Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

MULTIVARIATE CLASSIFICATION BASED ON METAL CONTENTS IN HUMAN SENILE CATARACT LENSES

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.

TEOR DE VITAMINA C, ATIVIDADE DE ASCORBATO OXIDASE E PERFIL SENSORIAL DE MANGA (Mangífera índica L.) VAR. HADEN, DURANTE O AMADURECIMENTO

The chemical and biochemical composition of mango, varies according to the cultivation conditions, variety and maturation state, generally containing a high level of ascorbic acid. In order to establish the correlation between the activity of the ascorbate oxidase [E.C.1.10.3.3], and ascorbic acid level in the ripening process of the Haden mango (Mangífera índica L.), sample of the fruits related to hard green stage (zero), 2, 4, 6, 8, 10, 12 and 14 days stored at 20 ± 2oC, were tested. The samples were obtained by cutting small cubes of 8 cm3 from pulps of 8 mangoes with texture without significant difference (p£0.05) at Magness-Taylor pressure tester scale. In each sample the activity of ascorbate oxidase was followed, in order to check its participation in possible substrate losses during the ripening fruits. The ascorbic acid level and sensory profile also was determined periodically during the ripening period. The enzymatic activity was spectrophotometrically determined at 245 nm and 30oC. The ascorbic acid was analyzed according modified AOAC methodology, and sensory analysis by descriptive quantitative analysis. Data were analyzed using correlation analysis, analysis of variance (ANOVA), Tukey's test, principal component analysis and stepwise discriminant analysis. During the ripening, the ascorbate oxidase activity increased (from 0 to 5.0 x 10-1 U/ml) and the ascorbic acid level decreased (from 209.3 mg to 110.0 mg per 100g of pulp), showing a significant (p£0.05) inverse linear correlation (r=-0.98). The descriptors terms for mangoes were: characteristic flavor, characteristic aroma, sourness, astringency, yellow coloration of pulp, sweetness and succulence. The sensory profile presented significant improvement during ripening. All sensory attributes increased significantly (p£0.05) except sourness and astringency, wich decreased during the ripening of mangoes.

A structure-activity relationship study of quinone compounds with trypanocidal activity

A set of 25 quinone compounds with anti-trypanocidal activity was studied by using the density functional theory (DFT) method in order to calculate atomic and molecular properties to be correlated with the biological activity. The chemometric methods principal component analysis (PCA), hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA), Kth nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA) were used to obtain possible relationships between the calculated descriptors and the biological activity studied and to predict the anti-trypanocidal activity of new quinone compounds from a prediction set. Four descriptors were responsible for the separation between the active and inactive compounds: T-5 (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors give information on the kind of interaction that occurs between the compounds and the biological receptor. The prediction study was done with a set of three new compounds by using the PCA, HCA, SDA, KNN and SIMCA methods and two of them were predicted as active against the Trypanosoma cruzi. (c) 2005 Elsevier SAS. All rights reserved.

Synthesis, X-ray Crystal Structure and Theoretical Calculations of Antileishmanial Neolignan Analogues

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Synthesis, X-ray crystal structure and theoretical calculations of antileishmanial neolignan analogues

A síntese e a estrutura cristalina por difração de raios-X de dois análogos de neolignanas, 2-(4-clorofenil)-1-feniletanona (20) e 2-[tio(4-clorofenil)]-1-(3,4-dimetoxifenil)propan-1-ona (12) são descritas. O composto 12 apresenta atividade intracelular contra Leishmania donovani e Leishmania amazonensis de amastigotas que causam a leishmaniose tegumentar e visceral. Além disso, a teoria do funcional de densidade (DFT) com o funcional híbrido B3LYP foi empregado para calcular um conjunto de descritores moleculares para dezenove análogos sintéticos de neolignanas com atividades antileishmaniose. Posteriormente, a análise discriminante stepwise foi realizada para investigar possíveis relações entre a estrutura molecular e atividades biológicas. Por meio dessa análise os compostos foram classificados em dois grupos ativos e inativos de acordo com seu grau de atividade biológica, e as propriedades mais importantes foram as cargas de alguns átomos, a afinidade eletrônica e o ClogP.

Fruit quality sensing:Post-harvest ripeness

Firmness sensing of selected varieties of apples, pears and avocado fruits has been developed using a nondestructive impact technique. In addition to firmness measurements, postharvest ripeness of apples and pears was monitored by spectrophotometric reflectance measurements, and that of avocadoes by Hunter colour measurements. The data obtained from firmness sensing were analyzed by three analytical procedures: principal component, correlation and regression, and stepwise discriminant analysis. A new software was developed to control the impact test, analyse the data, and sort the fruit into specified classes, based on the criteria obtained from a training procedure. Similar procedures were used to analyse the reflectance and colour data. Both sensing systems were able to classify fruits w i th good accuracy.

Cognitive dissonance and the effect of acceptance of methodology, beliefs about religion, religious orientation, and dogmatism on the success of university students in a religious studies course

The problem investigated was negative effects on the ability of a university student to successfully complete a course in religious studies resulting from conflict between the methodologies and objectives of religious studies and the student's system of beliefs. Using Festinger's theory of cognitive dissonance as a theoretical framework, it was hypothesized that completing a course with a high level of success would be negatively affected by (1) failure to accept the methodologies and objectives of religious studies (methodology), (2) holding beliefs about religion that had potential conflicts with the methodologies and objectives (beliefs), (3) extrinsic religiousness, and (4) dogmatism. The causal comparative method was used. The independent variables were measured with four scales employing Likert-type items. An 8-item scale to measure acceptance of the methodologies and objectives of religious studies and a 16-item scale to measure holding of beliefs about religion having potential conflict with the methodologies were developed for this study. These scales together with a 20-item form of Rokeach's Dogmatism Scale and Feagin's 12-item Religious Orientation Scale to measure extrinsic religiousness were administered to 144 undergraduate students enrolled in randomly selected religious studies courses at Florida International University. Level of success was determined by course grade with the 27% of students receiving the highest grades classified as highly successful and the 27% receiving the lowest grades classified as not highly successful. A stepwise discriminant analysis produced a single significant function with methodology and dogmatism as the discriminants. Methodology was the principal discriminating variable. Beliefs and extrinsic religiousness failed to discriminate significantly. It was concluded that failing to accept the methodologies and objectives of religious studies and being highly dogmatic have significant negative effects on a student's success in a religious studies course. Recommendations were made for teaching to diminish these negative effects.

Predictors of first semester attrition and their relation to retention of generic associate degree nursing students

In community college nursing programs the high rate of attrition was a major concern to faculty and administrators. Since first semester attrition could lead to permanent loss of students and low retention in nursing programs, it was important to identify at-risk students early and develop proactive approaches to assist them to be successful. The goal of nursing programs was to graduate students who were eligible to take the national council licensing examination (RN). This was especially important during a time of critical shortage in the nursing workforce. ^ This study took place at a large, multi-campus community college, and used Tinto's (1975) Student Integration Model of persistence as the framework. A correlational study was conducted to determine whether the independent variables, past academic achievement, English proficiency, achievement tendency, weekly hours of employment and financial resources, could discriminate between the two grade groups, pass and not pass. Establishing the relationship between the selected variables and successful course completion might be used to reduce attrition and improve retention. Three research instruments were used to collect data. A Demographic Information form developed by the researcher was used to obtain academic data, the research questionnaire Measure of Achieving Tendency measured achievement motivation, and the Test of Adult Basic Education (TABE), Form 8, Level A, Tests 1, 4, and 5 measured the level of English proficiency. The Department of Nursing academic policy, requiring a minimum course grade of “C” or better was used to determine the final course outcome. A stepwise discriminant analysis procedure indicated that college language level and pre-semester grade point average were significant predictors of final course outcome. ^ Based on the findings of the study recommendations focused on assessing students' English proficiency prior to admission into the nursing program, an intensive remediation plan in language comprehension for at-risk students, and the selection of alternate textbooks and readings that more closely matched the English proficiency level of the students. A pilot study should be conducted to investigate the benefit of raising the admission grade point average. ^

Funding special education provision by means of discriminant function analysis

This thesis is concerned with the development of a funding mechanism, the Student Resource Index, which has been designed to resolve a number of difficulties which emerged following the introduction of integration or inclusion as an alternative means of providing educational support to students with disabilities in the Australian State of Victoria. Prior to 1984, the year in which the major integration or inclusion initiatives were introduced, the great majority of students with disabilities were educated in segregated special schools, however, by 1992 the integration initiatives had been successful in including within regular classes approximately half of the students in receipt of additional educational assistance on the basis of disability. The success of the integration program brought with it a number of administrative and financial problems which were the subject of three government enquiries. Central to these difficulties was the development of a dual system of special education provision. On one hand, additional resources were provided for the students attending segregated special schools by means of weighted student ratios, with one teacher being provided for each six students attending a special school. On the other hand, the requirements of individual students integrated into regular schools were assessed by school-based committees on the basis of their perceived extra educational needs. The major criticism of this dual system of special education funding was that it created inequities in the distribution of resources both between the systems and also within the systems. For example, three students with equivalent needs, one of whom attended a special school and two of whom attended different regular schools could each be funded at substantially differing levels. The solution to these inequities of funding was seen to be in the development of a needs based funding device which encompassed all students in receipt of additional disability related educational support. The Student Resource Index developed in this thesis is a set of behavioural descriptors designed to assess degree of additional educational need across a number of disability domains. These domains include hearing, vision, communication, health, co-ordination (manual and mobility), intellectual capacity and behaviour. The completed Student Resource Index provides a profile of the students’ needs across all of these domains and as such addresses the multiple nature of many disabling conditions. The Student Resource Index was validated in terms of its capacity to predict the ‘known’ membership or the type of special school which some 1200 students in the sample currently attended. The decision to use the existing special school populations as the criterion against which the Student Resource Index was validated was based on the premise that the differing resource levels of these schools had been historically determined by expert opinion, industrial negotiation and reference to other special education systems as the most reliable estimate of the enrolled students’ needs. When discriminant function analysis was applied to some 178 students attending one school for students with mild intellectual disability and one facility for students with moderate to severe intellectual disability the Student Resource Index was successful in predicting the student's known school in 92 percent of cases. An analysis of those students (8 percent) which the Student Resource Index had failed to predict their known school enrolment revealed that 13 students had, for a variety of reasons, been inappropriately placed in these settings. When these students were removed from the sample the predictive accuracy of the Student Resource Index was raised to 96 percent of the sample. By comparison the domains of the Vineland Adaptive Behaviour Scale accurately predicted known enrolments of 76 percent of the sample. By way of replication discriminant function analysis was then applied to the Student Resource Index profiles of 518 students attending Day Special Schools (Mild Intellectual Disability) and 287 students attending Special Developmental Schools (Moderate to Severe Intellectual Disability). In this case, the Student Resource Index profiles were successful in predicting the known enrolments of 85 percent of students. When a third group was added, 147 students attending Day Special Schools for students with physical disabilities, the Student Resource Index predicted known enrolments in 80 percent of cases. The addition of a fourth group of 116 students attending Day Special Schools (Hearing Impaired) to the discriminant analysis led to a small reduction in predictive accuracy from 80 percent to 78 percent of the sample. A final analysis which included students attending a School for the Deaf-Blind, a Hospital School and a Social and Behavioural Unit was successful in predicting known enrolments in 71 percent of the 1114 students in the sample. For reasons which are expanded upon within the thesis it was concluded that the Student Resource Index when used in conjunction with discriminant function analysis was capable of isolating four distinct groups on the basis of their additional educational needs. If the historically determined and varied funding levels provided to these groups, inherent in the cash equivalent of the staffing ratios of Day Special Schools (Mild Intellectual Disability), Special Development Schools (Moderate to Severe Intellectual Disability), Day Special Schools (Physical Disability) and Day Special Schools (Hearing Impairment) are accepted as reasonable reflections of these students’ needs these funding levels can be translated into funding bands. These funding bands can then be applied to students in segregated or inclusive placements. The thesis demonstrates that a new applicant for funding can be introduced into the existing data base and by the use of discriminant function analysis be allocated to one of the four groups. The analysis is in effect saying that this new student’s profile of educational needs has more in common with Group A than with the members of Groups B, C, or D. The student would then be funded at Group A level. It is immaterial from a funding point of view whether the student decides to attend a segregated or inclusive setting. The thesis then examines the impact of the introduction of Student Resource Index based funding upon the current funding of the special schools in one of the major metropolitan regions. Overall, such an initiative would lead to a reduction of 1.54 percent of the total funding accruing to the region’s special schools.

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

In order to differentiate and characterize Madeira wines according to main grape varieties, the volatile composition (higher alcohols, fatty acids, ethyl esters and carbonyl compounds) was determined for 36 monovarietal Madeira wine samples elaborated from Boal, Malvazia, Sercial and Verdelho white grape varieties. The study was carried out by headspace solid-phase microextraction technique (HS-SPME), in dynamic mode, coupled with gas chromatography–mass spectrometry (GC–MS). Corrected peak area data for 42 analytes from the above mentioned chemical groups was used for statistical purposes. Principal component analysis (PCA) was applied in order to determine the main sources of variability present in the data sets and to establish the relation between samples (objects) and volatile compounds (variables). The data obtained by GC–MS shows that the most important contributions to the differentiation of Boal wines are benzyl alcohol and (E)-hex-3-en-1-ol. Ethyl octadecanoate, (Z)-hex-3-en-1-ol and benzoic acid are the major contributions in Malvazia wines and 2-methylpropan-1-ol is associated to Sercial wines. Verdelho wines are most correlated with 5-(ethoxymethyl)-furfural, nonanone and cis-9-ethyldecenoate. A 96.4% of prediction ability was obtained by the application of stepwise linear discriminant analysis (SLDA) using the 19 variables that maximise the variance of the initial data set.

Seed image analysis and taxonomy of Diplotaxis DC. (Brassicaceae, Brassiceae)

The genus Diplotaxis, comprising 32 or 34 species, plus several additional infraspecific taxa, displays a considerable degree of heterogeneity in the morphology, molecular markers, chromosome numbers and geographical amplitude of the species. The taxonomic relationships within the genus Diplotaxis were investigated by phenetic characterisation of germplasm belonging to 27 taxa of the genus, because there is an increasing interest in Diplotaxis, since some of its species (D. tenuifolia, D. muralis) are gathered or cultivated for human consumption, whereas others are frequent arable weeds (D. erucoides) in many European vineyards. Using a computer-aided vision system, 33 morpho-colorimetric features of seeds were electronically measured. The data were used to implement a statistical classifier, which is able to discriminate the taxa within the genus Diplotaxis, in order to compare the resulting species grouping with the current infrageneric systematics of this genus. Despite the high heterogeneity of the samples, due to the great intra-population variability, the stepwise Linear Discriminant Analysis method, applied to distinguish the groups, was able to reach over 80% correct identification. The results obtained allowed us to confirm the current taxonomic position of most taxa and suggested the taxonomic position of others for reconsideration.

Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.