Multidimensional cluster stability analysis from a Brazilian Bradyrhizobium sp RFLP/PCR data set

The taxonomy of the N(2)-fixing bacteria belonging to the genus Bradyrhizobium is still poorly refined, mainly due to conflicting results obtained by the analysis of the phenotypic and genotypic properties. This paper presents an application of a method aiming at the identification of possible new clusters within a Brazilian collection of 119 Bradryrhizobium strains showing phenotypic characteristics of B. japonicum and B. elkanii. The stability was studied as a function of the number of restriction enzymes used in the RFLP-PCR analysis of three ribosomal regions with three restriction enzymes per region. The method proposed here uses Clustering algorithms with distances calculated by average-linkage clustering. Introducing perturbations using sub-sampling techniques makes the stability analysis. The method showed efficacy in the grouping of the species B. japonicum and B. elkanii. Furthermore, two new clusters were clearly defined, indicating possible new species, and sub-clusters within each detected cluster. (C) 2008 Elsevier B.V. All rights reserved.

Assessing poverty with survey data. Uni-dimensional, multidimensional and resilience poverty analysis in Kenya

Traditionally Poverty has been measured by a unique indicator, income, assuming this was the most relevant dimension of poverty. Sen’s approach has dramatically changed this idea shedding light over the existence of many more dimensions and over the multifaceted nature of poverty; poverty cannot be represented by a unique indicator that only can evaluate a specific aspect of poverty. This thesis tracks an ideal path along with the evolution of the poverty analysis. Starting from the unidimensional analysis based on income and consumptions, this research enter the world of multidimensional analysis. After reviewing the principal approaches, the Foster and Alkire method is critically analyzed and implemented over data from Kenya. A step further is moved in the third part of the thesis, introducing a new approach to multidimensional poverty assessment: the resilience analysis.

Multidimensional item response theory models with general and specific latent traits for ordinal data

The aim of the thesis is to propose a Bayesian estimation through Markov chain Monte Carlo of multidimensional item response theory models for graded responses with complex structures and correlated traits. In particular, this work focuses on the multiunidimensional and the additive underlying latent structures, considering that the first one is widely used and represents a classical approach in multidimensional item response analysis, while the second one is able to reflect the complexity of real interactions between items and respondents. A simulation study is conducted to evaluate the parameter recovery for the proposed models under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameter recovery is particularly sensitive to the sample size, due to the model complexity and the high number of parameters to be estimated. For a sufficiently large sample size the parameters of the multiunidimensional and additive graded response models are well reproduced. The results are also affected by the trade-off between the number of items constituting the test and the number of item categories. An application of the proposed models on response data collected to investigate Romagna and San Marino residents' perceptions and attitudes towards the tourism industry is also presented.

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

MILVA:An interactive tool for the exploration of multidimensional microarray data

Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.

A generalized multidimensional index structure for multimedia data to support content-based similarity searches in a collaborative search environment

Since multimedia data, such as images and videos, are way more expressive and informative than ordinary text-based data, people find it more attractive to communicate and express with them. Additionally, with the rising popularity of social networking tools such as Facebook and Twitter, multimedia information retrieval can no longer be considered a solitary task. Rather, people constantly collaborate with one another while searching and retrieving information. But the very cause of the popularity of multimedia data, the huge and different types of information a single data object can carry, makes their management a challenging task. Multimedia data is commonly represented as multidimensional feature vectors and carry high-level semantic information. These two characteristics make them very different from traditional alpha-numeric data. Thus, to try to manage them with frameworks and rationales designed for primitive alpha-numeric data, will be inefficient. An index structure is the backbone of any database management system. It has been seen that index structures present in existing relational database management frameworks cannot handle multimedia data effectively. Thus, in this dissertation, a generalized multidimensional index structure is proposed which accommodates the atypical multidimensional representation and the semantic information carried by different multimedia data seamlessly from within one single framework. Additionally, the dissertation investigates the evolving relationships among multimedia data in a collaborative environment and how such information can help to customize the design of the proposed index structure, when it is used to manage multimedia data in a shared environment. Extensive experiments were conducted to present the usability and better performance of the proposed framework over current state-of-art approaches.

Using multidimensional projection techniques for reaching a high distinguishing ability in biosensing

Recent advances in the control of molecular engineering architectures have allowed unprecedented ability of molecular recognition in biosensing, with a promising impact for clinical diagnosis and environment control. The availability of large amounts of data from electrical, optical, or electrochemical measurements requires, however, sophisticated data treatment in order to optimize sensing performance. In this study, we show how an information visualization system based on projections, referred to as Projection Explorer (PEx), can be used to achieve high performance for biosensors made with nanostructured films containing immobilized antigens. As a proof of concept, various visualizations were obtained with impedance spectroscopy data from an array of sensors whose electrical response could be specific toward a given antibody (analyte) owing to molecular recognition processes. In addition to discussing the distinct methods for projection and normalization of the data, we demonstrate that an excellent distinction can be made between real samples tested positive for Chagas disease and Leishmaniasis, which could not be achieved with conventional statistical methods. Such high performance probably arose from the possibility of treating the data in the whole frequency range. Through a systematic analysis, it was inferred that Sammon`s mapping with standardization to normalize the data gives the best results, where distinction could be made of blood serum samples containing 10(-7) mg/mL of the antibody. The method inherent in PEx and the procedures for analyzing the impedance data are entirely generic and can be extended to optimize any type of sensor or biosensor.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.

Multidimensional scaling for orthodontic root resorption

The paper investigates the risk factors for the severity of orthodontic root resorption. The multidimensional scaling (MDS) visualization method is used to investigate the experimental data from patients who received orthodontic treatment at the Department of Orthodontics and Dentofacial Orthopedics, Faculty of Dentistry, “Carol Davila” University of Medicine and Pharmacy, during a period of 4 years. The clusters emerging in the MDS plots reveal features and properties not easily captured by classical statistical tools. The results support the adoption of MDS for tackling the dentistry information and overcoming noise embedded into the data. The method introduced in this paper is rapid, efficient, and very useful for treating the risk factors for the severity of orthodontic root resorption.

Is multidimensional scaling suitable for mapping the input respiratory impedance in subjects and patients?

This paper presents the application of multidimensional scaling (MDS) analysis to data emerging from noninvasive lung function tests, namely the input respiratory impedance. The aim is to obtain a geometrical mapping of the diseases in a 3D space representation, allowing analysis of (dis)similarities between subjects within the same pathology groups, as well as between the various groups. The adult patient groups investigated were healthy, diagnosed chronic obstructive pulmonary disease (COPD) and diagnosed kyphoscoliosis, respectively. The children patient groups were healthy, asthma and cystic fibrosis. The results suggest that MDS can be successfully employed for mapping purposes of restrictive (kyphoscoliosis) and obstructive (COPD) pathologies. Hence, MDS tools can be further examined to define clear limits between pools of patients for clinical classification, and used as a training aid for medical traineeship.

Characterization approach to modified glassy carbon electrode-nanofilm system within multidimensional scaling

The aim of this work is to characterize the nanofilm consisting of the benzoic acid-modified glassy carbon (GC) electrode system through multidimensional scaling space analysis. The surface modification is based on the electrochemical reaction between the GC electrode and benzoic acid-diazonium salt (BA-DAS). As a result, the nonofilms regarding the benzoic acid-glassy carbon (BA-GC) electrode surface was obtained. For the analysis of the naonfilm of BC-GC electrode system, the IR spectra of the modified BA-GC electrode surface, GC surface and BA-DAS were recorded in the spectral range of 599.84 – 3996.34 [cm–1]. The IR data vectors of the above three forms were processed by the using the multidimensional scaling space approach to demonstrate the existence of a nanofilm on the modified BA-GC electrode system. Two- and three-dimensional MDS profiles obtained by application of multidimensional scaling approach to the data sets {CG1,...,CG10}, {BA-GC1,...,BA-GC10} and {FILM1,...,FILM10} allow a good recognition of the nanofilm on the modified glassy carbon (GC) electrode system.

Multidimensional scaling analysis of electricity market prices

This paper studies the impact of energy and stock markets upon electricity markets using Multidimensional Scaling (MDS). Historical values from major energy, stock and electricity markets are adopted. To analyze the data several graphs produced by MDS are presented and discussed. This method is useful to have a deeper insight into the behavior and the correlation of the markets. The results may also guide the construction models, helping electricity markets agents hedging against Market Clearing Price (MCP) volatility and, simultaneously, to achieve better financial results.

Multidimensional scaling visualization of earthquake phenomena

Earthquakes are associated with negative events, such as large number of casualties, destruction of buildings and infrastructures, or emergence of tsunamis. In this paper, we apply the Multidimensional Scaling (MDS) analysis to earthquake data. MDS is a set of techniques that produce spatial or geometric representations of complex objects, such that, objects perceived to be similar/distinct in some sense are placed nearby/distant on the MDS maps. The interpretation of the charts is based on the resulting clusters since MDS produces a different locus for each similarity measure. In this study, over three million seismic occurrences, covering the period from January 1, 1904 up to March 14, 2012 are analyzed. The events, characterized by their magnitude and spatiotemporal distributions, are divided into groups, either according to the Flinn–Engdahl seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Space-time and Space-frequency correlation indices are proposed to quantify the similarities among events. MDS has the advantage of avoiding sensitivity to the non-uniform spatial distribution of seismic data, resulting from poorly instrumented areas, and is well suited for accessing dynamics of complex systems. MDS maps are proven as an intuitive and useful visual representation of the complex relationships that are present among seismic events, which may not be perceived on traditional geographic maps. Therefore, MDS constitutes a valid alternative to classic visualization tools, for understanding the global behavior of earthquakes.

Analysis of Forest Fires by means of Pseudo Phase Plane and Multidimensional Scaling Methods

Forest fires dynamics is often characterized by the absence of a characteristic length-scale, long range correlations in space and time, and long memory, which are features also associated with fractional order systems. In this paper a public domain forest fires catalogue, containing information of events for Portugal, covering the period from 1980 up to 2012, is tackled. The events are modelled as time series of Dirac impulses with amplitude proportional to the burnt area. The time series are viewed as the system output and are interpreted as a manifestation of the system dynamics. In the first phase we use the pseudo phase plane (PPP) technique to describe forest fires dynamics. In the second phase we use multidimensional scaling (MDS) visualization tools. The PPP allows the representation of forest fires dynamics in two-dimensional space, by taking time series representative of the phenomena. The MDS approach generates maps where objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to better understand forest fires behaviour.

Paralisia cerebral na Região de Lisboa e Vale do Tejo : factores de risco : caracterização multidimensional : inclusão escolar

RESUMO - A Paralisia Cerebral (PC) deve ser olhada como uma patologia do neurodesenvolvimento: a infância é um período de actividade exploratória por essência, a restrição motora condiciona as várias áreas do desenvolvimento. Contextos, apoios, oportunidades e experiências de vida serão determinantes no desenvolvimento de todo o seu potencial. Objectivos/finalidade: Identificar, descrever, comparar e analisar factores de risco associados à PC, sua caracterização multidimensional e integração escolar aos 5 e 10 anos. Procurou‐se contribuir para a sua prevenção primária e secundária, e obter dados para planeamento e implementação dos programas de apoio. Métodos: Adoptou‐se a abordagem do Programa Nacional de Vigilância da Paralisia Cerebral (PNVPC) e da Surveillance of Cerebral Palsy in Europe (SCPE). Analisaram‐se factores de risco, competências funcionais, défices associados, severidade e integração escolar de duas populações de Lisboa e Vale do Tejo, (nascimento 1996/1997‐2001/2002 e prevalência aos 5 e 10 anos). Descreveram‐se os dados, efectuaram‐se correlações, aplicaram‐se testes de independência e compararam‐se com dados dos nadovivos, dados nacionais e europeus. Analisaram‐se os factores que influenciaram a integração escolar através de métodos de regressão logística. Resultados/Conclusões/Recomendações: 1,65‰ e 1,57‰ dos nadovivos desenvolveram PC; a prevalência aos 5 anos foi de 1,7‰ e de 1,48‰; 5,9% e 7,9% faleceram antes dos 5 anos. Em 2001/2002 verificou‐se aumento de: PC espástica bilateral‐2/3membros, prematuridade, causa pos‐neonatal, níveis funcionais ligeiros e graves; percentil estaturo‐ponderal <3 (5‐anos). Diminuição de: disquinésia, anóxia e alguns défices associados. Destacaram‐se as associação: prematuridade e PC espástica bilateral‐ 2/3membros; nascer de termo e anóxia, disquinésia, primíparas, défices associados e severidade; infecção pré‐natal e QI<50, epilepsia e severidade; causa pos‐neonatal e PC espástica bilateral‐4membros e múltiplos défices. Aos 5 anos, as variáveis explicativas para a não inclusão escolar foram: QI<50 e epilepsia; uma elevada percentagem de crianças com PC moderada/grave encontrava‐se integrada; 75% das que se encontravam desintegradas mantiveram‐se nesta situação aos 10. Nesta idade, as variáveis explicativas para a não inclusão escolar foram: QI<50 e motricidade fina; 35,1% encontrava‐se fora do ensino regular; 4,5%, embora em idade de escolaridade obrigatória, não frequentavam qualquer estabelecimento escolar. Informação sistematizada, abrangente, objectiva, simples e acessível, sobre novos casos de PC, factores de risco, prevalência em idades‐chave e caracterização multidimensional constitui uma ferramenta clínica e epidemiológica, que deve sustentar as políticas de saúde, educacionais e sociais, contribuindo para a permanência destas crianças no ensino regular, trazendo às crianças e famílias o suporte que as encorajem e sustentem nestes processos. ABSTRACT ------- Cerebral Palsy (CP) must be recognized as a neurodevelopmental disorder: childhood is, on its nature, a period for exploring the environment and therefore motor deficit interferes with all developmental areas. The context, support, opportunities and life experiences are determinants for the development of his full potential. Objective/Aim: To identify, describe, compare and analyze CP risk factors the multidimensional characterization and school integration levels at the age of 5 and 10 years. We aim to contribute to CP primary and secondary prevention and provide information for service planning and implementation of support programs. Methods: The approach of National Cerebral Palsy Surveillance Programme (NCPSP) and Surveillance of Cerebral Palsy in Europe (SCPE) were used. For two groups of children from Lisboa e Vale do Tejo region, birth data 1996/1997‐2001/2002 and prevalence at 5 and 10 years, were analyzed: CP risk factors, functional ability, associated impairments, severity and school integration settings. Data collected was described, analyzed using correlations, applied tests of independence and compared with new born data, national data and european data. To analyze the factors related to school inclusive settings, logistic regression was appealed. Results/Conclusions/Recommendations: 1,65% and 1,57‰ of the new‐born alive developed CP. The prevalence at 5 years was 1,7‰ and 1,48‰ 5,9% and 7,9% died before their 5th birthday. Bilateral spastic CP 2/3limb, preterm birth, cases of post‐neonatal origin, mild and sever functional impairment; weight and height percentile <3 at 5 years old Increased in 2001. Decreased dyskinetic CP, anoxia and some additional imparments. Were identified among other the association between prematurity and spastic bilateral CP‐2/3 members; born at term and anoxia, dyskinetic CP, first child, associated impairments and severity; prenatal infection and IQ<50, epilepsy and severity; post‐neonatal cause and spastic bilateral CP‐4 members and associated impairments. At 5‐years‐old the more explanatory variables for not be in a school inclusive settings were IQ<50 and epilepsy, a high percentage of children with moderate/severe CP was attending regular school, but most children who were out of inclusive settings at 5 years continue on this situations at 10‐years‐old. At this age the more explanatory variables for not be in a school inclusive settings were: IQ<50 and upper limb function; 35,1% were out of regular school; 4,5%, even in compulsory school age, are out of school. Standardized comprehensive, objective, simple and accessible information about CP new cases, risk factors, prevalence in the key‐age and children multidimensional characterization constitutes a clinical and epidemiological tool that should sustain health, educational and social policy. This would support the continuity of these children in regular schools, encouraging g them and their families in these processes.