bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time spent suckling is affected by different social organization in three zebra species
Authors: Pluháček J. , Olléová M. , Bartoš L. , Bartošová J. 
Published: September 2013

AbstractSuckling bout duration and frequency were used in the past as an indicator of milk intake. However, later studies found no significant relationship between suckling bout duration and frequency and milk or energy intake. On the other hand recent studies are in line with the suggestion that suckling bout duration and frequency may express intensity of maternal care. The three extant zebra species differ in their ecology and social system. Mountain Equus zebra and Grévy's zebra E. grevyi live in an arid environment, whereas plains zebras E. quagga are found in savannah. Mountain and plains zebra mares form stable herds associated with high aggression and low aggression, respectively. Female Grévy's zebras form loose associations with the lowest level of aggression. The aim of this study was to re‐evaluate the suggestion that suckling bout duration and frequency are affected by social system. We observed suckling behaviour of 30 foals (16 plains zebras, 8 Grévy's zebras and 6 mountain zebras) at the Dvůr Králové Zoo, Czech Republic. We found that suckling bout duration was longest in mountain zebras, followed by plains and Grévy's zebras. Similar results were found for suckling frequency. These results coincide with the rate of aggression among mares; foals spent more time by suckling in species, where more aggression among adults occurred. Thus, the results of our study support the suggestion that suckling bout duration reflects social needs of the foal rather than milk intake requirements.
Long‐term exposure to elevatedCO2andO3alters aspen foliar chemistry across developmental stages
Authors: COUTURE J. J. , HOLESKI L. M. , LINDROTH R. L. 
Published: September 2013

AbstractAnthropogenic activities are altering levels of greenhouse gases to the extent that multiple and diverse ecosystem processes are being affected. Two gases that substantially influence forest health are atmospheric carbon dioxide (CO2) and tropospheric ozone (O3). Plant chemistry will play an important role in regulating ecosystem processes in future environments, but little information exists about the longitudinal effects of elevatedCO2andO3on phytochemistry, especially for long‐lived species such as trees. To address this need, we analysed foliar chemical data from two genotypes of trembling aspen,Populus tremuloides, collected over 10 years of exposure to levels ofCO2andO3predicted for the year 2050. ElevatedCO2andO3altered both primary and secondary chemistry, and the magnitude and direction of the responses varied across developmental stages and between aspen genotypes. Our findings suggest that the effects ofCO2andO3on phytochemical traits that influence forest processes will vary over tree developmental stages, highlighting the need to continue long‐term, experimental atmospheric change research.
Calendarization with Interpolating Splines and State Space Models
Authors: Quenneville B. , Picard F. , Fortier S. 
Published: September 2013

SummaryWe consider the problem of transforming values from a flow time series observed over varying time intervals into values that cover calendar intervals such as day, week, month, quarter and year. We call this process calendarization. We propose simple methods based on interpolating the cumulated flows with natural spline interpolations. Alternatively, we provide state space models with missing observations to obtain smoothed values of the level of the cumulated flows. The state space models are the underlying statistical models behind Denton's benchmarking methods modified by Cholette. We therefore provide efficient alternative methods for benchmarking and temporal distribution. We show the theoretical properties of our methods, compare them and illustrate them with various examples.
Statistical Approaches to Three Key Challenges in Protein Structural Bioinformatics
Authors: Mardia Kanti V. 
Published: September 2013

SummaryProteins are the workhorses of all living systems, and protein bioinformatics deals with analysis of protein sequences (one dimensional) and structures (three dimensional). The paper reviews statistical advances in three major active areas of protein structural bioinformatics: structure comparison, Ramachandran plots and structure prediction. These topics play a key role in understanding one of the greatest unsolved problems in biology, how proteins fold from one dimension to three dimensions, and have relevance to protein functionality, drug discovery and evolutionary biology. For each area, we give the biological background and review one of the main bioinformatics solutions to a specific problem in that area. We then present statistical tools recently developed to investigate these problems, consisting of Bayesian alignment, directional distributions and hidden Markov models. We illustrate each problem with a new case-study and describe what statistics can offer to these problems. We highlight challenges facing these areas and conclude with an overall discussion.
Analysis of Interval-Censored Data with Random Unknown End Points: An Application to Soft Error Rate Estimation
Authors: Michalak Sarah E. , Hamada Michael S. , Hengartner Nicolas W. 
Published: September 2013

Summary
The paper presents a Bayesian approach to analysing interval-censored data with random unknown end points. Such data occur when the event of interest is interval censored but, because of the measurement process, the interval end points are not known exactly. Modelling the measurement process permits inference that accounts for this source of variability. Our results are motivated by an experimental study that was designed to characterize the cosmic-ray–neutron-induced soft error rate of a semiconductor device.
Reduced Hierarchical Models with Application to Estimating Health Effects of Simultaneous Exposure to Multiple Pollutants
Authors: Bobb Jennifer F. , Dominici Francesca , Peng Roger D. 
Published: September 2013

Summary
Hierarchical models (HMs) have been used extensively in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for other pollutants and other time varying factors. Recently, the US Environmental Protection Agency has called for research quantifying health effects of simultaneous exposure to many air pollutants. However, straightforward application of HMs in this context is challenged by the need to specify a random-effect distribution on a high dimensional vector of nuisance parameters. Here we introduce the reduced HM as a general statistical approach for analysing correlated data with many nuisance parameters. For reduced HMs we first calculate the integrated likelihood of the parameter of interest (e.g. the excess number of deaths attributed to simultaneous exposure to high levels of many pollutants), and we then specify a flexible random-effect distribution directly on this parameter. Simulation studies show that the reduced HM performs comparably with the full HM in many scenarios and even performs better in some cases, particularly when the multivariate random-effect distribution of the full HM is misspecified. Methods are applied to estimate relative risks of cardio-vascular hospital admissions associated with simultaneous exposure to elevated levels of particulate matter and ozone in 51 US counties during 1999–2005.
Fully Bayesian Hierarchical Modelling in Two Stages, with Application to Meta-Analysis
Authors: Lunn David , Barrett Jessica , Sweeting Michael , Thompson Simon 
Published: September 2013

Summary
Meta-analysis is often undertaken in two stages, with each study analysed separately in stage 1 and estimates combined across studies in stage 2. The study-specific estimates are assumed to arise from normal distributions with known variances equal to their corresponding estimates. In contrast, a one-stage analysis estimates all parameters simultaneously. A Bayesian one-stage approach offers additional advantages, such as the acknowledgement of uncertainty in all parameters and greater flexibility. However, there are situations when a two-stage strategy is compelling, e.g. when study-specific analyses are complex and/or time consuming. We present a novel method for fitting the full Bayesian model in two stages, hence benefiting from its advantages while retaining the convenience and flexibility of a two-stage approach. Using Markov chain Monte Carlo methods, posteriors for the parameters of interest are derived separately for each study. These are then used as proposal distributions in a computationally efficient second stage. We illustrate these ideas on a small binomial data set; we also analyse motivating data on the growth and rupture of abdominal aortic aneurysms. The two-stage Bayesian approach closely reproduces a one-stage analysis when it can be undertaken, but can also be easily carried out when a one-stage approach is difficult or impossible.
Finite Mixture Modelling in Mass Spectrometry Analysis
Authors: Melnykov Volodymyr 
Published: September 2013

SummarySecondary ion mass spectrometry is a popular physical technique that allows learning elemental and chemical compositions of various substances. In some cases, the mass spectrum of the sample studied is not observable without that of the liquid solution in which the investigated substance must be placed. To separate the informative part of the observed mixed spectrum from the irrelevant liquid solution part, we develop an approach that is based on finite mixtures. Finite mixture modelling is a statistical technique that is capable of accounting for various shapes and patterns in data. We discuss a mixture model constructed with bell-shaped discrete component distributions that can be employed in the mass spectrometry framework for extracting the informative part of the mixed spectrum of organic objects. The results for assessing variability in the mass spectrum extracted are derived. The procedure is thoroughly illustrated on simulated data sets and applied to several real life problems that are concerned with complex organic dyes placed in a glycerol liquid matrix. The results obtained have clear chemical interpretation and suggest that the approach proposed is very promising.
Assessing the Heterogeneity of Treatment Effects via Potential Outcomes of Individual Patients
Authors: Zhang Zhiwei , Wang Chenguang , Nie Lei , Soon Guoxing 
Published: September 2013

SummaryThere is growing interest in understanding the heterogeneity of treatment effects (HTE), which has important implications in treatment evaluation and selection. The standard approach to assessing HTE (i.e. subgroup analyses based on known effect modifiers) is informative about the heterogeneity between subpopulations but not within. It is arguably more informative to assess HTE in terms of individual treatment effects, which can be defined by using potential outcomes. However, estimation of HTE based on potential outcomes is challenged by the lack of complete identifiability. The paper proposes methods to deal with the identifiability problem by using relevant information in baseline covariates and repeated measurements. If a set of covariates is sufficient for explaining the dependence between potential outcomes, the joint distribution of potential outcomes and hence all measures of HTE will then be identified under a conditional independence assumption. Possible violations of this assumption can be addressed by including a random effect to account for residual dependence or by specifying the conditional dependence structure directly. The methods proposed are shown to reduce effectively the uncertainty about HTE in a trial of human immunodeficiency virus.
The Use of Sample Weights in Multivariate Multilevel Models with an Application to Income Data Collected by Using a Rotating Panel Survey
Authors: Veiga Alinne , Smith Peter W. F. , Brown James J. 
Published: September 2013

SummaryLongitudinal data from labour force surveys permit the investigation of income dynamics at the individual level. However, the data often originate from surveys with a complex multistage sampling scheme. In addition, the hierarchical structure of the data that is imposed by the different stages of the sampling scheme often represents the natural grouping in the population. Motivated by how income dynamics differ between the formal and informal sectors of the Brazilian economy and the data structure of the Brazilian Labour Force Survey, we extend the probability-weighted iterative generalized least squares estimation method. Our method is used to fit multivariate multilevel models to the Brazilian Labour Force Survey data where the covariance structure between occasions at the individual level is modelled. We conclude that there are significant income differentials and that incorporating the weights in the parameter estimation has some effect on the estimated coefficients and standard errors.
The Use of Accuracy Indicators to Correct for Survey Measurement Error
Authors: Da Silva Damião N. , Skinner Chris 
Published: September 2013

SummaryAn accuracy indicator is an observed variable which is related to the size of measurement error. Basic and extended models are introduced to represent the properties of a binary accuracy indicator. Under specified assumptions, it is shown that an accuracy indicator can identify a measurement error model. An approach to estimating a distribution function is presented together with methodology for variance estimation. The approach is applied to data on earnings from the British Household Panel Survey, where the accuracy indicator is whether or not a payslip is observed. A validation study provides justification for the modelling assumptions.
Handling Initial Conditions and Endogenous Covariates in Dynamic/Transition Models for Binary Data with Unobserved Heterogeneity
Authors: Skrondal Anders , Rabe-Hesketh Sophia 
Published: September 2013

SummaryDistinguishing between longitudinal dependence due to the effects of previous responses on subsequent responses and dependence due to unobserved heterogeneity is important in many disciplines. For example, wheezing is an inflammatory reaction that may ‘remodel’ a child's airway structure and thereby affect the probability of future wheezing (state dependence). Alternatively, children could vary in their susceptibilities because of unobserved covariates such as genes (unobserved heterogeneity). For binary responses, distinguishing between state dependence and unobserved heterogeneity is typically accomplished by using dynamic/transition models that include both a lagged response and a random intercept. Naive maximum likelihood estimators can be severely inconsistent because of two kinds of endogeneity problem: lack of independence of the initial response and the random intercept (the initial conditions problem) and lack of independence of the covariates and the random intercept (the endogenous covariates problem). We clarify and unify previous work on handling these problems in the disconnected literatures of statistics and econometrics, suggest improved methods, investigate the asymptotic performance of competing methods and provide practical recommendations. The recommended methods are applied to longitudinal data on children's wheezing, where we investigate the extent of state dependence and unobserved heterogeneity and whether there is an effect of maternal smoking.
Flexible Regression Models Over River Networks
Authors: O'Donnell David , Rushworth Alastair , Bowman Adrian W. , Marian Scott E. , Hallard Mark 
Published: September 2013

SummaryMany statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along complex paths and, in addition, account must be taken of the relative flows of water into and out of confluences. Suitable models for this type of data have been constructed based on covariance functions. The aim of the paper is to place the focus on underlying spatial trends by adopting a regression formulation and using methods which allow smooth but flexible patterns. Specifically, kernel methods and penalized splines are investigated, with the latter proving more suitable from both computational and modelling perspectives. In addition to their use in a purely spatial setting, penalized splines also offer a convenient route to the construction of spatiotemporal models, where data are available over time as well as over space. Models which include main effects and spatiotemporal interactions, as well as seasonal terms and interactions, are constructed for data on nitrate pollution in the River Tweed. The results give valuable insight into the changes in water quality in both space and time.
Characterizing Spatial and Chronological Target Selection of Serial Offenders
Authors: Hering Amanda S. , Bair Sean 
Published: September 2013

SummaryUsing 31 unique crime series from two US cities, the spatial pattern that individual criminal decision makers follow is investigated. Locations within a city vary in the likelihood for crimes of a given type to occur, and this is accounted for by using kernel density estimation based on all crimes of each type. Kernel density estimation is highly influenced by the bandwidth, so an objective approach is used to select the estimate. Then, the kernel density estimate for each type of crime is incorporated in an inhomogeneous K-function that can identify significant spatial clustering and/or uniformity at various spatial scales for each crime series. We find that robbery series are more likely to exhibit uniformity than burglary series, which tend to show strong clustering. In addition, the order in which new crimes are added to a series is found to follow an interesting pattern that does not always support the theory of offender as forager in which criminals first cluster crimes and then gradually disperse them. Half of the robbery series exhibit a spatial distribution that begins dispersed and develops clusters as increasingly more events are added to the series, indicating that they often return to locations of earlier crimes after having committed crimes elsewhere.
Latent Variable Models that Account for Atypical Responses
Authors: Moustaki Irini , Knott Martin 
Published: September 2013

SummaryResponses to a set of indicators, or items, or variables are often used in social sciences for measuring unobserved constructs as attitudes. Latent variable models, which are also known as factor analysis models, are used for linking the observed responses to the latent constructs. Often, some respondents provide random responses to the items. We distinguish between two response strategies: a primary response strategy that is driven by the latent variable of interest and a secondary response strategy that can be characterized as random. We propose an extended latent variable model for binary responses that models the secondary response mechanism through a latent class model implemented as an unobserved pseudoitem. We allow for the secondary response strategy that is employed by some respondents to be a function of the latent variable of interest and covariates. Not taking into account the proportion of responses generated by secondary strategies in the data can affect parameter estimates and the goodness of fit. Covariates are used to identify the demographic characteristics of those who choose a secondary response strategy and increase the precision of model estimation. We fit our proposed model to two data sets: one from a section of the 1990 Workplace Industrial Relations Survey and one from a section of the 2007 British Social Attitudes Survey.
At the mercy of others – for better or worse
Authors: Dybvik Tove Katrin , Gjengedal Eva , Lykkeslet Else 
Published: September 2013

Many elderly people spend their remaining days in nursing homes and are thus affected by issues pertaining to a good life in a nursing home. The Norwegian study presented in this article aimed to gain greater knowledge and understanding of the significant factors associated with a good life in a nursing home. Qualitative in‐depth interviews with six nursing home residents aged 80 to 96 years were conducted to collect data. The aim of the analysis was to identify typical aspects related to a good life in a nursing home according to descriptions provided by nursing home residents. The findings identified both physical and psychosocial elements, and the following three main themes emerged: receiving help when the body fails is good, meaningful days despite loss and limitations, and time as a friend or foe. In conclusion, our findings indicate that it is important to understand that a good life has a deeper meaning because it challenges the existential preconditions for life.
Amyloplast displacement is necessary for gravisensing in Arabidopsis shoots as revealed by a centrifuge microscope
Authors: Toyota Masatsugu , Ikeda Norifumi , Sawai‐Toyota Satoe , Kato Takehide , Gilroy Simon , Tasaka Masao , Morita Miyo Terao 
Published: September 2013

SummaryThe starch‐statolith hypothesis proposes that starch‐filled amyloplasts act as statoliths in plant gravisensing, moving in response to the gravity vector and signaling its direction. However, recent studies suggest that amyloplasts show continuous, complex movements in Arabidopsis shoots, contradicting the idea of a so‐called ‘static’ or ‘settled’ statolith. Here, we show that amyloplast movement underlies shoot gravisensing by using a custom‐designed centrifuge microscope in combination with analysis of gravitropic mutants. The centrifuge microscope revealed that sedimentary movements of amyloplasts under hypergravity conditions are linearly correlated with gravitropic curvature in wild‐type stems. We next analyzed the hypergravity response in the shoot gravitropism 2 (sgr2) mutant, which exhibits neither a shoot gravitropic response nor amyloplast sedimentation at 1 g. sgr2 mutants were able to sense and respond to gravity under 30 g conditions, during which the amyloplasts sedimented. These findings are consistent with amyloplast redistribution resulting from gravity‐driven movements triggering shoot gravisensing. To further support this idea, we examined two additional gravitropic mutants, phosphoglucomutase (pgm) and sgr9, which show abnormal amyloplast distribution and reduced gravitropism at 1 g. We found that the correlation between hypergravity‐induced amyloplast sedimentation and gravitropic curvature of these mutants was identical to that of wild‐type plants. These observations suggest that Arabidopsis shoots have a gravisensing mechanism that linearly converts the number of amyloplasts that settle to the ‘bottom’ of the cell into gravitropic signals. Further, the restoration of the gravitropic response by hypergravity in the gravitropic mutants that we tested indicates that these lines probably have a functional gravisensing mechanism that is not triggered at 1 g.