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

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

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

Patient Perspectives on Factors Affecting Direct Oral Anticoagulant Use for Stroke Prevention in Atrial Fibrillation

YesIntroduction: Oral anticoagulant therapy choices for patients with atrial fibrillation (AF) expanded in the last decade with the introduction of direct oral anticoagulants (DOAC). However, the implementation of DOACs was slow and varied across different health economies in England. There is limited evidence on the patient role in the uptake of new medicines, including DOACs, apart from considering their demographic and clinical characteristics. Hence, this study aimed to address the gap by exploring the view of patients with AF on factors affecting DOAC use. Methods: A qualitative study using semi-structured interviews was conducted in three health economies in the North of England. Adult patients (>18 years) diagnosed with non-valvular AF, prescribed an oral anticoagulant (vitamin K antagonist or DOAC), and able to give written consent were recruited. Data were collected between August 2018 and April 2019. Audio recorded interviews were transcribed verbatim and analyzed using the framework method. Results: Four themes with eleven subthemes discussed identified factors affecting the use of DOACs. They were linked to limited healthcare financial and workforce resources, patient involvement in decision-making, patient knowledge about DOACs, safety concerns about oral anticoagulants, and oral anticoagulant therapy impact on patients' daily lives. Lack of a) opportunities to voice patient preferences and b) information on available therapy options resulted in some patients experiencing difficulties with the prescribed therapy. This was reported to cause negative impact on their daily lives, adherence, and overall satisfaction with the therapy. Conclusion: Greater patient involvement in decision-making could prevent and resolve difficulties encountered by some patients and potentially improve outcomes plus increase the uptake of DOACs.Pharmacy Research UK (PRUK-2018-GA-1-KM) and Leeds Teaching Hospitals NHS Trus

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/

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

Discrimination between oral corticosteroid-treated and oral corticosteroid-non-treated severe asthma patients by an electronic nose platform

Rationale: Some severe asthma patients require oral corticosteroids (OCS) likely due to greater disease severity. Exhaled molecular markers can provide phenotypic information in asthma. Objectives: Determine whether patients on OCS (OCS+) have a different breathprint compared with those who were not on OCS (OCS-); determine the classification accuracy of eNose as compared to FEV1 % pred, % sputum eosinophils, and exhaled nitric oxide (FENO). Methods: This was a cross-sectional analysis of the U-BIOPRED cohort. Severe asthma was defined by IMI-criteria [Bel Thorax 2011]. OCS+ patients had daily OCS. OCS- patients had never had OCS and were on maintenance inhaled fluticasone equivalent >1000 μg/day. Exhaled volatile organic compounds trapped on adsorption tubes were analysed by centralized eNose platform (Owlstone Lonestar, Cyranose 320, Comon Invent, Tor Vergata TEN) including a total of 190 sensors. t test was used for comparing groups and support vector machine with leave-one-out cross-validation as a classifier. Results: 33 OCS+ (age 55±11yr, mean±SD, 52% female, 27% smokers, pre-bronchodilator FEV1 64.1±24% pred) and 40 OCS- severe asthma patients (age 54±15yr, mean±SD, 55% female, 35% smokers, pre-bronchodilator FEV1 61.8±24% pred) were studied. Sensor by sensor analysis showed that 56 sensors provided different mean values (change in sensor resistance or frequency) between groups (P<0.05). Accuracy of classification was as follows: eNose 71% (n=73), FENO 71% (n=70), FEV1 62% (n=73) and sputum eosinophils 59% (n=37). Conclusions: Preliminary results suggest OCS+ and OCS- severe asthma patients can be distinguished by an eNose platform

Clinical decision-making: midwifery students' recognition of, and response to, post partum haemorrhage in the simulation environment

<p>Abstract</p> <p>Background</p> <p>This paper reports the findings of a study of how midwifery students responded to a simulated post partum haemorrhage (PPH). Internationally, 25% of maternal deaths are attributed to severe haemorrhage. Although this figure is far higher in developing countries, the risk to maternal wellbeing and child health problem means that all midwives need to remain vigilant and respond appropriately to early signs of maternal deterioration.</p> <p>Methods</p> <p>Simulation using a patient actress enabled the research team to investigate the way in which 35 midwifery students made decisions in a dynamic high fidelity PPH scenario. The actress wore a birthing suit that simulated blood loss and a flaccid uterus on palpation. The scenario provided low levels of uncertainty and high levels of relevant information. The student's response to the scenario was videoed. Immediately after, they were invited to review the video, reflect on their performance and give a commentary as to what affected their decisions. The data were analysed using Dimensional Analysis.</p> <p>Results</p> <p>The students' clinical management of the situation varied considerably. Students struggled to prioritise their actions where more than one response was required to a clinical cue and did not necessarily use mnemonics as heuristic devices to guide their actions. Driven by a response to single cues they also showed a reluctance to formulate a diagnosis based on inductive and deductive reasoning cycles. This meant they did not necessarily introduce new hypothetical ideas against which they might refute or confirm a diagnosis and thereby eliminate fixation error.</p> <p>Conclusions</p> <p>The students response demonstrated that a number of clinical skills require updating on a regular basis including: fundal massage technique, the use of emergency standing order drugs, communication and delegation of tasks to others in an emergency and working independently until help arrives. Heuristic devices helped the students to evaluate their interventions to illuminate what else could be done whilst they awaited the emergency team. They did not necessarily serve to prompt the students' or help them plan care prospectively. The limitations of the study are critically explored along with the pedagogic implications for initial training and continuing professional development.</p