Language is the best example of a cultural evolutionary system, able to retain a phylogenetic signal over many thousands of years. The temporal stability (conservatism) of basic vocabulary is relatively well understood, but the stability of the structural properties of language (phonology, morphology, syntax) is still unclear. Here we report an extensive Bayesian phylogenetic investigation of the structural stability of numerous features across many language families and we introduce a novel method for analyzing the relationships between the "stability profiles" of language families. We found that there is a strong universal component across language families, suggesting the existence of universal linguistic, cognitive and genetic constraints. Against this background, however, each language family has a distinct stability profile, and these profiles cluster by geographic area and likely deep genealogical relationships. These stability profiles seem to show, for example, the ancient historical relationships between the Siberian and American language families, presumed to be separated by at least 12,000 years, and possible connections between the Eurasian families. We also found preliminary support for the punctuated evolution of structural features of language across families, types of features and geographic areas. Thus, such higher-level properties of language seen as an evolutionary system might allow the investigation of ancient connections between languages and shed light on the peopling of the world.
Cites: Proc Biol Sci. 2011 Feb 7;278(1704):474-920810441
Cites: Philos Trans R Soc Lond B Biol Sci. 2010 Dec 12;365(1559):3903-1221041214
Cites: Nature. 2011 May 5;473(7345):79-8221490599
Cites: Hum Biol. 2011 Apr;83(2):279-9621615290
Cites: Science. 2011 Oct 21;334(6054):351-322021854
Cites: Science. 2011 Mar 25;331(6024):1599-60321436451
Cites: Science. 2001 Dec 14;294(5550):2310-411743192
Cites: Bioinformatics. 2003 Aug 12;19(12):1572-412912839
Recent research advocates the use of count models with random parameters as an alternative method for analyzing accident frequencies. In this paper a dataset composed of urban arterials in Vancouver, British Columbia, is considered where the 392 segments were clustered into 58 corridors. The main objective is to assess the corridor effects with alternate specifications. The proposed models were estimated in a Full Bayes context via Markov Chain Monte Carlo (MCMC) simulation and were compared in terms of their goodness of fit and inference. A variety of covariates were found to significantly influence accident frequencies. However, these covariates resulted in random parameters and thereby their effects on accident frequency were found to vary significantly across corridors. Further, a Poisson-lognormal (PLN) model with random parameters for each corridor provided the best fit. Apart from the improvement in goodness of fit, such an approach is useful in gaining new insights into how accident frequencies are influenced by the covariates, and in accounting for heterogeneity due to unobserved road geometrics, traffic characteristics, environmental factors and driver behavior. The inclusion of corridor effects in the mean function could also explain enough variation that some of the model covariates would be rendered non-significant and thereby affecting model inference.
Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed.
To evaluate the possibility of applying the Third Molar Eruption Predictor to all panoramic radiographs.
Panoramic radiographs were retrospectively analyzed from a 4-year follow-up study of third molars carried out at the University of Copenhagen, Denmark. The radiographs, taken at a mean age of 20.6 years, included 45 unerupted or partially erupted mandibular third molars in 28 subjects. Because the device was calibrated both with simple proportions and by use of the methods of Bayes' Decision Theory, the separation point of the device was therefore adjusted at 12 mm from the distal surface of the second molar.
The predictions of future eruption or impaction made with the calibrated device and the actual clinical outcome 4 years later were in conformity for 80% of the mandibular third molars.
The Third Molar Eruption Predictor may be applied to all panoramic radiographs, but it seems to require calibration before use.
A non-sequential Bayesian program for diagnosing acute abdominal pain was developed using an Amdahl mainframe accessed by a Texas Instrument remote terminal. Transferring the program to a MacIntosh SE/30 using hypercard was attended by increased utilisation from 15 to 44%.
Cites: Br Med J (Clin Res Ed). 1986 Sep 27;293(6550):800-43094664
Cites: Ann R Coll Surg Engl. 1990 Mar;72(2):140-62185682
The study is based on a sample of 965 children living in Oulu region (Finland), who were monitored for acute middle ear infections from birth to the age of two years. We introduce a nonparametrically defined intensity model for ear infections, which involves both fixed and time dependent covariates, such as calendar time, current age, length of breast-feeding time until present, or current type of day care. Unmeasured heterogeneity, which manifests itself in frequent infections in some children and rare in others and which cannot be explained in terms of the known covariates, is modelled by using individual frailty parameters. A Bayesian approach is proposed to solve the inferential problem. The numerical work is carried out by Monte Carlo integration (Metropolis-Hastings algorithm).
To examine the incidence, mortality and case fatality of acute coronary syndrome (ACS) in Finland during 1993-2007 and to create forecasts of the absolute numbers of ACS cases in the future, taking into account the aging of the population.
Community surveillance study and modelled forecasts of the future.
Two sets of population-based coronary event register data from Finland (FINAMI and the National Cardiovascular Disease Register (CVDR)). Bayesian age-period-cohort (APC) modelling.
24 905 observed ACS events in the FINAMI register and 364 137 in CVDR.
Observed trends of ACS events during 1993-2007, forecasted numbers of ACS cases, and the prevalence of ACS survivors until the year 2050.
In the FINAMI register, the average annual declines in age-standardised incidence of ACS were 1.6% (p
PURPOSE: To study the association between alopecia and selective serotonin reuptake inhibitors (SSRIs) by estimating reporting rates and by making association comparisons within databases of adverse drug reactions (ADRs). METHODS: All reports of alopecia with marketed SSRIs until the end of 2004 were identified in SWEDIS, the national Swedish database for spontaneously reported ADRs, and in Vigibase, the international ADR database of the World Health Organization. Total SSRI sales volumes in Sweden until the end of 2004 were obtained from the National Corporation of Swedish Pharmacies. The Bayes' Confidence Propagation Neural Network (BCPNN) method was used to estimate associations between alopecia and each of the SSRIs within the two databases. RESULTS: A total of 27 reports of alopecia were identified in SWEDIS. As two reports concerned the use of two SSRIs, there was a total of 29 drug-ADR combinations. All except three reports concerned women (88.9%). The reporting rate of alopecia in Sweden was significantly higher with sertraline compared with citalopram; 20.1 (95%CI 10.7-34.4) reports per million patient-years versus 4.5 (95%CI 1.8-9.3) reports per million patient-years. No significant differences in reporting rates were noted for the remaining SSRIs. Sertraline also showed a statistically significant association with alopecia in both SWEDIS and Vigibase. Citalopram was significantly associated with alopecia in Vigibase, but not in SWEDIS. No statistically significant associations were found for any of the other SSRIs. CONCLUSIONS: Alopecia appears to be a rare ADR to SSRIs. The risk of alopecia seems to vary between the different SSRIs, and might be higher in women than in men.
Identification of abnormalities in the developmental trajectory during infancy of future schizophrenia cases offers the potential to reveal pathogenic mechanisms of this disorder. Previous studies of head circumference in pre-schizophrenia were limited to measures at birth. The use of growth acceleration of head circumference (defined as the rate of change in head circumference) provides a more informative representation of the maturational landscape of this measure compared to studies based on static head circumference measures. To date, however, no study has examined whether HC growth acceleration differs between pre-schizophrenia cases and controls. In the present study, we employed a nested case control design of a national birth cohort in Finland. Cases with schizophrenia or schizoaffective disorder (N=375) and controls (N=375) drawn from the birth cohort were matched 1:1 on date of birth (within 1month), sex, and residence in Finland at case diagnosis. Longitudinal data were obtained on head circumference from birth through age 1. Data were analyzed using a new nonparametric Bayesian inversion method which allows for a detailed understanding of growth dynamics. Adjusting for growth velocity of height and weight, and gestational age, there was significantly accelerated growth of head circumference in females with schizophrenia from birth to 2months; the findings remained significant following Bonferroni correction (p
Effective utilisation of limited resources is a challenge for health care providers. Accurate and relevant information extracted from the length of stay distributions is useful for management purposes. Patient care episodes can be reconstructed from the comprehensive health registers, and in this paper we develop a Bayesian approach to analyse the length of care episode after a fractured hip. We model the large scale data with a flexible nonparametric multilayer perceptron network and with a parametric Weibull mixture model. To assess the performances of the models, we estimate expected utilities using predictive density as a utility measure. Since the model parameters cannot be directly compared, we focus on observables, and estimate the relevances of patient explanatory variables in predicting the length of stay. To demonstrate how the use of the nonparametric flexible model is advantageous for this complex health care data, we also study joint effects of variables in predictions, and visualise nonlinearities and interactions found in the data.