Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

The human melanocortin-1 receptor locus: analysis of transcription unit, locus polymorphism and haplotype evolution

The complete sequence of the MCIR locus has been assembled, the coding region of the gene is intronless and placed within a 12 kb region flanked by the NULP1 and TUBB4 genes. The immediate promoter region has an E-box site with homology to the M-box consensus known to bind the microphthalmia transcription factor (MITF), however, promoter deletion analysis and transactivation studies have failed to show activation through this element by MITF. Polymorphism within the coding region, immediate 5' promoter region and a variable number tandem repeat (VNTR) minisatellite within the locus have been examined in a collection of Caucasian families and African individuals. Haplotype analysis shows linkage disequilibrium between the VNTR and MCIR coding region red hair variant alleles which can be used to estimate the age of these missense changes. Assuming a mean VNTR mutation rate of 1% and a star phylogeny, we estimate the Arg151Cys variant arose 7500 years before the present day, suggesting these variants may have arisen in the Caucasian population more recently than previously thought. (C) 2001 Published by Elsevier Science B.V.

Diversity of Phytophthora clandestina isolated from subterranean clover in southern australia: Analysis of virulence and RAPD profiles

The virulence spectrum of 112 isolates of Phytophthora clandestina collected from 56 sites in four subterranean clover-growing states in southern Australia was determined using differential cultivars of subterranean clover. Five races were detected, with race 0 in all states except New South Wales, race 1 in all states, race 2 only in Victoria, race 3 only in New South Wales, and race 4 in Victoria and Western Australia. The level of genotypic diversity among the different P. clandestina populations was investigated using five RAPD primers. Among 30 bands amplified, only two were polymorphic. This enabled identification of four multilocus RAPD genotypes. Three of the four genotypes occurred in all four states. Races 2 and 3 occurred with RAPD genotypes 1 and 2 only whereas races 0 and 1 occurred in all four multilocus RAPD genotypes. These results indicate that the pathogenicity spectrum of P. clandestina can change rapidly.

Lucerne biology and genetic improvement - an analysis of past activities and future goals in Australia

Breeding methodologies for cultivated lucerne (Medicago sativa L.), an autotetraploid, have changed little over the last 50 years, with reliance on polycross methods and recurrent phenotypic selection. There has been, however, an increase in our understanding of lucerne biology, in particular the genetic relationships between members of the M. sativa complex, as deduced by DNA analysis. Also, the differences in breeding behaviour and vigour of diploids versus autotetraploids, and the underlying genetic causes, are discussed in relation to lucerne improvement. Medicago falcata, a member of the M. sativa complex, has contributed substantially to lucerne improvement in North America, and its diverse genetics would appear to have been under-utilised in Australian programs over the last two decades, despite the reduced need for tolerance to freezing injury in Australian environments. Breeding of lucerne in Australia only commenced on a large scale in 1977, driven by an urgent need to introgress aphid resistance into adapted backgrounds. The release in the early 1980s of lucernes with multiple pest and disease resistance (aphids, Phytophthora, Colletotrichum) had a significant effect on increasing lucerne productivity and persistence in eastern Australia, with yield increases under high disease pressure of up to 300% being recorded over the predominant Australian cultivar, up to 1977, Hunter River. Since that period, irrigated lucerne yields have plateaued, highlighting the need to identify breeding objectives, technologies, and the germplasm that will create new opportunities for increasing performance. This review discusses major goals for lucerne improvement programs in Australia, and provides indications of the germplasm sources and technologies that are likely to deliver the desired outcomes.

Identification of causal relationships among traits related to drought resistance in Stylosanthes scabra using QTL analysis

Previous studies have shown that a negative relationship exists between transpiration efficiency (TE) and carbon isotope discrimination (Delta) and between TE and specific leaf area (SLA) in Stylosanthes scabra, A glasshouse experiment was conducted to confirm these relationships in an F-2 population and to study the causal nature of these relationships through quantitative trait loci (QTL) analysis, One hundred and twenty F-2 genotypes from a cross between two genotypes within S. scabra were used. Three replications for each genotype were maintained through vegetative propagation, Water stress was imposed by maintaining plants at 40% of field capacity for about 45 d. To facilitate QTL analysis, a genetic linkage map consisting of 151 RAPD markers was developed, Results from this study show that Delta was significantly and negatively correlated with TE and biomass production. Similarly, SLA showed significant negative correlation with TE and biomass production, Most of the QTL for TE and Delta were present on linkage groups 5 and 11. Similarly, QTL for SLA, transpiration and biomass productivity traits were clustered on linkage groups 13 and 24, One unlinked marker was also associated with these traits, There were several markers coincident between different traits, At all the coincident QTL, the direction of QTL effects was consistent with phenotypic data, At the coincident markers between TE and Delta, high alleles of TE were associated with low alleles of Delta. Similarly, low alleles of SLA were associated with high alleles of biomass productivity traits and transpiration. At the coincident markers between trans-4-hydroxy-N-methyl proline (MHP) and relative water content (RWC), low alleles of MHP were associated with high alleles of RWC, This study suggests the causal nature of the relationship between TE and Delta. Phenotypic data and QTL, data show that SLA was more closely associated with biomass production than with TE, This study also shows that a cause-effect relationship may exist between SLA and biomass production.

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.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Occult Axillary Lymph Node Metastases in Breast Cancer Do Matter: Results of 10-Year Survival Analysis

Axillary lymph node status is one of the most powerful prognostic factors for patients with breast cancer and is often critical in stratifying patients into adjuvant treatment regimens. In 203 apparently node-negative cases of breast cancer, a combination of immunohistochemical staining and step-sectioning identified occult metastases in 25% of cases. Ten-year follow-up information is available for these patients. Histologic features of the primary tumor and immunohistochemical staining for estrogen receptor, progesterone receptor, Her-2, and p53 were also evaluated. With multivariate analysis, both occult metastases and higher histologic grade of the primary tumor were independent predictors of disease-free survival. Histologic grade was the only significant independent predictor of overall survival. Estrogen receptor, progesterone receptor, Her-2, and p53 status did not predict the presence of metastases or survival when all tumor types were considered together. Metastases >0.5 mm significantly predicted a poorer disease-free survival when invasive ductal carcinomas were considered alone. Histologic grade was significantly associated with disease-free survival in the premenopausal and perimenopausal patients but not in the postmenopausal patients. The presence of occult metastases approached significance for overall survival in the premenopausal and perimenopausal patients but not in the postmenopausal patients.

The risk-return trade-off in Europe: A temporal and cross-sectional analysis

This paper analyzes the risk-return trade-off in European equities considering both temporal and cross-sectional dimensions. In our analysis, we introduce not only the market portfolio but also 15 industry portfolios comprising the entire market. Several bivariate GARCH models are estimated to obtain the covariance matrix between excess market returns and the industrial portfolios and the existence of a risk-return trade-off is analyzed through a cross-sectional approach using the information in all portfolios. It is obtained evidence for a positive and significant risk-return trade-off in the European market. This conclusion is robust for different GARCH specifications and is even more evident after controlling for the main financial crisis during the sample period.

Cluster Analysis of Business Data

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Analysis of stock market indices through multidimensional scaling

We propose a graphical method to visualize possible time-varying correlations between fifteen stock market values. The method is useful for observing stable or emerging clusters of stock markets with similar behaviour. The graphs, originated from applying multidimensional scaling techniques (MDS), may also guide the construction of multivariate econometric models.

Some reflections on acquisition, processing and analysis of statistical data in forest soils

Beyond the classical statistical approaches (determination of basic statistics, regression analysis, ANOVA, etc.) a new set of applications of different statistical techniques has increasingly gained relevance in the analysis, processing and interpretation of data concerning the characteristics of forest soils. This is possible to be seen in some of the recent publications in the context of Multivariate Statistics. These new methods require additional care that is not always included or refered in some approaches. In the particular case of geostatistical data applications it is necessary, besides to geo-reference all the data acquisition, to collect the samples in regular grids and in sufficient quantity so that the variograms can reflect the spatial distribution of soil properties in a representative manner. In the case of the great majority of Multivariate Statistics techniques (Principal Component Analysis, Correspondence Analysis, Cluster Analysis, etc.) despite the fact they do not require in most cases the assumption of normal distribution, they however need a proper and rigorous strategy for its utilization. In this work, some reflections about these methodologies and, in particular, about the main constraints that often occur during the information collecting process and about the various linking possibilities of these different techniques will be presented. At the end, illustrations of some particular cases of the applications of these statistical methods will also be presented.

Clinical and epidemiological analysis of patients with HIV/AIDS admitted to a reference hospital in the northeast region of Brazil

The AIDS epidemic has become a worldwide phenomenon of enormous magnitude and extension, deeply transforming medical practices and public health initiatives. This retrospective survey aimed to analyze clinical and epidemiological characteristics of patients with HIV/AIDS admitted to the Institute of Tropical Diseases Natan Portella, Teresina, Piauí, Brazil, from January, 2001 through December, 2004. Of the 828 patients, 43% were from other states and 71.3% were men. Average patient age was 35.4 ± 11.5 years-old and 85.5% were illiterate or had primary education. The main form of exposure to HIV was heterosexual behavior (54.1%), while injectable drug use was confirmed by only 2.7% of registered cases. The most frequent infectious complications were candidiasis (42.4%) and pneumocystosis (22.2%). Sixty-eight cases (8.2%) of visceral leishmaniasis were registered. Using multivariate analysis, individuals aged over 40 years-old, patients with active tuberculosis, Pneumocystis carinii pneumonia and central nervous system cryptococcosis showed increased risk of death. In this study, young male adults with low educational levels predominated and the most frequent opportunistic infections were candidiasis and pneumocystosis.

Fractional State Space Analysis of Temperature Time Series

Atmospheric temperatures characterize Earth as a slow dynamics spatiotemporal system, revealing long-memory and complex behavior. Temperature time series of 54 worldwide geographic locations are considered as representative of the Earth weather dynamics. These data are then interpreted as the time evolution of a set of state space variables describing a complex system. The data are analyzed by means of multidimensional scaling (MDS), and the fractional state space portrait (fSSP). A centennial perspective covering the period from 1910 to 2012 allows MDS to identify similarities among different Earth’s locations. The multivariate mutual information is proposed to determine the “optimal” order of the time derivative for the fSSP representation. The fSSP emerges as a valuable alternative for visualizing system dynamics.

Exploraty Multivariate Statistical Methods Applied to Pharmaceutical Industry CRM Data

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação