Evolution of IgE responses to multiple allergen components throughout childhood

BACKGROUND: There is a paucity of information about longitudinal patterns of IgE responses to allergenic proteins (components) from multiple sources. OBJECTIVE: To investigate temporal patterns of component-specific IgE responses from infancy to adolescence, and their relationship with allergic diseases. METHODS: In a population-based birth cohort, we measured IgE to 112 components at 6 follow-ups during childhood. We used a Bayesian method to discover cross-sectional sensitization patterns and their longitudinal trajectories, and related these patterns to asthma and rhinitis in adolescence. RESULTS: We identified one sensitization cluster at age one, 3 at age three, 4 at ages five and eight, 5 at age 11, and six at age 16 years. "Broad" cluster was the only cluster present at every follow-up, comprising of components from multiple sources. "Dust mite" cluster formed at age three and remained unchanged to adolescence. At age three, a single-component "Grass" cluster emerged, which at age five absorbed additional grass components and Fel d 1 to form the "Grass/cat" cluster. Two new clusters formed at age 11: "Cat" cluster and "PR-10/profilin" (which divided at age 16 into "PR-10" and "Profilin"). The strongest contemporaneous associate of asthma at age 16 years was sensitization to "Dust mite" cluster (OR [95% CI]: 2.6 [1.2-6.1], P<0.05), but the strongest early-life predictor of subsequent asthma was sensitization to "Grass/cat" cluster (3.5 [1.6-7.4], P<0.01). CONCLUSIONS: We describe the architecture of the evolution of IgE responses to multiple allergen components throughout childhood, which may facilitate development of better diagnostic and prognostic biomarkers for allergic diseases

The statistical mechanics of a polygenic characterunder stabilizing selection, mutation and drift

By exploiting an analogy between population genetics and statistical mechanics, we study the evolution of a polygenic trait under stabilizing selection, mutation, and genetic drift. This requires us to track only four macroscopic variables, instead of the distribution of all the allele frequencies that influence the trait. These macroscopic variables are the expectations of: the trait mean and its square, the genetic variance, and of a measure of heterozygosity, and are derived from a generating function that is in turn derived by maximizing an entropy measure. These four macroscopics are enough to accurately describe the dynamics of the trait mean and of its genetic variance (and in principle of any other quantity). Unlike previous approaches that were based on an infinite series of moments or cumulants, which had to be truncated arbitrarily, our calculations provide a well-defined approximation procedure. We apply the framework to abrupt and gradual changes in the optimum, as well as to changes in the strength of stabilizing selection. Our approximations are surprisingly accurate, even for systems with as few as 5 loci. We find that when the effects of drift are included, the expected genetic variance is hardly altered by directional selection, even though it fluctuates in any particular instance. We also find hysteresis, showing that even after averaging over the microscopic variables, the macroscopic trajectories retain a memory of the underlying genetic states.Comment: 35 pages, 8 figure

Globally optimal on-line learning rules for multi-layer neural networks

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This rule maximizes the total reduction in generalization error over the whole learning process. A simple example demonstrates that the locally optimal rule, which maximizes the rate of decrease in generalization error, may perform poorly in comparison

Diaries For Intensive Care Unit Survivors (discuss): Perspectives Of Patients And Family Members Regarding Psychological Support

Rationale Diaries summarising care in the intensive care unit (ICU) have been incorporated into routine practice in some countries, although diary format, content, timing and method of delivery vary. The evidence and underpinning theory to support diary use is limited and uptake by patients and family members is infrequent raising questions of acceptance and generalisability. The purpose of this study was to elicit preferences of ICU survivors and their family members regarding diaries and other psychological support mechanisms, specifically if they wish to receive a diary or alternative psychological support material, what content they would like and preferred process and timing for receiving this information. Methods Patients in a general ICU for ≥3 days and their family members were invited to participate in this prospective exploratory study. Semi-structured interviews were conducted 3-5 months after ICU discharge via phone or in person. Interviews were transcribed verbatim and thematic analysis was conducted, with codes developed inductively using an iterative process and agreement on themes negotiated by team members. Results Forty-one survivors of critical illness and 26 family members consented to participation. Patients were usually male (63%), aged 50±18 years and stayed in ICU for median 8.5 (IQR 5.3-12.6) days. Patients and family members raised similar themes, although with a diverse range of opinion related to each aspect of ICU diary use. Considerations included: 1) memory and recall with some respondents wanting to know detail while others were either not curious or had no desire to be reminded of their ICU stay; 2) level of detail ranging from a comprehensive summary of clinical activities to a predominantly personal record of the experience; 3) the desire to develop shared memory of ICU and for the patient to understand what the family had been through; 4) timing of provision of diary from soon after the patient woke to months later; 5) concerns regarding the workload of staff to contribute to the diary; 6) potential to exacerbate or alternatively relieve stress; 7) uncertainty in regard to writing in diary as the patient may not survive; 8) ownership of the diary resting with the patient. Conclusions Common themes have been expressed by participants, although views regarding these themes were extremely diverse. Development of future interventions to aid psychological recovery must take into account these variable views to minimise the potential for causing distress to some survivors of critical illness and/or their family members whilst helping others

Genetic algorithm dynamics on a rugged landscape

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.Comment: 10 pages RevTeX, 4 figures PostScrip

The Herschel Planetary Nebula Survey (HerPlaNS) I. Data Overview and Analysis Demonstration with NGC 6781

This is the first of a series of investigations into far-IR characteristics of 11 planetary nebulae (PNs) under the Herschel Space Observatory Open Time 1 program, Herschel Planetary Nebula Survey (HerPlaNS). Using the HerPlaNS data set, we look into the PN energetics and variations of the physical conditions within the target nebulae. In the present work, we provide an overview of the survey, data acquisition and processing, and resulting data products. We perform (1) PACS/SPIRE broadband imaging to determine the spatial distribution of the cold dust component in the target PNs and (2) PACS/SPIRE spectral-energy-distribution (SED) and line spectroscopy to determine the spatial distribution of the gas component in the target PNs. For the case of NGC 6781, the broadband maps confirm the nearly pole-on barrel structure of the amorphous carbon-richdust shell and the surrounding halo having temperatures of 26-40 K. The PACS/SPIRE multi-position spectra show spatial variations of far-IR lines that reflect the physical stratification of the nebula. We demonstrate that spatially-resolved far-IR line diagnostics yield the (T_e, n_e) profiles, from which distributions of ionized, atomic, and molecular gases can be determined. Direct comparison of the dust and gas column mass maps constrained by the HerPlaNS data allows to construct an empirical gas-to-dust mass ratio map, which shows a range of ratios with the median of 195+-110. The present analysis yields estimates of the total mass of the shell to be 0.86 M_sun, consisting of 0.54 M_sun of ionized gas, 0.12 M_sun of atomic gas, 0.2 M_sun of molecular gas, and 4 x 10^-3 M_sun of dust grains. These estimates also suggest that the central star of about 1.5 M_sun initial mass is terminating its PN evolution onto the white dwarf cooling track.Comment: 27 pages, 16 figures, accepted for publication in A&

Sparsest factor analysis for clustering variables: a matrix decomposition approach

We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA

Chronic inflammatory arthritis drives systemic changes in circadian energy metabolism

SignificanceRheumatoid arthritis (RA) is a debilitating chronic inflammatory disease in which symptoms exhibit a strong time-of-day rhythmicity. RA is commonly associated with metabolic disturbance and increased incidence of diabetes and cardiovascular disease, yet the mechanisms underlying this metabolic dysregulation remain unclear. Here, we demonstrate that rhythmic inflammation drives reorganization of metabolic programs in distal liver and muscle tissues. Chronic inflammation leads to mitochondrial dysfunction and dysregulation of fatty acid metabolism, including accumulation of inflammation-associated ceramide species in a time-of-day-dependent manner. These findings reveal multiple points for therapeutic intervention centered on the circadian clock, metabolic dysregulation, and inflammatory signaling

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/