DIP-STR: A new marker for resolving unbalanced DNA mixtures

The genetic characterization of unbalanced mixed stains remains an important area where improvementis imperative. Most cases of aggression, homicide and sexual assault produce biological traces withrelatively large amount of the victim's DNA and small amount of the aggressor's DNA. If this ratio issmaller than 1:10 it is currently not possible to obtain a conventional autosomal DNA profile of the minorcontributor, with potential loss of crucial DNA evidence. Y-STR analysis represents a solution for somecases but has several limitations. We propose here a method based on a new compound genetic markerformed by a Deletion/Insertion Polymorphism (DIP) linked to a Short Tandem Repeat polymorphism(STR), that we name DIP-STR. By means of allele-specific amplifications of DIP-STR haplotypes, we canproduce a high resolution autosomal DNA profile of a donor that contributes less than 0.1% to a DNAmixture. Based on these features DIP-STR markers may outperform conventional Y-STR markers inmixed stain analysis.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Fusões e aquisições da indústria de petróleo e gás natural na América do Sul: um estudo qualitativo de 5 eventos

Este trabalho tem como objetivo contextualizar as Fusões e Aquisições realizadas, na América do Sul, na indústria de petróleo, pela análise qualitativa de cinco eventos, utilizando a metodologia de estudo de eventos e a influência nas cotações das ações. A partir dessa análise, foi possível associar os períodos de aumento das atividades de F&A com a evolução da indústria mundial de petróleo, e mostrar que os processos de F&A possuem um caráter estratégico frente à economia mundial. Os resultados, todavia, apresentam indícios de não geração de valor. Dentre os cinco eventos, quatro apresentaram retornos anormais acumulados negativos. Um único evento obteve retorno positivo e foi somente para a empresa adquirida. Quanto ao método de integração, a amostra de cinco eventos apresentou evidências de destruição de valor para as firmas em todas as três formas estudadas. O retorno médio na janela pós-evento – 180 dias após o anúncio – divergiu do retorno anormal acumulado na janela de evento. As influências de um determinado evento podem ser negligenciadas devido a limitações, tanto operacionais quanto metodológicas.

Estudos preliminares com proteínas de Neurospora crassa identificadas em complexos DNA-proteína

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Desenvolvimento local e capital social: uma análise interdisciplinar do processo de indução do DLIS no Estado de Roraima

Uma nova tendência em termos de ações para o desenvolvimento vem se configurando. Trata-se de ações que levam em conta o território e seus atores. Nesse âmbito, insere-se a metodologia de indução do Desenvolvimento Local Integrado e Sustentável (DLIS). Esse Programa de política pública se constitui uma estratégia participativa de indução do desenvolvimento, pela qual se mobilizam recursos das comunidades, que em parceria com o Estado (em seus três níveis) e o mercado, realizam diagnósticos, identificam potencialidades e vocações, elaboram planos integrados de desenvolvimento, na perspectiva de envolvimento dos sujeitos como proponentes e protagonistas da ação social em seus territórios. Com este trabalho, buscou-se compreender a multidimensionalidade do processo de DLIS, no estado de Roraima, procurando, ao mesmo tempo, o estabelecimento de um nexo causal entre as trajetórias sócio-econômicas (resultados) do DLIS e o capital social. A pesquisa se enquadrou na modalidade qualitativa. Adotou-se como estratégia metodológica o estudo de caso, envolvendo os municípios de Rorainópolis, Uiramutã, Baliza e Pacaraima. Três dimensões analítico-contextuais foram adotadas, quais sejam: conceitual, de implementação e de impacto. Na avaliação do capital social, as variáveis participação, confiança, cooperação e redes foram consideradas. Na interpretação dos dados, as seguintes abordagens foram utilizadas: análise contextual, análise descritiva, análise das diferenças de proporções e análise de correspondência. Os resultados da pesquisa revelaram que alguns conceitos e categorias adotados pelo Programa, embora relevantes, apresentam alguma ordem de problema. Dois municípios, Rorainópolis e Uiramutã, foram considerados municípios de bons resultados. Isso porque atenderam a um conjunto de questões que expressavam condições desejáveis para que o desenvolvimento local integrado e sustentável, em suas múltiplas dimensões, se efetivasse. Por seu turno, os municípios de Baliza e Pacaraima foram classificados como municípios de maus resultados. Os resultados do DLIS em Rorainópolis e Uiramutã estão associados ao capital social (relação positiva). A despeito da extensa literatura que trata da relevância desse tema, não se têm, ainda, instrumentos satisfatórios para medir capital social. Nesse contexto, entendese que estes resultados representam, de fato, uma aproximação.

Pós-processamento de formas de ondas oriundas de descargas atmosféricas no Centro de Estudos de Descargas Atmosféricas e Alta Tensão (CENDAT) – IEE/USP

Atmospheric Discharges are responsible for several lost in the electrical system therefore it´s done studies to find ways to reduce the problem caused by discharges. This branch of engineering is necessary the gathering, stock and analysis of large quantity of data to validate or refuse the many studies produced about it The CENDAT proposed a project to collect data on induced voltages in distribution lines and current waveform of the lightning, but a difficulty that arose was the accumulation of data due to lack of manpower available to catalog all the data collected. Thinking in this difficulty, the engineer Acacio Silva Neto CENDAT´s researcher with trainees began to develop a program to solve this problem. This work keeps the development of this program in order to solve the problem of accumulation of data

Aportación de técnicas geoespaciales para la determinación de los niveles de referencia de la deforestación y la degradación de bosques en el ámbito de proyectos REDD. análisis de factores de causalidad subyacentes y directos

La pérdida de bosques en la Tierra, principalmente en ecosistemas amazónicos, es un factor clave en el proceso del cambio climático. Para revertir esta situación, los mecanismos REDD (Reducing Emission from Deforestation and forest Degradation) están permitiendo la implementación de actividades de protección del clima a través de la reducción de emisiones por deforestación evitada, según los esquemas previstos en el Protocolo de Kioto. El factor técnico más crítico en un proyecto REDD es la determinación de la línea de referencia de emisiones, que define la expectativa futura sobre las emisiones de CO2 de origen forestal en ausencia de esfuerzos adicionales obtenidos como consecuencia de la implementación del programa REDD para frenar este tipo de emisiones. La zona del estudio se ubica en la región de San Martín (Perú), provincia cubierta fundamentalmente por bosques tropicales cuyas tasas de deforestación son de las más altas de la cuenca amazónica. En las últimas décadas del siglo XX, la región empezó un acelerado proceso de deforestación consecuencia de la integración vial con el resto del país y la rápida inmigración desde zonas rurales en busca de nuevas tierras agrícolas. Desde el punto de vista de la investigación llevada a cabo en la tesis doctoral, se pueden destacar dos líneas: 1. El estudio multitemporal mediante imágenes de satélite Landsat 5/TM con el propósito de calcular las pérdidas de bosque entre períodos. El estudio multitemporal se llevó a cabo en el período 1998-2011 utilizando imágenes Landsat 5/TM, aplicando la metodología de Análisis de Mezclas Espectrales (Spectral Mixtures Analysis), que permite descomponer la reflectancia de cada píxel de la imagen en diferentes fracciones de mezcla espectral. En este proceso, las etapas más críticas son el establecimiento de los espectros puros o endemembers y la recopilación de librerías espectrales adecuadas, en este caso de bosques tropicales, que permitan reducir la incertidumbre de los procesos. Como resultado de la investigación se ha conseguido elaborar la línea de referencia de emisiones histórica, para el período de estudio, teniendo en cuenta tanto los procesos de deforestación como de degradación forestal. 2. Relacionar los resultados de pérdida de bosque con factores de causalidad directos e indirectos. La determinación de los procesos de cambio de cobertura forestal utilizando técnicas geoespaciales permite relacionar, de manera significativa, información de los indicadores causales de dichos procesos. De igual manera, se pueden estimar escenarios futuros de deforestación y degradación de acuerdo al análisis de la evolución de dichos vectores, teniendo en cuenta otros factores indirectos o subyacentes, como pueden ser los económicos, sociales, demográficos y medioambientales. La identificación de los agentes subyacentes o indirectos es una tarea más compleja que la de los factores endógenos o directos. Por un lado, las relaciones causa – efecto son mucho más difusas; y, por otro, los efectos pueden estar determinados por fenómenos más amplios, consecuencia de superposición o acumulación de diferentes causas. A partir de los resultados de pérdida de bosque obtenidos mediante la utilización de imágenes Landsat 5/TM, se investigaron los criterios de condicionamiento directos e indirectos que podrían haber influido en la deforestación y degradación forestal en ese período. Para ello, se estudiaron las series temporales, para las mismas fechas, de 9 factores directos (infraestructuras, hidrografía, temperatura, etc.) y 196 factores indirectos (económicos, sociales, demográficos y ambientales, etc.) con, en principio, un alto potencial de causalidad. Finalmente se ha analizado la predisposición de cada factor con la ocurrencia de deforestación y degradación forestal por correlación estadística de las series temporales obtenidas. ABSTRACT Forests loss on Earth, mainly in Amazonian ecosystems, is a key factor in the process of climate change. To reverse this situation, the REDD (Reducing Emission from Deforestation and forest Degradation) are allowing the implementation of climate protection activities through reducing emissions from avoided deforestation, according to the schemes under the Kyoto Protocol. Also, the baseline emissions in a REDD project defines a future expectation on CO2 emissions from deforestation and forest degradation in the absence of additional efforts as a result of REDD in order to stop these emissions. The study area is located in the region of San Martín (Peru), province mainly covered by tropical forests whose deforestation rates are the highest in the Amazon basin. In the last decades of the twentieth century, the region began an accelerated process of deforestation due to road integration with the rest of the country and the rapid migration from rural areas for searching of new farmland. From the point of view of research in the thesis, we can highlight two lines: 1. The multitemporal study using Landsat 5/TM satellite images in order to calculate the forest loss between periods. The multitemporal study was developed in the period 1998-2011 using Landsat 5/TM, applying the methodology of Spectral Mixture Analysis, which allows decomposing the reflectance of each pixel of the image in different fractions of mixture spectral. In this process, the most critical step is the establishment of pure spectra or endemembers spectra, and the collecting of appropriate spectral libraries, in this case of tropical forests, to reduce the uncertainty of the process. As a result of research has succeeded in developing the baseline emissions for the period of study, taking into account both deforestation and forest degradation. 2. Relate the results of forest loss with direct and indirect causation factors. Determining the processes of change in forest cover using geospatial technologies allows relating, significantly, information of the causal indicators in these processes. Similarly, future deforestation and forest degradation scenarios can be estimated according to the analysis of the evolution of these drivers, taking into account other indirect or underlying factors, such as economic, social, demographic and environmental. Identifying the underlying or indirect agents is more complex than endogenous or direct factors. On the one hand, cause - effect relationships are much more diffuse; and, second, the effects may be determined by broader phenomena, due to superposition or accumulation of different causes. From the results of forest loss obtained using Landsat 5/TM, the criteria of direct and indirect conditioning that might have contributed to deforestation and forest degradation in that period were investigated. For this purpose, temporal series, for the same dates, 9 direct factors (infrastructure, hydrography, temperature, etc.) and 196 underlying factors (economic, social, demographic and environmental) with, in principle, a high potential of causality. Finally it was analyzed the predisposition of each factor to the occurrence of deforestation and forest degradation by statistical correlation of the obtained temporal series.

Análisis de Mezclas Espectrales para el estudio de la deforestación y establecimiento de la línea de referencia de emisiones en el marco del programa REDD. Aplicación a la región de San Martín, Perú.

La determinación de la línea histórica de deforestación como parte del establecimiento de la línea de referencia de emisiones, en el marco del programa REDD (Reducing Emissions from Deforestation and Forest Degradation), permite medir la evolución de la pérdida de bosque en un periodo definido de tiempo. El objetivo fue calcular la línea histórica de deforestación mediante estudio multitemporal para el periodo 1998-2011, en la región de San Martín (Perú), utilizando la metodología de Análisis de Mezclas Espectrales (Spectral Mixtures Analysis) con imágenes Landsat 5-TM. Palabras clave: teledetección, Landsat 5-TM, análisis de mezclas espectrales, REDD, Protocolo de Kioto, deforestación, Amazonía, SMA Spectral Mixture Analysis for the study of deforestation and establishing reference emissions level within the REDD Program framework. Application to the region of San Martin, Peru. Abstract: Determination of the historical baseline of deforestation as part of establishing the reference emissions level within the REDD (Reducing Emissions from Deforestation and Forest Degradation) Program framework allows for the measurement of the evolution of forest loss over a defined period time. The objective was to estimate the historical baseline of deforestation through a multi-temporal study for the period 1998-2011, in the region of San Martin (Peru), using the methodology of Spectral Mixture Analysis (Mixtures Spectral Analysis) from Landsat 5-TM imagery. Keywords: remote sensing, Landsat 5-TM, spectral mixtures analysis, REDD, Kyoto Protocol, deforestation, Amazon, SMA

On the dimensionality of the Attachment Style Questionnaire in Italian clinical and nonclinical participants

This study examined the utility of the Attachment Style Questionnaire (ASQ) in an Italian sample of 487 consecutively admitted psychiatric participants and an independent sample of 605 nonclinical participants. Minimum average partial analysis of data from the psychiatric sample supported the hypothesized five-factor structure of the items; furthermore, multiple-group component analysis showed that this five-factor structure was not an artifact of differences in item distributions. The five-factor structure of the ASQ was largely replicated in the nonclinical sample. Furthermore, in both psychiatric and nonclinical samples, a two-factor higher order structure of the ASQ scales was observed. The higher order factors of Avoidance and Anxious Attachment showed meaningful relations with scales assessing parental bonding, but were not redundant with these scales. Multivariate normal mixture analysis supported the hypothesis that adult attachment patterns, as measured by the ASQ, are best considered as dimensional constructs.

Long-Term Reward Patterns Contribute to Personal Goals at Work Among Finnish Managers

The research addresses the impact of long-term reward patterns on contents of personal work goals among young Finnish managers (N = 747). Reward patterns were formed on the basis of perceived and objective career rewards (i.e., career stability and promotions) across four measurements (years 2006 –2012). Goals were measured in 2012 and classified into categories of competence, progression, well-being, job change, job security, organization, and financial goals. The factor mixture analysis identified a three-class solution as the best model of reward patterns: High rewards (77%); Increasing rewards (17%); and Reducing rewards (7%). Participants with Reducing rewards reported more progression, well-being, job change and financial goals than participants with High rewards as well as fewer competence and organizational goals than participants with Increasing rewards. Workplace resources can be in a key role in facilitating goals towards building competence and organizational performance.

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

Potential errors in the application of mixture theory to multifrequency bioelectrical impedance analysis

Potential errors in the application of mixture theory to the analysis of multiple-frequency bioelectrical impedance data for the determination of body fluid volumes are assessed. Potential sources of error include: conductive length; tissue fluid resistivity; body density; weight and technical errors of measurement. Inclusion of inaccurate estimates of body density and weight introduce errors of typically < +/-3% but incorrect assumptions regarding conductive length or fluid resistivities may each incur errors of up to 20%.

Qualitative assessment of stock prices listed on the Sao Paulo stock exchange: An approach from the perspective of homogeneity analysis

The stock market suffers uncertain relations throughout the entire negotiation process, with different variables exerting direct and indirect influence on stock prices. This study focuses on the analysis of certain aspects that may influence these values offered by the capital market, based on the Brazil Index of the Sao Paulo Stock Exchange (Bovespa), which selects 100 stocks among the most traded on Bovespa in terms of number of trades and financial volume. The selected variables are characterized by the companies` activity area and the business volume in the month of data collection, i.e. April/2007. This article proposes an analysis that joins the accounting view of the stock price variables that can be influenced with the use of multivariate qualitative data analysis. Data were explored through Correspondence Analysis (Anacor) and Homogeneity Analysis (Homals). According to the research, the selected variables are associated with the values presented by the stocks, which become an internal control instrument and a decision-making tool when it comes to choosing investments.

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright (C) 2003 John Wiley Sons, Ltd.