and the data. This is illustrated for Gaussian and Poisson models with lower bounds for the mean parameter

On the future of astrostatistics: statistical foundations and statistical practice

This paper summarizes a presentation for a panel discussion on "The Future of Astrostatistics" held at the Statistical Challenges in Modern Astronomy V conference at Pennsylvania State University in June 2011. I argue that the emerging needs of astrostatistics may both motivate and benefit from fundamental developments in statistics. I highlight some recent work within statistics on fundamental topics relevant to astrostatistical practice, including the Bayesian/frequentist debate (and ideas for a synthesis), multilevel models, and multiple testing. As an important direction for future work in statistics, I emphasize that astronomers need a statistical framework that explicitly supports unfolding chains of discovery, with acquisition, cataloging, and modeling of data not seen as isolated tasks, but rather as parts of an ongoing, integrated sequence of analyses, with information and uncertainty propagating forward and backward through the chain. A prototypical example is surveying of astronomical populations, where source detection, demographic modeling, and the design of survey instruments and strategies all interact.Comment: 8 pp, 2 figures. To appear in "Statistical Challenges in Modern Astronomy V," (Lecture Notes in Statistics, Vol. 209), ed. Eric D. Feigelson and G. Jogesh Babu; publication planned for Sep 2012; see http://www.springer.com/statistics/book/978-1-4614-3519-

An Exact Formula for the Average Run Length to False Alarm of the Generalized Shiryaev-Roberts Procedure for Change-Point Detection under Exponential Observations

We derive analytically an exact closed-form formula for the standard minimax Average Run Length (ARL) to false alarm delivered by the Generalized Shiryaev-Roberts (GSR) change-point detection procedure devised to detect a shift in the baseline mean of a sequence of independent exponentially distributed observations. Specifically, the formula is found through direct solution of the respective integral (renewal) equation, and is a general result in that the GSR procedure's headstart is not restricted to a bounded range, nor is there a "ceiling" value for the detection threshold. Apart from the theoretical significance (in change-point detection, exact closed-form performance formulae are typically either difficult or impossible to get, especially for the GSR procedure), the obtained formula is also useful to a practitioner: in cases of practical interest, the formula is a function linear in both the detection threshold and the headstart, and, therefore, the ARL to false alarm of the GSR procedure can be easily computed.Comment: 9 pages; Accepted for publication in Proceedings of the 12-th German-Polish Workshop on Stochastic Models, Statistics and Their Application