Academic literature on the topic 'Partial Least Squares Discriminant Analysis (PLS-DA)'

A partial least-squares (PLS) discriminant analysis on the autofluorescence spectra of oral squamous cell carcinoma based on the cross-validation technique was conducted to discriminate among oral tissues at different cancer development stages. These tissues were obtained from hamsters of DMBA-induced buccal pouch carcinogenesis. The study on the fluorescence spectra of the cancer tissues revealed that 320 nm might be the optimal excitation wavelength, and it was selected for the discriminating analysis. The PLS discriminant plot based on cross-validation showed that tissues of oral carcinogenesis belonging to four clinically important cancer development stages—normal tissues, hyperplasia, dysplasia and early cancers, and frankly invasive cancers—could be classified by using the first two PLS factors that emerged from the fluorescence spectra at 320 nm excitation. The PLS factor loading plots of the first PLS factor of 320 and 360 nm excitations showed that the first PLS factor was correlated to the fluorescent structure changes. This study indicates that further development of the PLS discriminant analysis on the autofluorescence spectra may be useful for developing a simple and efficient discriminating algorithm for the identification of different stages of human oral carcinogenesis.

To explore novel methods for the analysis of metabolomics data, we compared the ability of Partial Least Squares Discriminant Analysis (PLS-DA) and Bayesian networks (BN) to build predictive plasma metabolite models of age three asthma status in 411 three year olds (n = 59 cases and 352 controls) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART) study. The standard PLS-DA approach had impressive accuracy for the prediction of age three asthma with an Area Under the Curve Convex Hull (AUCCH) of 81%. However, a permutation test indicated the possibility of overfitting. In contrast, a predictive Bayesian network including 42 metabolites had a significantly higher AUCCH of 92.1% (p for difference < 0.001), with no evidence that this accuracy was due to overfitting. Both models provided biologically informative insights into asthma; in particular, a role for dysregulated arginine metabolism and several exogenous metabolites that deserve further investigation as potential causative agents. As the BN model outperformed the PLS-DA model in both accuracy and decreased risk of overfitting, it may therefore represent a viable alternative to typical analytical approaches for the investigation of metabolomics data.

This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm-1 to 4000 cm-1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oil by implementing Partial Least Square Discriminant Analysis (PLS-DA), Leaming Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardised before developing the classification models. The classification model was validated by finding the value of percentage correctly classified using test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as /-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLS-DA classifier of the standardised data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.

This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm·1 to 4000 cm·1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oil by implementing Partial Least Square Discriminant Analysis (PLS-DA), Leaming Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardised before developing the classification models. The classification model was validated by finding the value of percentage correctly classified using test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as /-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLS-DA classifier of the standardised data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.

Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.

This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining it with logistic regression and linear discriminant analysis, to get a partial least squares generalized linear regression-logistic regression model (PLSGLR-log), and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative study of the obtained classifiers with the classical methodologies like the k-nearest neighbours (KNN), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM) is then carried out. Furthermore, a new methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classifiers. The KMA emerged as the best classifier based on the lowest classification error rates compared to the others when applied to the types of data are considered; the un- preprocessed and preprocessed.

Polycyclic aromatic compounds (PACs) are organic compounds that include several sub-groups of toxic, persistent and carcinogenic environmental pollutants consisting of two or more non-substituted or substituted aromatic rings. Due to the complexity of PAC-mixtures found in the environment it can be challenging and time-consuming to track the sources of contamination. In the present study, multivariate data analysis (MVDA) models, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to track sources of PACs at contaminated sites. Based on the chemical profile of 78 PACs obtained in GC-MS analysis of soils, 26 observations were classified according to their petrogenic, pyrogenic or urban background soil origin. Two soil samples of unknown origin collected at a contaminated site in Mjölby, Sweden, were successfully fitted to the validated PLS-DA model and their origins were determined as petrogenic. The study shows that validated PLS-DA models can be applied to predict the petrogenic, pyrogenic and urban background soil origins of samples collected at PAC contaminated sites, thus to track the sources of contamination. It is also concluded that 16 U.S. Environmental Protection Agency’s (EPA) priority polycyclic aromatic hydrocarbons (PAHs) are not sufficient to predict the origin of contamination with PCA or PLS-DA.

Trots att det inom forskningsvärlden propageras för formativ bedömning, kan man i dagsläget notera en mycket stor variation gällande införlivandet av, samt effekter av, formativ bedömning i skolor. Metoder för att kartlägga formativ bedömningspraxis fordras för att kunna särskilja på ”god” respektive ”mindre god” formativ bedömningspraxis. Syftet med föreliggande uppsats var att, med hjälp av en elevenkät och multivariata projektionsmetoder såsom PCA och PLS-DA, kartlägga, och särskilja, formativ bedömningspraxis hos sex olika gymnasieklasser som genomfört kursen kemi 2. Ett sekundärt syfte var även att, med samma verktyg, försöka karakterisera och särskilja frekvenser av olika genomförda undervisningsmoment inom samma kurs och klasser. Studien visade, på ett grafiskt och illustrativt sätt, en stor variation av upplevelser av formativ bedömning inom de tillfrågade klasserna. Vidare visade sig PCA vara ett utmärkt verktyg för att identifiera elevsvar som låg utanför den ”normala” variationen. Genom en PLS-DA-analys påvisades en skillnad i frekvenser av genomförda undervisningsmoment mellan två kommunala och en privat skola – även om dessa resultat bör tolkas med en viss försiktighet.

The present Ph.D. thesis, primarily conceived to support and reinforce the relation between academic and industrial worlds, was developed in collaboration with Shell Global Solutions (Amsterdam, The Netherlands) in the endeavour of applying and possibly extending well-established latent variable-based approaches (i.e. Principal Component Analysis - PCA - Partial Least Squares regression - PLS - or Partial Least Squares Discriminant Analysis - PLSDA) for complex problem solving not only in the fields of manufacturing troubleshooting and optimisation, but also in the wider environment of multivariate data analysis. To this end, novel efficient algorithmic solutions are proposed throughout all chapters to address very disparate tasks, from calibration transfer in spectroscopy to real-time modelling of streaming flows of data. The manuscript is divided into the following six parts, focused on various topics of interest: Part I - Preface, where an overview of this research work, its main aims and justification is given together with a brief introduction on PCA, PLS and PLSDA; Part II - On kernel-based extensions of PCA, PLS and PLSDA, where the potential of kernel techniques, possibly coupled to specific variants of the recently rediscovered pseudo-sample projection, formulated by the English statistician John C. Gower, is explored and their performance compared to that of more classical methodologies in four different applications scenarios: segmentation of Red-Green-Blue (RGB) images, discrimination of on-/off-specification batch runs, monitoring of batch processes and analysis of mixture designs of experiments; Part III - On the selection of the number of factors in PCA by permutation testing, where an extensive guideline on how to accomplish the selection of PCA components by permutation testing is provided through the comprehensive illustration of an original algorithmic procedure implemented for such a purpose; Part IV - On modelling common and distinctive sources of variability in multi-set data analysis, where several practical aspects of two-block common and distinctive component analysis (carried out by methods like Simultaneous Component Analysis - SCA - DIStinctive and COmmon Simultaneous Component Analysis - DISCO-SCA - Adapted Generalised Singular Value Decomposition - Adapted GSVD - ECO-POWER, Canonical Correlation Analysis - CCA - and 2-block Orthogonal Projections to Latent Structures - O2PLS) are discussed, a new computational strategy for determining the number of common factors underlying two data matrices sharing the same row- or column-dimension is described, and two innovative approaches for calibration transfer between near-infrared spectrometers are presented; Part V - On the on-the-fly processing and modelling of continuous high-dimensional data streams, where a novel software system for rational handling of multi-channel measurements recorded in real time, the On-The-Fly Processing (OTFP) tool, is designed; Part VI - Epilogue, where final conclusions are drawn, future perspectives are delineated, and annexes are included.
La presente tesis doctoral, concebida principalmente para apoyar y reforzar la relación entre la academia y la industria, se desarrolló en colaboración con Shell Global Solutions (Amsterdam, Países Bajos) en el esfuerzo de aplicar y posiblemente extender los enfoques ya consolidados basados en variables latentes (es decir, Análisis de Componentes Principales - PCA - Regresión en Mínimos Cuadrados Parciales - PLS - o PLS discriminante - PLSDA) para la resolución de problemas complejos no sólo en los campos de mejora y optimización de procesos, sino también en el entorno más amplio del análisis de datos multivariados. Con este fin, en todos los capítulos proponemos nuevas soluciones algorítmicas eficientes para abordar tareas dispares, desde la transferencia de calibración en espectroscopia hasta el modelado en tiempo real de flujos de datos. El manuscrito se divide en las seis partes siguientes, centradas en diversos temas de interés: Parte I - Prefacio, donde presentamos un resumen de este trabajo de investigación, damos sus principales objetivos y justificaciones junto con una breve introducción sobre PCA, PLS y PLSDA; Parte II - Sobre las extensiones basadas en kernels de PCA, PLS y PLSDA, donde presentamos el potencial de las técnicas de kernel, eventualmente acopladas a variantes específicas de la recién redescubierta proyección de pseudo-muestras, formulada por el estadista inglés John C. Gower, y comparamos su rendimiento respecto a metodologías más clásicas en cuatro aplicaciones a escenarios diferentes: segmentación de imágenes Rojo-Verde-Azul (RGB), discriminación y monitorización de procesos por lotes y análisis de diseños de experimentos de mezclas; Parte III - Sobre la selección del número de factores en el PCA por pruebas de permutación, donde aportamos una guía extensa sobre cómo conseguir la selección de componentes de PCA mediante pruebas de permutación y una ilustración completa de un procedimiento algorítmico original implementado para tal fin; Parte IV - Sobre la modelización de fuentes de variabilidad común y distintiva en el análisis de datos multi-conjunto, donde discutimos varios aspectos prácticos del análisis de componentes comunes y distintivos de dos bloques de datos (realizado por métodos como el Análisis Simultáneo de Componentes - SCA - Análisis Simultáneo de Componentes Distintivos y Comunes - DISCO-SCA - Descomposición Adaptada Generalizada de Valores Singulares - Adapted GSVD - ECO-POWER, Análisis de Correlaciones Canónicas - CCA - y Proyecciones Ortogonales de 2 conjuntos a Estructuras Latentes - O2PLS). Presentamos a su vez una nueva estrategia computacional para determinar el número de factores comunes subyacentes a dos matrices de datos que comparten la misma dimensión de fila o columna y dos planteamientos novedosos para la transferencia de calibración entre espectrómetros de infrarrojo cercano; Parte V - Sobre el procesamiento y la modelización en tiempo real de flujos de datos de alta dimensión, donde diseñamos la herramienta de Procesamiento en Tiempo Real (OTFP), un nuevo sistema de manejo racional de mediciones multi-canal registradas en tiempo real; Parte VI - Epílogo, donde presentamos las conclusiones finales, delimitamos las perspectivas futuras, e incluimos los anexos.
La present tesi doctoral, concebuda principalment per a recolzar i reforçar la relació entre l'acadèmia i la indústria, es va desenvolupar en col·laboració amb Shell Global Solutions (Amsterdam, Països Baixos) amb l'esforç d'aplicar i possiblement estendre els enfocaments ja consolidats basats en variables latents (és a dir, Anàlisi de Components Principals - PCA - Regressió en Mínims Quadrats Parcials - PLS - o PLS discriminant - PLSDA) per a la resolució de problemes complexos no solament en els camps de la millora i optimització de processos, sinó també en l'entorn més ampli de l'anàlisi de dades multivariades. A aquest efecte, en tots els capítols proposem noves solucions algorítmiques eficients per a abordar tasques dispars, des de la transferència de calibratge en espectroscopia fins al modelatge en temps real de fluxos de dades. El manuscrit es divideix en les sis parts següents, centrades en diversos temes d'interès: Part I - Prefaci, on presentem un resum d'aquest treball de recerca, es donen els seus principals objectius i justificacions juntament amb una breu introducció sobre PCA, PLS i PLSDA; Part II - Sobre les extensions basades en kernels de PCA, PLS i PLSDA, on presentem el potencial de les tècniques de kernel, eventualment acoblades a variants específiques de la recentment redescoberta projecció de pseudo-mostres, formulada per l'estadista anglés John C. Gower, i comparem el seu rendiment respecte a metodologies més clàssiques en quatre aplicacions a escenaris diferents: segmentació d'imatges Roig-Verd-Blau (RGB), discriminació i monitorització de processos per lots i anàlisi de dissenys d'experiments de mescles; Part III - Sobre la selecció del nombre de factors en el PCA per proves de permutació, on aportem una guia extensa sobre com aconseguir la selecció de components de PCA a través de proves de permutació i una il·lustració completa d'un procediment algorítmic original implementat per a la finalitat esmentada; Part IV - Sobre la modelització de fonts de variabilitat comuna i distintiva en l'anàlisi de dades multi-conjunt, on discutim diversos aspectes pràctics de l'anàlisis de components comuns i distintius de dos blocs de dades (realitzat per mètodes com l'Anàlisi Simultània de Components - SCA - Anàlisi Simultània de Components Distintius i Comuns - DISCO-SCA - Descomposició Adaptada Generalitzada en Valors Singulars - Adapted GSVD - ECO-POWER, Anàlisi de Correlacions Canòniques - CCA - i Projeccions Ortogonals de 2 blocs a Estructures Latents - O2PLS). Presentem al mateix temps una nova estratègia computacional per a determinar el nombre de factors comuns subjacents a dues matrius de dades que comparteixen la mateixa dimensió de fila o columna, i dos plantejaments nous per a la transferència de calibratge entre espectròmetres d'infraroig proper; Part V - Sobre el processament i la modelització en temps real de fluxos de dades d'alta dimensió, on dissenyem l'eina de Processament en Temps Real (OTFP), un nou sistema de tractament racional de mesures multi-canal registrades en temps real; Part VI - Epíleg, on presentem les conclusions finals, delimitem les perspectives futures, i incloem annexos.
Vitale, R. (2017). Novel chemometric proposals for advanced multivariate data analysis, processing and interpretation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90442
TESIS

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
In Analytical Chemistry it has been recurring in the literature the use of analytical signals recorded on multiple sensors combined with subsequent chemometric modeling for developing new analytical methodologies. For this purpose, it uses generally multivariate instrumental techniques as spectrometry ultraviolet-visible or near infrared, voltammetry, etc. In this scenario, the analyst is faced with the option of selecting individual variables or variable intervals so to avoid or reduce multicollinearity problems. A well-known strategy for selection of variable intervals is to divide the set of instrumental responses into equal width intervals and select the best interval based on the performance of the prediction of a unique range in the regression by Partial Least Squares (iPLS). On the other hand, the use of interval selection for classification purposes has received relatively little attention. A common practice is to use the iPLS regression method with the coded class indices as response variables to be predicted; that is the basic idea behind the release of the Discriminant Analysis by Partial Least Squares (PLS-DA) for classification. In other words, interval selection for classification purposes has no development of native functions (algorithms). Thus, in this work it is proposed two new strategies in classification problems using interval selection by the Successive Projections Algorithm. The first strategy is named Successive Projections Algorithm for selecting intervals in Discriminant Analysis Partial Least Squares (iSPA-PLS-DA), while the second strategy is called Successive Projections Algorithm for selecting intervals in Soft and Independent Modeling by Class Analogy (iSPA-SIMCA). The performance of the proposed algorithms was evaluated in three case studies: classification of vegetable oils according to the type of raw material and the expiration date using data obtained by square wave voltammetry; classification of unadulterated biodiesel/diesel blends (B5) and adulterated with soybean oil (OB5) using spectral data obtained in the ultraviolet-visible region; and classification of vegetable oils with respect to the expiration date using spectral data obtained in the near infrared region. The proposed iSPA-PLS-DA and iSPA-SIMCA algorithms provided good results in the three case studies, with correct classification rates always greater than or equal to those obtained by PLS-DA and SIMCA models using all variables, iPLS-DA and iSIMCA with a single selected interval, as well as SPA-LDA and GA-LDA with selection of individual variables. Therefore, the proposed iSPA-PLS-DA and iSPA-SIMCA algorithms can be considered as promising approaches for use in classification problems employing interval selection. In a more general point of view, the possibility of using interval selection without loss of the classification accuracy can be considered a very useful tool for the construction of dedicated instruments (e.g. LED-based photometers) for use in routine and in situ analysis.
Em Química Analítica tem sido recorrente na literatura o uso de sinais analíticos registrados em múltiplos sensores combinados com posterior modelagem quimiométrica para desenvolvimento de novas metodologias analíticas. Para esta finalidade, geralmente se faz uso de técnicas instrumentais multivariadas como a espectrometrias no ultravioleta-visível ou no infravermelho próximo, voltametria, etc. Neste cenário, o analista se depara com a opção de selecionar variáveis individuais ou intervalos de variáveis de modo de evitar ou diminuir problemas de multicolinearidade. Uma estratégia bem conhecida para seleção de intervalos de variáveis consiste em dividir o conjunto de respostas instrumentais em intervalos de igual largura e selecionar o melhor intervalo com base no critério de desempenho de predição de um único intervalo em regressão por Mínimos Quadrados Parciais (iPLS). Por outro lado, o uso da seleção de intervalo para fins de classificação tem recebido relativamente pouca atenção. Uma prática comum consiste em utilizar o método de regressão iPLS com os índices de classe codificados como variáveis de resposta a serem preditos, que é a idéia básica por trás da versão da Análise Discriminante por Mínimos Quadrados Parciais (PLS-DA) para a classificação. Em outras palavras, a seleção de intervalos para fins de classificação não possui o desenvolvimento de funções nativas (algoritmos). Assim, neste trabalho são propostas duas novas estratégias em problemas de classificação que usam seleção de intervalos de variáveis empregando o Algoritmo das Projeções Sucessivas. A primeira estratégia é denominada de Algoritmo das Projeções Sucessivas para seleção intervalos em Análise Discriminante por Mínimos Quadrados Parciais (iSPA-PLS-DA), enquanto a segunda estratégia é denominada de Algoritmo das Projeções Sucessivas para a seleção de intervalos em Modelagem Independente e Flexível por Analogia de Classe (iSPA-SIMCA). O desempenho dos algoritmos propostos foi avaliado em três estudos de casos: classificação de óleos vegetais com relação ao tipo de matéria-prima e ao prazo de validade utilizando dados obtidos por voltametria de onda quadrada; classificação de misturas biodiesel/diesel não adulteradas (B5) e adulteradas com óleo de soja (OB5) empregando dados espectrais obtidos na região do ultravioleta-visível; e classificação de óleos vegetais com relação ao prazo de validade usando dados espectrais obtidos na região do infravermelho próximo. Os algoritmos iSPA-PLS-DA e iSPA-SIMCA propostos forneceram bons resultados nos três estudos de caso, com taxas de classificação corretas sempre iguais ou superiores àquelas obtidas pelos modelos PLS-DA e SIMCA utilizando todas as variáveis, iPLS-DA e iSIMCA com um único intervalo selecionado, bem como SPA-LDA e GA-LDA com seleção de variáveis individuais. Portanto, os algoritmos iSPA-PLS-DA e iSPA-SIMCA propostos podem ser consideradas abordagens promissoras para uso em problemas de classificação empregando seleção de intervalos de variáveis. Num contexto mais geral, a possibilidade de utilização de seleção de intervalos de variáveis sem perda da precisão da classificação pode ser considerada uma ferramenta bastante útil para a construção de instrumentos dedicados (por exemplo, fotômetros a base de LED) para uso em análise de rotina e de campo.

Notre objectif est d’étudier les modalités d’accouchement au sein de la lignée humaine. Pour cela, nous utilisons deux approches complémentaires : la simulation numérique de l’accouchement et l’analyse discriminante des modalités d’accouchement au sein d’un échantillon obstétrical. Dans un premier temps, nous construisons des maillages de bassins et de crânes de foetus fossiles grâce à une méthode d’interpolation : le krigeage. Les groupes fossiles considérés sont les Australopithèques, les premiers représentants du genre Homo (PRGH) et les représentants du genre Homo au Pléistocène moyen et supérieur (RPMS). Les dimensions des crânes juvéniles sont utilisées pour estimer « à rebours » les dimensions néonatales à l’aide de courbes de croissance humaine et de chimpanzé. Nous réalisons une simulation numérique de l’accouchement à partir des maillages de ces dyades « virtuelles ». Puis nous réalisons des analyses discriminantes avec un jeu de données issu de mesures réalisées sur le pelviscanner de femmes et sur les mesures du crâne de leur nouveau-né afin de séparer les modalités d’accouchement grâce aux variables foeto-pelviennes. Ces mêmes variables foeto-pelviennes sont mesurées chez les dyades fossiles afin d’identifier, par les analyses discriminantes, leurs modalités d’accouchement les plus probables. Nos résultats suggèrent un accouchement eutocique sans rotation intra-pelvienne chez les Australopithèques, eutocique avec rotation intrapelvienne chez les PRGH, dystocique ou eutocique chez les RPMS, l’accouchement eutocique est caractérisé par une rotation et une incurvation de la trajectoire de descente
The purpose of this thesis is to estimate delivery outcomes for extinct hominids. We therefore use two complementary methods : numerical simulation of childbirth and discriminant analysis of delivery outcomes from an obstetrical sample. First, we use kriging to construct meshes of pelves and neonatal skulls. Fossil hominid specimens included in the study are Australopithecines, early Homo (EH) and middle to early Pleistocene Homo (MEPH). We estimate fetal cranial dimensions with chimpanzee or human cranial growth curve that we reversly use and apply on juveniles skull measurements. “Virtual” dyads are formed from pelves and neonatal skulls. Then, we simulate childbirth of these « virtual » dyads. Different levels of laxity of the sacro-iliac junction and different positions of the fetal head are considered. Finally, we use an obstetrical sample: delivery outcome is noted, CT-scans are used to obtain maternal pelvic measurements and diameters of the fetal head were also measured after delivery. A discriminant analysis is performed using this obstetrical sample to separate delivery outcomes thanks to fetal-pelvic measurements. Fossil dyads were subsequently added in the discriminant analysis to assess delivery outcomes to which they belong. Results suggest small fetal-pelvic constraint for Austalopithecines. This constraint is moderate for EH. Fetal-pelvic constraint is more important for MEPH. We suggest that rotational birth appears with EH. The curved trajectory of the fetal head appears with MEPH. Emergence of rotational birth and curved trajectory of fetal head are probably explained by two major increases in brain size during late and middle Pleistocene

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Fundo Mackenzie de Pesquisa
The allocation of resources and the constant search for competitive advantages differentiators to reach best results are always business challenges. In the contemporary context, in order to achieve superior performance, it reinforces the company's need to have, and make good use, of scarce, valuable, non-substitutable and inimitable resources. These resources include brands, customer base, knowledge, ability and competence of the work teams, corporate culture, partnerships and operational processes established, among other intangible assets, usually arising from a long and risky development process. Mergers and acquisitions (M & A) arise, then, as an important strategic action, being an alternative means to obtain and accelerate the accumulation of these resources within the companies. That is the subject of this work, which discusses the importance of existing and intangible assets disclosed, previous to the M & A transactions, their classification into various types, measurement, and impact on the resulting firm's financial performance in long term. The overall objective of this thesis was to analyze how this performance, after a minimum period of 36 months of the event, is related to the existence, level of disclosure and the nature of intangible assets in the organizations involved. One hundred-eighteen (118) companies were investigated in fifty-nine (59) cases of M & A occurred in France between 1997 and 2007; the study reflects a multi-method research, pluralistic, on qualitative and quantitative aspects. Intangible assets disclosure indicators were built by applying the content analysis technique to financial and accounting reports provided by the companies prior to the events, as well as financial indicators (proxies) for the existence of intangibles were calculated. These indicators were initially confronted with each other and later their explanatory power in relation to financial ratios of growth and profitability (for the corporation and its shareholders), which are the analyzed dimensions of financial performance. Many methods for statistical analysis were used in the multivariate data analysis (correlations and factor analysis, multiple regressions) and in the structural equation modeling (SEM), via Partial Least Squares (PLS). A total of twelve models, with statistics significance, were established to express the relationship among the constructs examined. Best results were achieved in the models developed with variables of semantic origin, in detriment of those with financial indicators only. The results obtained in this thesis leads to deduce that, in this study, there are positive relationships between the existence and the disclosure of intangible assets by firms involved in the operations of M & A and subsequent financial performance, measured by the corporate profitability and the growth of the resulting organization. This suggests that the strategic choice for business growth via M & A operations is favorable to the accumulation of intangible assets in the firms, in search for better results.
A alocação de recursos e a constante busca por diferenciais competitivos, visando melhores resultados, são grandes desafios empresariais. No contexto contemporâneo, para obter desempenho superior, reforça-se a necessidade de a empresa dispor, e fazer bom uso, de recursos raros, valiosos, não-substituíveis e de difícil imitação. Dentre estes recursos, destacam-se aspectos como as marcas, a base de clientes, o conhecimento, a capacidade e competência das equipes de trabalho, a cultura corporativa, as parcerias e os processos operacionais estabelecidos, dentre outros ativos intangíveis, geralmente decorrentes de longos e arriscados processos de desenvolvimento. As fusões e aquisições (F&A) surgem, então, como movimentos estratégicos importantes, sendo meio alternativo para obter e acelerar a acumulação destes recursos nas empresas. É essa a temática deste trabalho, que discorre sobre a importância dos ativos intangíveis existentes e divulgados previamente às operações de F&A de empresas, sobre a classificação dos seus diversos tipos, a sua mensuração e o seu impacto sobre o desempenho financeiro da firma resultante, no longo prazo. O objetivo geral desta tese foi analisar como este desempenho, após prazo mínimo de 36 meses do evento, está relacionado à existência, ao nível de divulgação e à natureza dos ativos intangíveis das organizações envolvidas. Foram investigadas 118 empresas, em 59 casos de F&A ocorridos na França entre 1997 e 2007, em uma pesquisa multi-métodos, pluralística, nas vertentes qualitativa e quantitativa. Foram construídos indicadores de divulgação (disclosure) de ativos intangíveis, mediante aplicação da técnica de análise de conteúdos aos relatórios contábil-financeiros disponibilizados pelas empresas antes do evento, e calculados indicadores financeiros (proxies) para a existência de intangíveis. Estes indicadores foram inicialmente confrontados entre si e posteriormente quanto ao seu poder explicativo em relação aos índices financeiros de crescimento e de lucratividade (empresarial e para os acionistas), que são as dimensões analisadas do desempenho financeiro. Utilizaram-se métodos de análise estatística de dados multivariados (análises de correlações, fatoriais, regressões múltiplas) e modelagem em equações estruturais, via Partial Least Squares (SEM- PLS). Foram estabelecidos, no total, doze modelos com significância estatística para expressar o relacionamento entre os construtos examinados. Alcançaram-se melhores resultados nos modelos desenvolvidos com variáveis de origem semântica, em detrimento daqueles que utilizaram indicadores exclusivamente financeiros. Os resultados obtidos nesta tese permitiram deduzir que há relações positivas entre a existência e a divulgação de ativos intangíveis pelas firmas envolvidas nas operações de F&A estudadas e o posterior desempenho financeiro, mensurado pela lucratividade empresarial e pelo crescimento, da organização resultante. Isto sugere que a opção estratégica por crescimento empresarial via operações de F&A é favorável ao acúmulo de recursos intangíveis nas firmas, na busca por melhores resultados.

Les tourbières ombrotrophes, ou bogs sont particulièrement vulnérables à l’augmentation de la déposition atmosphérique d’azote. Cet apport d’un nutriment normalement limitant altère la capacité des tourbières à accumuler le carbone (C), en plus de mener à des changements de leur composition végétale. L’imagerie spectrale est une approche prometteuse puisqu’elle rend possible la détection des espèces végétales et de certaines caractéristiques chimiques des plantes, à distance. Toutefois, l’ampleur des différences spectrales intra- et interespèces n’est pas encore connue. Nous avons évalué la façon dont la chimie, la structure et la signature spectrale des feuilles changent chez Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum et Eriophorum vaginatum, dans trois tourbières du sud du Québec et de l’Ontario, incluant une tourbière où se déroule une expérience de fertilisation à long terme. Nous avons mesuré des changements dans les traits fonctionnels dus aux différences dans la quantité d’azote disponible dans les sites. Toutefois, la déposition atmosphérique d’azote a eu relativement peu d’effet sur les spectres foliaires ; les variations spectrales les plus importantes étaient entre les espèces. En fait, nous avons trouvé que les quatre espèces ont un spectre caractéristique, une signature spectrale permettant leur identification au moyen d’analyses discriminantes des moindres carrés partiels (PLSDA). De plus, nous avons réussi à prédire plusieurs traits fonctionnels (l’azote, le carbone ; et la proportion d’eau et de matière sèche) avec moins de 10 % d’erreur grâce à des régressions des moindres carrés partiels (PLSR) des données spectrales. Notre étude fournit de nouvelles preuves que les variations intraspécifiques, causées en partie par des variations environnementales considérables, sont perceptibles dans les spectres foliaires. Toutefois, les variations intraspécifiques n’affectent pas l’identification des espèces ou la prédiction des traits. Nous démontrons que les spectres foliaires comprennent des informations sur les espèces et leurs traits fonctionnels, confirmant le potentiel de la spectroscopie pour le suivi des tourbières.
Abstract Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18 years N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.

Notre objectif est d’étudier les modalités d’accouchement au sein de la lignée humaine. Pour cela, nous utilisons deux approches complémentaires : la simulation numérique de l’accouchement et l’analyse discriminante des modalités d’accouchement au sein d’un échantillon obstétrical. Dans un premier temps, nous construisons des maillages de bassins et de crânes de foetus fossiles grâce à une méthode d’interpolation : le krigeage. Les groupes fossiles considérés sont les Australopithèques, les premiers représentants du genre Homo (PRGH) et les représentants du genre Homo au Pléistocène moyen et supérieur (RPMS). Les dimensions des crânes juvéniles sont utilisées pour estimer « à rebours » les dimensions néonatales à l’aide de courbes de croissance humaine et de chimpanzé. Nous réalisons une simulation numérique de l’accouchement à partir des maillages de ces dyades « virtuelles ». Puis nous réalisons des analyses discriminantes avec un jeu de données issu de mesures réalisées sur le pelviscanner de femmes et sur les mesures du crâne de leur nouveau-né afin de séparer les modalités d’accouchement grâce aux variables foeto-pelviennes. Ces mêmes variables foeto-pelviennes sont mesurées chez les dyades fossiles afin d’identifier, par les analyses discriminantes, leurs modalités d’accouchement les plus probables. Nos résultats suggèrent un accouchement eutocique sans rotation intra-pelvienne chez les Australopithèques, eutocique avec rotation intrapelvienne chez les PRGH, dystocique ou eutocique chez les RPMS, l’accouchement eutocique est caractérisé par une rotation et une incurvation de la trajectoire de descente
The purpose of this thesis is to estimate delivery outcomes for extinct hominids. We therefore use two complementary methods : numerical simulation of childbirth and discriminant analysis of delivery outcomes from an obstetrical sample. First, we use kriging to construct meshes of pelves and neonatal skulls. Fossil hominid specimens included in the study are Australopithecines, early Homo (EH) and middle to early Pleistocene Homo (MEPH). We estimate fetal cranial dimensions with chimpanzee or human cranial growth curve that we reversly use and apply on juveniles skull measurements. “Virtual” dyads are formed from pelves and neonatal skulls. Then, we simulate childbirth of these « virtual » dyads. Different levels of laxity of the sacro-iliac junction and different positions of the fetal head are considered. Finally, we use an obstetrical sample: delivery outcome is noted, CT-scans are used to obtain maternal pelvic measurements and diameters of the fetal head were also measured after delivery. A discriminant analysis is performed using this obstetrical sample to separate delivery outcomes thanks to fetal-pelvic measurements. Fossil dyads were subsequently added in the discriminant analysis to assess delivery outcomes to which they belong. Results suggest small fetal-pelvic constraint for Austalopithecines. This constraint is moderate for EH. Fetal-pelvic constraint is more important for MEPH. We suggest that rotational birth appears with EH. The curved trajectory of the fetal head appears with MEPH. Emergence of rotational birth and curved trajectory of fetal head are probably explained by two major increases in brain size during late and middle Pleistocene