Polytomies and bayesian phylogenetic inference

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.

Polytomies and bayesian phylogenetic inference

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^

Comparison of Bayesian and frequentist approaches in clinical outcomes between hospitals

Many public health agencies and researchers are interested in comparing hospital outcomes, for example, morbidity, mortality, and hospitalization across areas and hospitals. However, since there is variation of rates in clinical trials among hospitals because of several biases, we are interested in controlling for the bias and assessing real differences in clinical practices. In this study, we compared the variations between hospitals in rates of severe Intraventricular Haemorrhage (IVH) infant using Frequentist statistical approach vs. Bayesian hierarchical model through simulation study. The template data set for simulation study was included the number of severe IVH infants of 24 intensive care units in Australian and New Zealand Neonatal Network from 1995 to 1997 in severe IVH rate in preterm babies. We evaluated the rates of severe IVH for 24 hospitals with two hierarchical models in Bayesian approach comparing their performances with the shrunken rates in Frequentist method. Gamma-Poisson (BGP) and Beta-Binomial (BBB) were introduced into Bayesian model and the shrunken estimator of Gamma-Poisson (FGP) hierarchical model using maximum likelihood method were calculated as Frequentist approach. To simulate data, the total number of infants in each hospital was kept and we analyzed the simulated data for both Bayesian and Frequentist models with two true parameters for severe IVH rate. One was the observed rate and the other was the expected severe IVH rate by adjusting for five predictors variables for the template data. The bias in the rate of severe IVH infant estimated by both models showed that Bayesian models gave less variable estimates than Frequentist model. We also discussed and compared the results from three models to examine the variation in rate of severe IVH by 20th centile rates and avoidable number of severe IVH cases. ^

Capitalizing on success against childhood HIV/AIDS in Botswana: Towards a comprehensive healthcare policy for pediatric oncology in Botswana

Pediatric HIV/AIDS in sub-Saharan Africa has been a major public health crisis with an estimated 3.5 million children infected. Baylor International Pediatric AIDS Initiative (BIPAI) has created a network of centers providing care and treatment for these children in several countries. In Botswana, where the first BIPAI center in Africa was opened, childhood mortality from HIV/AIDS is now less than 1%. Botswana is a middle-income country that previously held the highest HIV prevalence rate in the world. Efforts against HIV/AIDS have resulted in the building of a strong medical infrastructure with clear success against pediatric HIV/AIDS. The WHO predicts the next global health crisis will be cancer. Given the increased incidence of cancer in the setting of HIV/AIDS, Botswana has already implemented strategies to combat HIV-related malignancies in adults, but efforts in pediatrics have been lagging. This policy paper describes the importance of building on success against pediatric HIV/AIDS and extending this success to pediatric cancer in general. Specifically, it outlines a comprehensive pediatric cancer policy for the education and training of health professionals, the development of a pediatric cancer program, a pediatric cancer registry, public awareness efforts, and an appropriate, country specific pediatric cancer research agenda.^

(Table 1) Summary of general lithology in IODP Exp302 cores

The Cenozoic ice-rafted debris (IRD) history of the central Arctic is reconstructed utilizing the terrigenous coarse sand fraction in IODP 302 cores from 0 to 273 meters composite depth. This Holocene - middle Eocene quantitative record of terrigenous sand accumulation on the Lomonosov Ridge, along with qualitative information on grain texture and composition, confirms the interpretation that ice initiation (sea ice and glacial ice) occurred ~46 Ma in the Arctic, and provides a long-term pattern of Arctic ice expansion and decay since the middle Eocene. IRD mass accumulation rates range from 0 to 0.13 g/cm2/ka in the middle Eocene and from 0 to 0.36 g/cm2/ka in the Neogene. IRD mass accumulation rate (MAR) maxima in the Miocene and Pliocene cooccur with either glacial initiation or intensification in the sub-Arctic. The 46.25 Ma IRD onset in the central Arctic slightly precedes the earliest evidence of ice in the Antarctic, and compares in timing with a >1000 ppm decrease in atmospheric concentrations of CO2. The decline of pCO2 in the middle Eocene may have driven both poles across the temperature threshold that enabled the nucleation of glaciers on land and partial freezing of the surface Arctic Ocean, especially during times of low insolation.

Sustentabilidad sub-nacional

La finalidad del presente trabajo es realizar un análisis que complementa y actualiza el estudio realizado en el año 2011 por Molina, Correa y Morabito. En dicho estudio se mostró la situación de la deuda pública de Mendoza (fundamentalmente del nivel y composición de la misma), así como los esfuerzos fiscales que se necesitaban realizar para que la misma sea sustentable en el tiempo. A partir del análisis de la deuda pública consolidada (analizada en su nivel, composición y relación con la tasa de interés) y su vinculación con los resultados financieros de la provincia, se pretende opinar sobre las perspectivas de solvencia a mediano y largo plazo, así como determinar, en su caso, los esfuerzos fiscales necesarios para retomar la senda de la sustentabilidad, sin necesidad de producir cambios bruscos en la cuentas fiscales, que afecten a la población en general y a los contribuyentes en particular. Para tal fin se utilizarán distintos métodos y modelos que acompañen el análisis de las finanzas de las provincias en general.

Registry of samples and environmental context from the Ocean Sampling Day 2014

The Ocean Sampling Day (OSD) is a simultaneous sampling campaign of the world's oceans which took place (for the first time) on the summer solstice (June 21st) in the year 2014. These cumulative samples, related in time, space and environmental parameters, provide insights into fundamental rules describing microbial diversity and function and contribute to the blue economy through the identification of novel, ocean-derived biotechnologies. We see OSD data as a reference data set for generations of experiments to follow in the coming decade. The present data set includes a description of each sample collected during the Ocean Sampling Day 2014 and provides contextual environmental data measured concurrently with the collection of water samples for genomic analyses.

Migration and Wellbeing at the Lower Echelons of the Economy: A Study of Delhi Slums

This paper based on a primary survey of households (2004-05) in the slum clusters of Delhi examines whether migrants are likely to experience upward mobility in their place of destination or alternatively, if they merely transfer their poverty from rural areas to large cities. First, a simple bifurcation of population in terms of poor and non-poor sub-groups is examined along with the incidence of poverty across different categories of occupations and non-workers. Then, an explanation of the variations in per capita expenditure across households is provided, and a binomial logit model (poor/non-poor) is developed identifying the variables which raise (or reduce) the probability of being non-poor (or poor). Next, an estimate of the wellbeing (deprivation) index is derived from factor analysis of a large number of variables including demographic and economic aspects of households. Empirical findings suggest that while duration of migration and the wellbeing index do not have a definite relationship, migrant households who have been in the city for a very long time have a higher wellbeing index on average than those who migrated in the last ten years. This tends to support the view that migrants do not merely transfer rural poverty to urban areas, and further that population mobility yields improvement in the living standard, if only in the very long term. Implementation of "employment-cum-shelter" support schemes in the urban areas may contribute to their wellbeing.

Effectiveness and Challenges of Three Economic Corridors of the Greater Mekong Sub-region

Since the Greater Mekong Sub-region (GMS) program began in 1992, activities have expanded and flourished. The three economic corridors are composed of the East-West, North-South, and Southern; these are the most important parts of the flagship program. This article presents an evaluation of these economic corridors and their challenges in accordance with the regional distribution of population and income, population pyramids of member countries, and trade relations of member economies.

How has financial deepening affected poverty reduction in India? : empirical analysis using state-level panel data

This paper examines empirically whether financial deepening has contributed to poverty reduction in India. Using unbalanced panel data for 28 states and union territories between 1973 and 2004, we estimate models in which the poverty ratio is explained by financial deepening, controlling for international openness, inflation rate, and economic growth. From the dynamic generalised method of moments (GMM) estimation, we find that financial deepening and economic growth alleviate poverty, while international openness and the inflation rate have the opposite effect. These results are robust to changes in the poverty ratios in rural areas, urban areas, and the whole economy.

An empirical analysis on the efficiency of the microfinance investment market

This paper empirically analyzes the market efficiency of microfinance investment funds. For the empirical analysis, we use an index of the microfinance investment funds and apply two kinds of variance ratio tests to examine whether or not this index follows a random walk. We use the entire sample period from December 2003 to June 2010 as well as two sub-samples which divide the entire period before and after January 2007. The empirical evidence demonstrates that the index does not follow a random walk, suggesting that the market of the microfinance investment funds is not efficient. This result is not affected by changes in either empirical techniques or sample periods.

Assessing agglomeration economies in the Yangzi River Delta, China : a bayesian spatial econometric approach

This paper estimates the elasticity of labor productivity with respect to employment density, a widely used measure of the agglomeration effect, in the Yangtze River Delta, China. A spatial Durbin model is presented that makes explicit the influences of spatial dependence and endogeneity bias in a very simple way. Results of Bayesian estimation using the data of the year 2009 indicate that the productivity is influenced by factors correlated with density rather than density itself and that spatial spillovers of these factors of agglomeration play a significant role. They are consistent with the findings of Ke (2010) and Artis, et al. (2011) that suggest the importance of taking into account spatial dependence and hitherto omitted variables.