From the Apennines to the Alps: colonization genetics of the naturally expanding Italian wolf (Canis lupus) population

Wolves in Italy strongly declined in the past and were confined south of the Alps since the turn of the last century, reduced in the 1970s to approximately 100 individuals surviving in two fragmented subpopulations in the central-southern Apennines. The Italian wolves are presently expanding in the Apennines, and started to recolonize the western Alps in Italy, France and Switzerland about 16 years ago. In this study, we used a population genetic approach to elucidate some aspects of the wolf recolonization process. DNA extracted from 3068 tissue and scat samples collected in the Apennines (the source populations) and in the Alps (the colony), were genotyped at 12 microsatellite loci aiming to assess (i) the strength of the bottleneck and founder effects during the onset of colonization; (ii) the rates of gene flow between source and colony; and (iii) the minimum number of colonizers that are needed to explain the genetic variability observed in the colony. We identified a total of 435 distinct wolf genotypes, which showed that wolves in the Alps: (i) have significantly lower genetic diversity (heterozygosity, allelic richness, number of private alleles) than wolves in the Apennines; (ii) are genetically distinct using pairwise F(ST) values, population assignment test and Bayesian clustering; (iii) are not in genetic equilibrium (significant bottleneck test). Spatial autocorrelations are significant among samples separated up to c. 230 km, roughly correspondent to the apparent gap in permanent wolf presence between the Alps and north Apennines. The estimated number of first-generation migrants indicates that migration has been unidirectional and male-biased, from the Apennines to the Alps, and that wolves in southern Italy did not contribute to the Alpine population. These results suggest that: (i) the Alps were colonized by a few long-range migrating wolves originating in the north Apennine subpopulation; (ii) during the colonization process there has been a moderate bottleneck; and (iii) gene flow between sources and colonies was moderate (corresponding to 1.25-2.50 wolves per generation), despite high potential for dispersal. Bottleneck simulations showed that a total of c. 8-16 effective founders are needed to explain the genetic diversity observed in the Alps. Levels of genetic diversity in the expanding Alpine wolf population, and the permanence of genetic structuring, will depend on the future rates of gene flow among distinct wolf subpopulation fragments.

Does this child have a foreign body aspiration?

Context: Foreign body aspiration (FbA) is a serious problem in children. Accurate clinical and radiographic diagnosis is important because missed or delayed diagnosis can result in respiratory difficulties ranging from life-treatening airway obstruction to chronic wheezing or recurrent pneumonia. Bronchoscopy also has risks and accurate clinical and radiographc diagnosis can support the decision of bronchoscopy. Objective: To rewiev the diagnostic accuracy of clinical presentation (CP) and pulmonary radiograph (PR) for the diagnosis of FbA. There is no previous rewievMethods: A search of Medline is conducted for articles containing data regarding CP and PR signes of FbA. Calculation of likelihood ratios (LR) and pre and post test probability using Bayes theorem were performed for all signs of CP and PR. Inclusion criteria: Articles containing prospective data regarding CP and PR of FbA. Exclusion criteria: Retrospectives studies. Articles containing incomplete data for calculation of LR. Results: Five prospectives studies are included with a total of 585 patients. Prevalence of FbA is 63% in children suspected of FbA. If CP is normal, probability of FbA is 25% and if PR is normal, probability is 14%. If CP is pathologic, probability of FbA is 69-76% with presence of cough (LR = 1.32) or dyspnea (LR = 1.84) or localized crackles (LR = 1.5). Probability is 81-88% if cyanosis (LR = 4.8) or decreased breaths sounds (LR = 4.3) or asymetric auscultation (LR = 2.9) or localized wheezing (LR = 2.5) are present. When CP is anormal and PR show mediatinal shift (LR = 100), pneumomediatin (LR = 100), radio opaque foreign body (LR = 100), lobar distention (LR = 4), atelectasis (LR = 2.5), inspiratory/expiratory abnormal (LR = 7), the probability of FbA is 96-100%. If CP is normal and PR is abnormal the probability is 40-100%. If CP is abnormal and PR is normal the probability is 55-75%. Conclusions: This rewiev of prospective studies demonstrates the importance of CP and PR and an algorithm can be proposed. When CP is abnormal with or without PR pathologic, the probability of FbA is high and bronchoscopy is indicated. When CP and PR are normal the probability of FbA is low and bronchoscopy is not necessary immediatly, observation should be proposed. This approach should be validated with prospective study.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

Linear least squares estimation of the first order moving average parameter

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the first order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed

Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.

Inferring landscape effects on dispersal from genetic distances: how far can we go?

Functional connectivity affects demography and gene dynamics in fragmented populations. Besides species-specific dispersal ability, the connectivity between local populations is affected by the landscape elements encountered during dispersal. Documenting these effects is thus a central issue for the conservation and management of fragmented populations. In this study, we compare the power and accuracy of three methods (partial correlations, regressions and Approximate Bayesian Computations) that use genetic distances to infer the effect of landscape upon dispersal. We use stochastic individual-based simulations of fragmented populations surrounded by landscape elements that differ in their permeability to dispersal. The power and accuracy of all three methods are good when there is a strong contrast between the permeability of different landscape elements. The power and accuracy can be further improved by restricting analyses to adjacent pairs of populations. Landscape elements that strongly impede dispersal are the easiest to identify. However, power and accuracy decrease drastically when landscape complexity increases and the contrast between the permeability of landscape elements decreases. We provide guidelines for future studies and underline the needs to evaluate or develop approaches that are more powerful.

Performance anxiety : cardiorespiratory activity in high- and low-anxious professional music students in a performance situation

Questionnaire studies indicate that high-anxious musicians may suffer from hyperventilation symptoms before and/or during performance. Reported symptoms include amongst others shortness of breath, fast or deep breathing, dizziness and thumping heart. A self-report study by Widmer, Conway, Cohen and Davies (1997) shows that up to seventy percent of the tested highly anxious musicians are hyperventilators during performance. However, no study has yet tested if these self-reported symptoms reflect actual cardiorespiratory changes just before and during performance. Disturbances in breathing patterns and hyperventilation may negatively affect the performance quality in stressful performance situations. The main goal of this study is to determine if music performance anxiety is manifest physiologically in specific correlates of cardiorespiratory activity. We studied 74 professional music students of Swiss Music Universities divided into two groups (high- and lowanxious) based on their self-reported performance anxiety (State-Trait Anxiety Inventory by Spielberger). The students were tested in three distinct situations: baseline, performance without audience, performance with audience. We measured a) breathing patterns, end-tidal carbon dioxide, which is a good non-invasive estimator for hyperventilation, and cardiac activation and b) self-perceived emotions and self-perceived physiological activation. Analyses of heart rate, respiratory rate, self-perceived palpitations, self-perceived shortness of breath and self-perceived anxiety for the 15 most and the 15 least anxious musicians show that high-anxious and low-anxious music students have a comparable physiological activation during the different measurement periods. However, highanxious music students feel significantly more anxious and perceive significantly stronger palpitations and significantly stronger shortness of breath just before and during a public performance. The results indicate that low- and high-anxious music students a) do not differ in the considered physiological responses and b) differ in the considered self-perceived physiological symptoms and the selfreported anxiety before and/or during a public performance.

Additional data for nuclear DNA give new insights into the phylogenetic position of Sorex granarius within the Sorex araneus group.

Many species contain genetic lineages that are phylogenetically intermixed with those of other species. In the Sorex araneus group, previous results based on mtDNA and Y chromosome sequence data showed an incongruent position of Sorex granarius within this group. In this study, we explored the relationship between species within the S. araneus group, aiming to resolve the particular position of S. granarius. In this context, we sequenced a total of 2447 base pairs (bp) of X-linked and nuclear genes from 47 individuals of the S. araneus group. The same taxa were also analyzed within a Bayesian framework with nine autosomal microsatellites. These analyses revealed that all markers apart from mtDNA showed similar patterns, suggesting that the problematic position of S. granarius is best explained by an incongruent behavior by mtDNA. Given their close phylogenetic relationship and their close geographic distribution, the most likely explanation for this pattern is past mtDNA introgression from S. araneus race Carlit to S. granarius.

Robust accelerated failure time regression

Robust estimators for accelerated failure time models with asymmetric (or symmetric) error distribution and censored observations are proposed. It is assumed that the error model belongs to a log-location-scale family of distributions and that the mean response is the parameter of interest. Since scale is a main component of mean, scale is not treated as a nuisance parameter. A three steps procedure is proposed. In the first step, an initial high breakdown point S estimate is computed. In the second step, observations that are unlikely under the estimated model are rejected or down weighted. Finally, a weighted maximum likelihood estimate is computed. To define the estimates, functions of censored residuals are replaced by their estimated conditional expectation given that the response is larger than the observed censored value. The rejection rule in the second step is based on an adaptive cut-off that, asymptotically, does not reject any observation when the data are generat ed according to the model. Therefore, the final estimate attains full efficiency at the model, with respect to the maximum likelihood estimate, while maintaining the breakdown point of the initial estimator. Asymptotic results are provided. The new procedure is evaluated with the help of Monte Carlo simulations. Two examples with real data are discussed.

Efficacy, tolerability and risk factors for virological failure of darunavir-based therapy for treatment-experienced HIV-infected patients: the Swiss HIV Cohort Study.

OBJECTIVES: Darunavir was designed for activity against HIV resistant to other protease inhibitors (PIs). We assessed the efficacy, tolerability and risk factors for virological failure of darunavir for treatment-experienced patients seen in clinical practice. METHODS: We included all patients in the Swiss HIV Cohort Study starting darunavir after recording a viral load above 1000 HIV-1 RNA copies/mL given prior exposure to both PIs and nonnucleoside reverse transcriptase inhibitors. We followed these patients for up to 72 weeks, assessed virological failure using different loss of virological response algorithms and evaluated risk factors for virological failure using a Bayesian method to fit discrete Cox proportional hazard models. RESULTS: Among 130 treatment-experienced patients starting darunavir, the median age was 47 years, the median duration of HIV infection was 16 years, and 82% received mono or dual antiretroviral therapy before starting highly active antiretroviral therapy. During a median patient follow-up period of 45 weeks, 17% of patients stopped taking darunavir after a median exposure of 20 weeks. In patients followed beyond 48 weeks, the rate of virological failure at 48 weeks was at most 20%. Virological failure was more likely where patients had previously failed on both amprenavir and saquinavir and as the number of previously failed PI regimens increased. CONCLUSIONS: As a component of therapy for treatment-experienced patients, darunavir can achieve a similar efficacy and tolerability in clinical practice to that seen in clinical trials. Clinicians should consider whether a patient has failed on both amprenavir and saquinavir and the number of failed PI regimens before prescribing darunavir.

On the relationship between connections and the asymptotic properties of predictive distributions

In a recent paper, Komaki studied the second-order asymptotic properties of predictive distributions, using the Kullback-Leibler divergence as a loss function. He showed that estimative distributions with asymptotically efficient estimators can be improved by predictive distributions that do not belong to the model. The model is assumed to be a multidimensional curved exponential family. In this paper we generalize the result assuming as a loss function any f divergence. A relationship arises between alpha connections and optimal predictive distributions. In particular, using an alpha divergence to measure the goodness of a predictive distribution, the optimal shift of the estimate distribution is related to alpha-covariant derivatives. The expression that we obtain for the asymptotic risk is also useful to study the higher-order asymptotic properties of an estimator, in the mentioned class of loss functions.

Evolutionary history of the greater white-toothed shrew (Crocidura russula) inferred from analysis of mtDNA, Y, and X chromosome markers.

We investigate the evolutionary history of the greater white-toothed shrew across its distribution in northern Africa and mainland Europe using sex-specific (mtDNA and Y chromosome) and biparental (X chromosome) markers. All three loci confirm a large divergence between eastern (Tunisia and Sardinia) and western (Morocco and mainland Europe) lineages, and application of a molecular clock to mtDNA divergence estimates indicates a more ancient separation (2.25 M yr ago) than described by some previous studies, supporting claims for taxonomic revision. Moroccan ancestry for the mainland European population is inconclusive from phylogenetic trees, but is supported by greater nucleotide diversity and a more ancient population expansion in Morocco than in Europe. Signatures of rapid population expansion in mtDNA, combined with low X and Y chromosome diversity, suggest a single colonization of mainland Europe by a small number of Moroccan shrews >38 K yr ago. This study illustrates that multilocus genetic analyses can facilitate the interpretation of species' evolutionary history but that phylogeographic inference using X and Y chromosomes is restricted by low levels of observed polymorphism.

Lag-one autocorrelation in short series: Estimation and hypothesis testing

In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is studied. The performance of the ten lag-one autocorrelation estimators is compared in terms of Mean Square Error (combining bias and variance) using data series generated by Monte Carlo simulation. The results show that there is not a single optimal estimator for all conditions, suggesting that the estimator ought to be chosen according to sample size and to the information available of the possible direction of the serial dependence. Additionally, the probability of labelling an actually existing autocorrelation as statistically significant is explored using Monte Carlo sampling. The power estimates obtained are quite similar among the tests associated with the different estimators. These estimates evidence the small probability of detecting autocorrelation in series with less than 20 measurement times.