Influence of factors in the oral mucosa maturation pattern: a cross-sectional study applying multivariate analyses

Aim: To evaluate the association between oral health status, socio-demographic and behavioral factors with the pattern of maturity of normal epithelial oral mucosa. Methods: Exfoliative cytology specimens were collected from 117 men from the border of the tongue and floor of the mouth on opposite sides. Cells were stained with the Papanicolaou method and classified into: anucleated, superficial cells with nuclei, intermediate and parabasal cells. Quantification was made by selecting the first 100 cells in each glass slide. Sociodemographic and behavioral variables were collected from a structured questionnaire. Oral health was analyzed by clinical examination, recording decayed, missing and filled teeth index (DMFT) and use of prostheses. Multivariable linear regression models were applied. Results: No significant differences for all studied variables influenced the pattern of maturation of the oral mucosa except for alcohol consumption. There was an increase of cell surface layers of the epithelium with the chronic use of alcohol. Conclusions: It is appropriate to use Papanicolaou cytopathological technique to analyze the maturation pattern of exposed subjects, with a strong recommendation for those who use alcohol - a risk factor for oral cancer, in which a change in the proportion of cell types is easily detected.

Environmental variables, habitat discontinuity and life history shaping the genetic structure of Pomatoschistus marmoratus

Coastal lagoons are semi-isolated ecosystems exposed to wide fluctuations of environmental conditions and showing habitat fragmentation. These features may play an important role in separating species into different populations, even at small spatial scales. In this study, we evaluate the concordance between mitochondrial (previous published data) and nuclear data analyzing the genetic variability of Pomatoschistus marmoratus in five localities, inside and outside the Mar Menor coastal lagoon (SE Spain) using eight microsatellites. High genetic diversity and similar levels of allele richness were observed across all loci and localities, although significant genic and genotypic differentiation was found between populations inside and outside the lagoon. In contrast to the FST values obtained from previous mitochondrial DNA analyses (control region), the microsatellite data exhibited significant differentiation among samples inside the Mar Menor and between lagoonal and marine samples. This pattern was corroborated using Cavalli-Sforza genetic distances. The habitat fragmentation inside the coastal lagoon and among lagoon and marine localities could be acting as a barrier to gene flow and contributing to the observed genetic structure. Our results from generalized additive models point a significant link between extreme lagoonal environmental conditions (mainly maximum salinity) and P. marmoratus genetic composition. Thereby, these environmental features could be also acting on genetic structure of coastal lagoon populations of P. marmoratus favoring their genetic divergence. The mating strategy of P. marmoratus could be also influencing our results obtained from mitochondrial and nuclear DNA. Therefore, a special consideration must be done in the selection of the DNA markers depending on the reproductive strategy of the species.

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.

The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

Selection and Passage of County Land Preservation Voter Referendum: The Role of Government

County jurisdictions in America are increasingly exercising self-government in the provision of public community services through the context of second order federalism. In states exercising this form of contemporary governance, county governments with “reformed” policy-making structures and professional management practices, have begun to rival or surpass municipalities in the delivery of local services with regional implications such as environmental protection (Benton 2002, 2003; Marando and Reeves, 1993). The voter referendum, a form of direct democracy, is an important component of county land preservation and environmental protection governmental policies. The recent growth and success of land preservation voter referendums nationwide reflects an increase in citizen participation in government and their desire to protect vacant land and its natural environment from threats of over-development, urbanization and sprawl, loss of open space and farmland, deterioration of ecosystems, and inadequate park and recreational amenities. The study’s design employs a sequential, mixed method. First, a quantitative approach employs the Heckman two-step model. It is fitted with variables for the non-random sample of 227 voter referendum counties and all non-voter referendum counties in the U.S. from 1988 to 2009. Second, the qualitative data collected from the in-depth investigation of three South Florida county case studies with twelve public administrator interviews is transformed for integration with the quantitative findings. The purpose of the qualitative method is to complement, explain and enrich the statistical analysis of county demographic, socio-economic, terrain, regional, governance and government, political preference, environmentalism, and referendum-specific factors. The research finds that government factors are significant in terms of the success of land preservation voter referendums; more specifically, the presence of self-government authority (home rule charter), a reformed structure (county administrator/manager or elected executive), and environmental interest groups. In addition, this study concludes that successful counties are often located coastal, exhibit population and housing growth, and have older and more educated citizens who vote democratic in presidential elections. The analysis of case study documents and public administrator interviews finds that pragmatic considerations of timing, local politics and networking of regional stakeholders are also important features of success. Further research is suggested utilizing additional public participation, local government and public administration factors.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

The efficiency of multivariate macroeconomic forecasts

We examine the efficiency of multivariate macroeconomic forecasts by estimating a vector autoregressive model on the forecast revisions of four variables (GDP, inflation, unemployment and wages). Using a data set of professional forecasts for the G7 countries, we find evidence of cross‐series revision dynamics. Specifically, forecasts revisions are conditionally correlated to the lagged forecast revisions of other macroeconomic variables, and the sign of the correlation is as predicted by conventional economic theory. This indicates that forecasters are slow to incorporate news across variables. We show that this finding can be explained by forecast underreaction.

Análisis de las variables de marketing que afectan al valor del cliente. La permanencia como variable controlable

To know how marketing variables affect customer value is essential for a company in order to be market and customer oriented, and to improve investment efficiency in both attracting and retaining customers. Thus, the assessment of the influence of marketing variables in customer value is of prime importance. This is recognized in many empirical studies of these variables, which address the impact of a single variable (or sets of a few variables) on customer value. A comprehensive, integrated assessment of all marketing variables and their interdependencies is an arduous and complex task for researchers and marketing managers. This research proposes a theoretical model of customer value that takes into account all significant marketing variables that have been partially addressed in empirical investigations of other researchers. These marketing variables include brand and reputation, point of sale, employees, price, termination fee commitment, discounts, complementarity of products, experiences, emotions, perceived value, quality, satisfaction, switching costs, and loyalty. The model incorporates the relationship between each variable with retention and with customer value as well as the relationships between them. A special focus is placed on the empirical analysis of the termination fee commitment and its relationship with customer value. This variable is widely used in the telecommunication’s industry for its influence on customer retention from the moment of purchase. However, there is strikingly little research in this topic. A large customer database of a telecommunications company containing five years information about 63.165 customers is used for this purpose. Multivariate linear regression and ANOVA method are applied...

Development of an activity disease score in patients with uveitis (UVEDAI)

To develop a disease activity index for patients with uveitis (UVEDAI) encompassing the relevant domains of disease activity considered important among experts in this field. The steps for designing UVEDAI were: (a) Defining the construct and establishing the domains through a formal judgment of experts, (b) A two-round Delphi study with a panel of 15 experts to determine the relevant items, (c) Selection of items: A logistic regression model was developed that set ocular inflammatory activity as the dependent variable. The construct “uveitis inflammatory activity” was defined as any intraocular inflammation that included external structures (cornea) in addition to uvea. Seven domains and 15 items were identified: best-corrected visual acuity, inflammation of the anterior chamber (anterior chamber cells, hypopyon, the presence of fibrin, active posterior keratic precipitates and iris nodules), intraocular pressure, inflammation of the vitreous cavity (vitreous haze, snowballs and snowbanks), central macular edema, inflammation of the posterior pole (the presence and number of choroidal/retinal lesions, vascular inflammation and papillitis), and global assessment from both (patient and physician). From all the variables studied in the multivariate model, anterior chamber cell grade, vitreous haze, central macular edema, inflammatory vessel sheathing, papillitis, choroidal/retinal lesions and patient evaluation were included in UVEDAI. UVEDAI is an index designed to assess the global ocular inflammatory activity in patients with uveitis. It might prove worthwhile to motorize the activity of this extraarticular manifestation of some rheumatic diseases.

Using multivariate sequential patterns to improve survival prediction in intensive care burn unit

Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient’s evolution. Experiments conducted on 480 patients show the improvement of survival prediction.

Geometrical Methods in Multivariate Risk Management: Algorithms and Applications

This thesis builds a framework for evaluating downside risk from multivariate data via a special class of risk measures (RM). The peculiarity of the analysis lies in getting rid of strong data distributional assumptions and in orientation towards the most critical data in risk management: those with asymmetries and heavy tails. At the same time, under typical assumptions, such as the ellipticity of the data probability distribution, the conformity with classical methods is shown. The constructed class of RM is a multivariate generalization of the coherent distortion RM, which possess valuable properties for a risk manager. The design of the framework is twofold. The first part contains new computational geometry methods for the high-dimensional data. The developed algorithms demonstrate computability of geometrical concepts used for constructing the RM. These concepts bring visuality and simplify interpretation of the RM. The second part develops models for applying the framework to actual problems. The spectrum of applications varies from robust portfolio selection up to broader spheres, such as stochastic conic optimization with risk constraints or supervised machine learning.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

An application of two techniques for the analysis of short, multivariate non-stationary time-series of Mauritanian trawl survey data

Min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) are complementary techniques for analysing short (> 15-25 y), non-stationary, multivariate data sets. We illustrate the two techniques using catch rate (cpue) time-series (1982-2001) for 17 species caught during trawl surveys off Mauritania, with the NAO index, an upwelling index, sea surface temperature, and an index of fishing effort as explanatory variables. Both techniques gave coherent results, the most important common trend being a decrease in cpue during the latter half of the time-series, and the next important being an increase during the first half. A DFA model with SST and UPW as explanatory variables and two common trends gave good fits to most of the cpue time-series. (c) 2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.

Asociación entre factores socioeconómicos y hábitos de limpieza en gestantes de Usaquén y Kennedy de la ciudad de Bogotá

Introducción: Actualmente existe un reconocimiento creciente de que el hogar desempeña un papel importante en varias cuestiones de higiene y salud pública. El ambiente del hogar ha sido implicado como una fuente importante de propagación de enfermedades infecciosas, y la intervención de las medidas de higiene, implican una reducción de la incidencia, especialmente en los países menos desarrollados y en poblaciones vulnerables como las gestantes. Objetivo: Evaluar la asociación entre la práctica de hábitos higiénicos de las gestantes estrato 1 y 2 de las localidades de Usaquén y Kennedy en relación a sus factores socioeconómicos. Métodos: Estudio Analítico de Corte transversal. Se realizó en las gestantes de los estratos 1 y 2 de las localidades de Usaquén y Kennedy en la ciudad de Bogotá. Se recolectaron datos referentes a factores socioeconómicos y hábitos de higiene de 141 gestantes a través de la aplicación de una encuesta. Los datos obtenidos de las variables de interés fueron procesados a través de análisis multivariado y regresión logística paramétrica y no paramétrica, con el fin de establecer si existía asociación o no entre las mismas. Resultados: Existe asociación entre el número de Nacidos vivos y la presencia de plagas (p=0.034 y Coeficiente de correlación: -1.253). Así mismo se encontró asociación habitar en cuartos rentados y la limpieza de casa general (p=0.008 y Coeficiente de correlación: 0.480). Existe una asociación entre la variable edad y el lavado de frutas (p=0.041 y Coeficiente de correlación: 0.384). Conclusiones: Existe relación entre los hábitos higiénicos y los factores socioeconómicos de las gestantes estudiadas. Existe un mayor hábito de lavado de frutas antes de ser consumidas en gestantes de mayor edad. Adicional a esto se evidencia a mayor número de hijos hay menor presencia de plagas en el hogar y mayor limpieza del hogar. Solo en un pequeño porcentaje de los hogares se evidencio una óptima limpieza, por lo cual se deben plantear más políticas para mejorar la higiene de los mismos ya que los datos reportados permanecen subóptimos en la población seleccionada.

Percepción de la seguridad y salud en el trabajo en una población perteneciente a 11 clínicas odontológicas particulares de Bogotá, D.C, 2016.

Objetivo: Establecer la relación entre la percepción de seguridad con los factores ocupacionales y socio demográficos de los trabajadores pertenecientes a 11 clínicas odontológicos particulares en Bogotá. Materiales y métodos: Estudio de corte transversal en población de trabajadores pertenecientes a clínicas odontológicas particulares de Bogotá (Colombia). La muestra incluyó 105 odontólogos y 107 auxiliares pertenecientes a 11 clínicas odontológicas privadas en Bogotá que cumplieron con los criterios de selección. Se incluyeron variables ocupacionales y socio demográficas y las relacionadas con el sistema de seguridad y salud en el trabajo. Se aplicó el “Cuestionario Nórdico Sobre Seguridad en el Trabajo. Para los factores socio-demográficos de la población estudiada, se utilizó estadística descriptiva, medidas de frecuencia absoluta y porcentual, las variables cuantitativas se describieron con medidas de tendencia central y de dispersión; para la asociación de las variables cualitativas con el tipo de percepción se usó la prueba Ji Cuadrado de Pearson o el test exacto de Fisher para valores esperados menores de 5. Resultados: Las dimensiones con mejor percepción fueron las relacionadas con la confianza en la eficacia de los sistemas de seguridad (D7 3.35±0.43) y la Administración de justicia de seguridad (D3 3.1±0.55). Las dimensiones que evalúan la Gestión de empoderamiento de seguridad (D2 2.74±0.99) y la prioridad de los trabajadores con la seguridad (D5 2.64±0.54) tuvieron la menor percepción. La percepción de seguridad fue buena en general teniendo en cuenta que el promedio de todas las dimensiones fue superior a 2.5; percepción mayor significativamente en los hombres (3.78±0.38), odontólogos (3.89±0.38) y personal que tienen o han tenido pareja (3.83±0.4). Las personas con más trabajos adicionales (4.07±0.17), mayor nivel educativo (3.89±0.31), mayor antigüedad laboral (3.92±0.51) y trabajadores mayores de 30 años (3.89±0.35), mostraron mejor percepción de seguridad. En el análisis multivariado los factores cargo y antigüedad laboral encontraron asociación en las dimensiones 2, 4 y 6 (p<0.001), mientras que para las variables numéricas, las dimensiones 1, 3, 4 y 6 tuvieron asociación significativa las variables género y posición (p<0.001). Conclusiones: La percepción de seguridad en las siete dimensiones se relacionó con el género, edad, estado civil, nivel educativo, cargo, posición y antigüedad laboral. No se encontraron diferencias significativas por turno de trabajo o clínica.

The Selection Process of Applications to the Portuguese Innovation Incentive System: Who Gets Financial Support?

Public policies to support entrepreneurship and innovation play a vital role when firms have difficulties in accessing external finance. However, some authors have found evidence of long-term inefficiency in subsidized firms (Bernini and Pelligrini, 2011; Cerqua and Pelligrini, 2014) and ineffectiveness of public funds (Jorge and Suárez, 2011). The aim of the paper is to assess the effectiveness in the selection process of applications to public financial support for stimulating innovation. Using a binary choice model, we investigate which factors influence the probability of obtaining public support for an innovative investment. The explanatory variables are connected to firm profile, the characteristics of the project and the macroeconomic environment. The analysis is based on the case study of the Portuguese Innovation.Incentive System (PIIS) and on the applications managed by the Alentejo Regional Operational Program in the period 2007 – 2013. The results show that the selection process is more focused on the expected impact of the project than on the firm’s past performance. Factors that influence the credit risk and the decision to grant a bank loan do not seem to influence the government evaluator regarding the funding of some projects. Past activities in R&D do not significantly affect the probability of having an application approved under the PIIS, whereas an increase in the number of patents and the number of skilled jobs are both relevant factors. Nevertheless, some evidence of firms’ short-term inefficiency was found, in that receiving public financial support is linked to a smaller increase in productivity compared to non-approved firm applications. At the macroeconomic level, periods with a higher cost of capital in financial markets are linked to a greater probability of getting an application for public support approved, which could be associated with the effectiveness of public support in correcting market failings.