Maria Eugenia Castellanos

Quantitative methods in ethnobotany and ethnopharmacology: considering the overall flora--hypothesis testing for over- and underused plant families with the Bayesian approach.

ETHNOPHARMACOLOGICAL RELEVANCE We introduce and explain the advantages of the Bayesian approach and exemplify the method with an analysis of the medicinal flora of Campania, Italy. The Bayesian approach is a new method, which allows to compare medicinal floras with the overall flora of a given area and to investigate over- and underused plant families. In contrast to previously used methods (regression analysis and binomial method) it considers the inherent uncertainty around the analyzed data. MATERIALS AND METHODS The medicinal flora with 423 species was compiled based on nine studies on local medicinal plant use in Campania. The total flora comprises 2237 species belonging to 128 families. Statistical analysis was performed with the Bayesian method and the binomial method. An approximated χ(2)-test was used to analyze the relationship between use categories and higher taxonomic groups. RESULTS Among the larger plant families we find the Lamiaceae, Rosaceae, and Malvaceae, to be overused in the local medicine of Campania and the Orchidaceae, Caryophyllaceae, Poaceae, and Fabaceae to be underused compared to the overall flora. Furthermore, do specific medicinal uses tend to be correlated with taxonomic plant groups. For example, are the Monocots heavily used for urological complaints. CONCLUSIONS Testing for over- and underused taxonomic groups of a flora with the Bayesian method is easy to adopt and can readily be calculated in excel spreadsheets using the excel function Inverse beta (INV.BETA). In contrast to the binomial method the presented method is also suitable for small datasets. With larger datasets the two methods tend to converge. However, results are generally more conservative with the Bayesian method pointing out fewer families as over- or underused.

An imprecise probability approach for the detection of over and underused taxonomic groups with the Campania (Italy) and the Sierra Popoluca (Mexico) medicinal flora

AIM OF THE STUDY We use the IDM model to test for over- and underuse of plant taxa as source for medicine. In contrast to the Bayes approach, which only considers the uncertainty around the data of medicinal plant surveys, the IDM model also takes the uncertainty around the inventory of the flora into account, which is used for the comparison between medicinal and local floras. MATERIALS AND METHODS Statistical analysis of the medicinal flora of Campania (Italy) and of the medicinal flora used by the Sierra Popoluca (Mexico) was performed with the IDM model and the Bayes approach. For Campania 423 medicinal plants and 2237 vascular plant species and for the Sierra Popoluca 605 medicinal plants and 2317 vascular plant species were considered. RESULTS The IDM model (s=4) indicates for Campania the Lamiaceae and Rosaceae as overused, and the Caryophyllaceae, Poaceae, and Orchidaceae as underused. Among the Popoluca the Asteraceae and Piperaceae turn out to be overused, while Cyperaceae, Poaceae, and Orchidaceae are underused. In comparison with the Bayes approach, the IDM approach indicates fewer families as over- or underused. CONCLUSIONS The IDM model leads to more conservative results compared to the Bayes approach. Only relatively few taxa are indicated as over- or underused. The larger the families (n(j)s) are, the more similar do the results of the two approaches turn out. In contrast to the Bayes approach, small taxa with most or all species used as medicine (e.g., n(j)=2, x(j)=2) tend not to be indicated as overused with the IDM model.

A default Bayesian approach for regression on extremes

A default Bayesian approach to predict extreme events in the presence of explanatory variables is presented. In the prediction model, covariates are introduced, using a non-homogenous Poisson-Generalized Pareto Distribution (GPD) point process, which allows for variation in the tail behaviour. The prior distribution proposed is based on a Jeffreys’ rule for regression parameters, extending the results previously obtained for an independent and identically distributed random sample drawn from the GPD. Special attention is given to mean return levels as an important summarizer. Inference is performed approximately via Markov chain Monte Carlo methods and the posterior distribution turns out to be relatively easy to be computed. The model is applied to two real datasets from meteorological applications.

Contact-State Classification in Human-Demonstrated Robot Compliant Motion Tasks Using the Boosting Algorithm

Robot programming by demonstration is a robot programming paradigm in which a human operator directly demonstrates the task to be performed. In this paper, we focus on programming by demonstration of compliant motion tasks, which are tasks that involve contacts between an object manipulated by the robot and the environment in which it operates. Critical issues in this paradigm are to distinguish essential actions from those that are not relevant for the correct execution of the task and to transform this information into a robot-independent representation. Essential actions in compliant motion tasks are the contacts that take place, and therefore, it is important to understand the sequence of contact states that occur during a demonstration, called contact classification or contact segmentation. We propose a contact classification algorithm based on a supervised learning algorithm, in particular on a stochastic gradient boosting algorithm. The approach described in this paper is accurate and does not depend on the geometric model of the objects involved in the demonstration. It neither relies on the kinestatic model of the contact interactions nor on the contact state graph, whose computation is usually of prohibitive complexity even for very simple geometric object models.

MCMC methods to approximate conditional predictive distributions

Sampling from conditional distributions is a problem often encountered in statistics when inferences are based on conditional distributions which are not of closed-form. Several Markov chain Monte Carlo (MCMC) algorithms to simulate from them are proposed. Potential problems are pointed out and some suitable modifications are suggested. Approximations based on conditioning sets are also explored. The issues are illustrated within a specific statistical tool for Bayesian model checking, and compared in an example. An example in frequentist conditional testing is also given.

Bayesian analysis of a disability model for lung cancer survival

Bayesian reasoning, survival analysis and multi-state models are used to assess survival times for Stage IV non-small-cell lung cancer patients and the evolution of the disease over time. Bayesian estimation is done using minimum informative priors for the Weibull regression survival model, leading to an automatic inferential procedure. Markov chain Monte Carlo methods have been used for approximating posterior distributions and the Bayesian information criterion has been considered for covariate selection. In particular, the posterior distribution of the transition probabilities, resulting from the multi-state model, constitutes a very interesting tool which could be useful to help oncologists and patients make efficient and effective decisions.

A strategy analysis for genetic association studies with known inbreeding

BackgroundAssociation studies consist in identifying the genetic variants which are related to a specific disease through the use of statistical multiple hypothesis testing or segregation analysis in pedigrees. This type of studies has been very successful in the case of Mendelian monogenic disorders while it has been less successful in identifying genetic variants related to complex diseases where the insurgence depends on the interactions between different genes and the environment. The current technology allows to genotype more than a million of markers and this number has been rapidly increasing in the last years with the imputation based on templates sets and whole genome sequencing. This type of data introduces a great amount of noise in the statistical analysis and usually requires a great number of samples. Current methods seldom take into account gene-gene and gene-environment interactions which are fundamental especially in complex diseases. In this paper we propose to use a non-parametric additive model to detect the genetic variants related to diseases which accounts for interactions of unknown order. Although this is not new to the current literature, we show that in an isolated population, where the most related subjects share also most of their genetic code, the use of additive models may be improved if the available genealogical tree is taken into account. Specifically, we form a sample of cases and controls with the highest inbreeding by means of the Hungarian method, and estimate the set of genes/environmental variables, associated with the disease, by means of Random Forest.ResultsWe have evidence, from statistical theory, simulations and two applications, that we build a suitable procedure to eliminate stratification between cases and controls and that it also has enough precision in identifying genetic variants responsible for a disease. This procedure has been successfully used for the beta-thalassemia, which is a well known Mendelian disease, and also to the common asthma where we have identified candidate genes that underlie to the susceptibility of the asthma. Some of such candidate genes have been also found related to common asthma in the current literature.ConclusionsThe data analysis approach, based on selecting the most related cases and controls along with the Random Forest model, is a powerful tool for detecting genetic variants associated to a disease in isolated populations. Moreover, this method provides also a prediction model that has accuracy in estimating the unknown disease status and that can be generally used to build kit tests for a wide class of Mendelian diseases.

Risk Analysis for Unintentional Slide Deployment During Airline Operations

We present a risk analysis undertaken to mitigate problems in relation to the unintended deployment of slides under normal operations within a commercial airline. This type of incident entails relevant costs for the airline industry. After assessing the likelihood and severity of its consequences, we conclude that such risks need to be managed. We then evaluate the effectiveness of various countermeasures, describing and justifying the chosen ones. We also discuss several issues faced when implementing and communicating the proposed measures, thus fully illustrating the risk analysis process.

From cumulative cultural transmission to evidence-based medicine: evolution of medicinal plant knowledge in Southern Italy.

In Mediterranean cultures written records of medicinal plant use have a long tradition. This written record contributed to building a consensus about what was perceived to be an efficacious pharmacopeia. Passed down through millennia, these scripts have transmitted knowledge about plant uses, with high fidelity, to scholars and laypersons alike. Herbal medicines importance and the long-standing written record call for a better understanding of the mechanisms influencing the transmission of contemporary medicinal plant knowledge. Here we contextualize herbal medicine within evolutionary medicine and cultural evolution. Cumulative knowledge transmission is approached by estimating the causal effect of two seminal scripts about materia medica written by Dioscorides and Galen, two classical Greco-Roman physicians, on todays medicinal plant use in the Southern Italian regions of Campania, Sardinia, and Sicily. Plant-use combinations are treated as transmissible cultural traits (or “memes”), which in analogy to the biological evolution of genetic traits, are subjected to mutation and selection. Our results suggest that until today ancient scripts have exerted a strong influence on the use of herbal medicine. We conclude that the repeated empirical testing and scientific study of health care claims is guiding and shaping the selection of efficacious treatments and evidence-based herbal medicine.

Unscaled Bayes factors for multiple hypothesis testing in microarray experiments.

Multiple hypothesis testing collects a series of techniques usually based on p-values as a summary of the available evidence from many statistical tests. In hypothesis testing, under a Bayesian perspective, the evidence for a specified hypothesis against an alternative, conditionally on data, is given by the Bayes factor. In this study, we approach multiple hypothesis testing based on both Bayes factors and p-values, regarding multiple hypothesis testing as a multiple model selection problem. To obtain the Bayes factors we assume default priors that are typically improper. In this case, the Bayes factor is usually undetermined due to the ratio of prior pseudo-constants. We show that ignoring prior pseudo-constants leads to unscaled Bayes factor which do not invalidate the inferential procedure in multiple hypothesis testing, because they are used within a comparative scheme. In fact, using partial information from the p-values, we are able to approximate the sampling null distribution of the unscaled Bayes factor and use it within Efrons multiple testing procedure. The simulation study suggests that under normal sampling model and even with small sample sizes, our approach provides false positive and false negative proportions that are less than other common multiple hypothesis testing approaches based only on p-values. The proposed procedure is illustrated in two simulation studies, and the advantages of its use are showed in the analysis of two microarray experiments.