A score test for non-nested hypotheses with applications to discrete data models

This paper suggests that a convenient score test against non-nested alternatives can be constructed from the linear combination of the likelihood functions of the competing models. It is shown that this procedure is essentially a test for the correct specification of the conditional distribution of the variable of interest.

Spatial-temporal modellization of the NO2 concentration data through geostatistical tools

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO2 levels, by using geostatisti- cal approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This method- ology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

The Role of Institutions in Cross-Section Income and Panel Data Growth Models: A Deeper Investigation on the Weakness and Proliferation of Instruments

This paper investigates the role of institutions in determining per capita income levels and growth. It contributes to the empirical literature by using different variables as proxies for institutions and by developing a deeper analysis of the issues arising from the use of weak and too many instruments in per capita income and growth regressions. The cross-section estimation suggests that institutions seem to matter, regardless if they are the only explanatory variable or are combined with geographical and integration variables, although most models suffer from the issue of weak instruments. The results from the growth models provides some interesting results: there is mixed evidence on the role of institutions and such evidence is more likely to be associated with law and order and investment profile; government spending is an important policy variable; collapsing the number of instruments results in fewer significant coefficients for institutions.

Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

Genomics of wine yeast: functional analysis of new and variable genes

Projecte de recerca elaborat a partir d’una estada a l’Institut National de la Recherche Agronomique, França, entre 2007 i 2009. Saccharomyces cerevisiae ha estat el llevat utilitzat durant mil.lenis en l'elaboració de vins. Tot i així, es té poc coneixement sobre les pressions de selecció que han actuat en la modelització del genoma dels llevats vínics. S’ha seqüenciat el genoma d'una soca vínica comercial, EC1118, obtenint 31 supercontigs que cobreixen el 97% del genoma de la soca de referència, S288c. S’ha trobat que el genoma de la soca vínica es diferencia bàsicament en la possessió de 3 regions úniques que contenen 34 gens implicats en funcions claus per al procés fermentatiu. A banda, s’han dut a terme estudis de filogènia i synteny (ordre dels gens) que mostren que una d'aquestes tres regions és pròxima a una espècie relacionada amb el gènere Saccharomyces, mentre que les altres dos regions tenen un origen no-Saccharomyces. S’ha identificat mitjançant PCR i seqüenciació a Zygosaccharomyces bailii, una espècie contaminant de les fermentacions víniques, com a espècie donadora d'una de les dues regions. Les hibridacions naturals entre soques de diferents espècies dins del grup Saccharomyces sensu stricto ja han estat descrites. El treball és el primer que presenta hibridacions entre espècies Saccharomyces i no-Saccharomyces (Z. bailii, en aquest cas). També s’assenyala que les noves regions es troben freqüent i diferencialment presents entre els clades de S. cerevisiae, trobant-se de manera gairebé exclusiva en el grup de les soques víniques, suggerint que es tracta d'una adquisició recent de transferència gènica. En general, les dades demostren que el genoma de les soques víniques pateix una constant remodelació mitjançant l'adquisició de gens exògens. Els resultats suggereixen que aquests processos estan afavorits per la proximitat ecològica i estan implicats en l'adaptació molecular de les soques víniques a les condicions d'elevada concentració en sucres, poc nitrogen i elevades concentracions en etanol.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Assessing the Relationship between the Baseline Value of a Continuous Variable and Subsequent Change over Time

Analyzing the relationship between the baseline value and subsequent change of a continuous variable is a frequent matter of inquiry in cohort studies. These analyses are surprisingly complex, particularly if only two waves of data are available. It is unclear for non-biostatisticians where the complexity of this analysis lies and which statistical method is adequate.With the help of simulated longitudinal data of body mass index in children,we review statistical methods for the analysis of the association between the baseline value and subsequent change, assuming linear growth with time. Key issues in such analyses are mathematical coupling, measurement error, variability of change between individuals, and regression to the mean. Ideally, it is better to rely on multiple repeated measurements at different times and a linear random effects model is a standard approach if more than two waves of data are available. If only two waves of data are available, our simulations show that Blomqvist's method - which consists in adjusting for measurement error variance the estimated regression coefficient of observed change on baseline value - provides accurate estimates. The adequacy of the methods to assess the relationship between the baseline value and subsequent change depends on the number of data waves, the availability of information on measurement error, and the variability of change between individuals.

Capillaria spp. eggs in Patagonian archaeological sites: statistical analysis of morphometric data

Discriminant analysis was used to identify eggs of Capillaria spp. at specific level found in organic remains from an archaeological site in Patagonia, Argentina, dated of 6,540 ± 110 years before present. In order to distinguish eggshell morphology 149 eggs were measured and grouped into four arbitrary subsets. The analysis used on egg width and length discriminated them into different morphotypes (Wilks' lambda = 0.381, p < 0.05). The correlation analysis suggests that width was the most important variable to discriminate among the Capillaria spp. egg morphotypes (Pearson coefficient = 0.950, p < 0.05). The study of eggshell patterns, the relative frequency in the sample, and the morphometric data allowed us to correlate the four morphotypes with Capillaria species.

Clinical data and molecular analysis of Mycobacterium tuberculosis isolates from drug-resistant tuberculosis patients in Goiás, Brazil

Drug resistance is one of the major concerns regarding tuberculosis (TB) infection worldwide because it hampers control of the disease. Understanding the underlying mechanisms responsible for drug resistance development is of the highest importance. To investigate clinical data from drug-resistant TB patients at the Tropical Diseases Hospital, Goiás (GO), Brazil and to evaluate the molecular basis of rifampin (R) and isoniazid (H) resistance in Mycobacterium tuberculosis. Drug susceptibility testing was performed on 124 isolates from 100 patients and 24 isolates displayed resistance to R and/or H. Molecular analysis of drug resistance was performed by partial sequencing of the rpoB and katGgenes and analysis of the inhA promoter region. Similarity analysis of isolates was performed by 15 loci mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) typing. The molecular basis of drug resistance among the 24 isolates from 16 patients was confirmed in 18 isolates. Different susceptibility profiles among the isolates from the same individual were observed in five patients; using MIRU-VNTR, we have shown that those isolates were not genetically identical, with differences in one to three loci within the 15 analysed loci. Drug-resistant TB in GO is caused by M. tuberculosis strains with mutations in previously described sites of known genes and some patients harbour a mixed phenotype infection as a consequence of a single infective event; however, further and broader investigations are needed to support our findings.

What spatial data do we need to develop global mammal conservation strategies?

Spatial data on species distributions are available in two main forms, point locations and distribution maps (polygon ranges and grids). The first are often temporally and spatially biased, and too discontinuous, to be useful (untransformed) in spatial analyses. A variety of modelling approaches are used to transform point locations into maps. We discuss the attributes that point location data and distribution maps must satisfy in order to be useful in conservation planning. We recommend that before point location data are used to produce and/or evaluate distribution models, the dataset should be assessed under a set of criteria, including sample size, age of data, environmental/geographical coverage, independence, accuracy, time relevance and (often forgotten) representation of areas of permanent and natural presence of the species. Distribution maps must satisfy additional attributes if used for conservation analyses and strategies, including minimizing commission and omission errors, credibility of the source/assessors and availability for public screening. We review currently available databases for mammals globally and show that they are highly variable in complying with these attributes. The heterogeneity and weakness of spatial data seriously constrain their utility to global and also sub-global scale conservation analyses.

Fracturas inestables de radio distal. Nuestra experiencia en osteosíntesis con placa volar de ángulo variable

OBJECTIVE: assess the functional, subjective and radiological results obtained in patients treated with variable-angle locking plate for unstable distal radius fracture and compare these results with current studies. PATIENTS AND METHOD: From October of 2008 to July of 2011, 20 patients were included who had undergone intervention using the volar approach to the Flexor Carpi Radialis. The average follow up was 18 months. Both clinical and radiological results were analyzed. The Mayo Wrist Score and DASH questioner were used. RESULTS: The average age was 50 years; 50% men and 50% women. The consolidation of the fracture was verified in all cases. An average volar angle was obtained of 5º, radial inclination of 19º, radial height of 10.5 mm, and ulnar variance of -1 mm. The clinical assessment revealed an average dorsal flexion of 75º, palm flexion of 70º, supination of 75º and pronation of 73 º. The results for the DASH questionnaire showed an average of 17.8 and 82.7 for the Mayo Wrist Score. CONCLUSIONS: Our experience has provided some good results, both in functional and subjective as well as radio logical terms, similar to those found in studies with implants from the same generation.

CAC controlled services sharing a link with connectionless data services in an ATM network: a performance evaluation study

In the B-ISDN there is a provision for four classes of services, all of them supported by a single transport network (the ATM network). Three of these services, the connected oriented (CO) ones, permit connection access control (CAC) but the fourth, the connectionless oriented (CLO) one, does not. Therefore, when CLO service and CO services have to share the same ATM link, a conflict may arise. This is because a bandwidth allocation to obtain maximum statistical gain can damage the contracted ATM quality of service (QOS); and vice versa, in order to guarantee the contracted QOS, the statistical gain have to be sacrificed. The paper presents a performance evaluation study of the influence of the CLO service on a CO service (a circuit emulation service or a variable bit-rate service) when sharing the same link

Segmentation and grid generation for numerical simulations of vad connections with patient-specific data

Objectives: We are interested in the numerical simulation of the anastomotic region comprised between outflow canula of LVAD and the aorta. Segmenta¬tion, geometry reconstruction and grid generation from patient-specific data remain an issue because of the variable quality of DICOM images, in particular CT-scan (e.g. metallic noise of the device, non-aortic contrast phase). We pro¬pose a general framework to overcome this problem and create suitable grids for numerical simulations.Methods: Preliminary treatment of images is performed by reducing the level window and enhancing the contrast of the greyscale image using contrast-limited adaptive histogram equalization. A gradient anisotropic diffusion filter is applied to reduce the noise. Then, watershed segmentation algorithms and mathematical morphology filters allow reconstructing the patient geometry. This is done using the InsightToolKit library (www.itk.org). Finally the Vascular Model¬ing ToolKit (www.vmtk.org) and gmsh (www.geuz.org/gmsh) are used to create the meshes for the fluid (blood) and structure (arterial wall, outflow canula) and to a priori identify the boundary layers. The method is tested on five different patients with left ventricular assistance and who underwent a CT-scan exam.Results: This method produced good results in four patients. The anastomosis area is recovered and the generated grids are suitable for numerical simulations. In one patient the method failed to produce a good segmentation because of the small dimension of the aortic arch with respect to the image resolution.Conclusions: The described framework allows the use of data that could not be otherwise segmented by standard automatic segmentation tools. In particular the computational grids that have been generated are suitable for simulations that take into account fluid-structure interactions. Finally the presented method features a good reproducibility and fast application.