Genetic diversity among alfalfa genotypes (Medicago sativa L.) of non-dormant cultivars using SSR markers and agronomic traits

The aim of this study was to assess genetic diversity among 40 alfalfa (Medicago sativa L.) genotypes of different non-dormant (FD=8) cultivars. Biomass yield, regrowth speed and reaction to spring black stem, lepto leaf spot, and rust were evaluated. Analyses of variances were performed using a mixed model to examine the agronomic variation among individuals. A principal component analysis on standardized agronomic data was performed. Agronomic data were also used to calculate Gower's distance and UPGMA algorithm. For the molecular analysis, six SSR markers were evaluated and 84 alleles were identified. The genetic distance was estimated using standard Nei's distance. Average standard genetic diversity was 0.843, indicating a high degree of variability among genotypes. Finally, a generalized procrustes analysis was performed to calculate the correlation between molecular and agronomic distance, indicating a 65.4% of consensus. This value is likely related to the low number of individuals included in the study, which might have underestimated the real phenotypic variability among genotypes. Despite the low number of individuals and SSR markers analyzed, this study provides a baseline for future diversity studies to identify genetically distant alfalfa individuals or cultivars.

Sediment physicochemical properties condition pollutant association : a multivariate study of polybrominated diphenyl ethers on the Mendoza River irrigation areas

This work is a multidisciplinary environmental study that provides new insights into the relationships between sediment-organic matter characteristics and polybrominated diphenyl ethers (PBDEs) concentration. The aim of the present multivariate study was to correlate factors influencing PBDEs accumulation in sediment by using principal component analysis (PCA). Organic matter studies by Fourier Transform-Infrared spectroscopy and physicochemical analyses (Total Organic Carbon, pH, electrical conductivity) of sediment samples were considered for PCA. Samples were collected from an artificial irrigation network on the Mendoza River irrigation areas. PCA provided a comprehensive analysis of the studied variables, identifying two components that explained 63% of the data variance. Those factors were mainly associated to organic matter degradation degree, which represent a new insight into the relationships between organic matter in sediments and PBDEs fate. In this sense it was possible to determine that not only the content but also the type of organic matter (chemical structure) could be relevant when evaluating PBDEs accumulation and transport in the environment. Typification of organic matter may be a useful tool to predict more feasible areas where PBDE, may accumulate, as well as sediment transportation capability.

A Late Pleistocene sea level stack

Late Pleistocene sea level has been reconstructed from ocean sediment core data using a wide variety of proxies and models. However, the accuracy of individual reconstructions is limited by measurement error, local variations in salinity and temperature, and assumptions particular to each technique. Here we present a sea level stack (average) which increases the signal-to-noise ratio of individual reconstructions. Specifically, we perform principal component analysis (PCA) on seven records from 0-430 ka and five records from 0-798 ka. The first principal component, which we use as the stack, describes ~80 % of the variance in the data and is similar using either five or seven records. After scaling the stack based on Holocene and Last Glacial Maximum (LGM) sea level estimates, the stack agrees to within 5 m with isostatically adjusted coral sea level estimates for Marine Isotope Stages 5e and 11 (125 and 400 ka, respectively). When we compare the sea level stack with the d18O of benthic foraminifera, we find that sea level change accounts for about ~40 % of the total orbital-band variance in benthic d18O, compared to a 65 % contribution during the LGM-to-Holocene transition. Additionally, the second and third principal components of our analyses reflect differences between proxy records associated with spatial variations in the d18O of seawater.

Two Novel Methods For The Determination Of The Number Of Components In Independent Components Analysis Models

Independent Components Analysis is a Blind Source Separation method that aims to find the pure source signals mixed together in unknown proportions in the observed signals under study. It does this by searching for factors which are mutually statistically independent. It can thus be classified among the latent-variable based methods. Like other methods based on latent variables, a careful investigation has to be carried out to find out which factors are significant and which are not. Therefore, it is important to dispose of a validation procedure to decide on the optimal number of independent components to include in the final model. This can be made complicated by the fact that two consecutive models may differ in the order and signs of similarly-indexed ICs. As well, the structure of the extracted sources can change as a function of the number of factors calculated. Two methods for determining the optimal number of ICs are proposed in this article and applied to simulated and real datasets to demonstrate their performance.

Region-dependent vehicle classification using PCA features

Video-based vehicle detection is the focus of increasing interest due to its potential towards collision avoidance. In particular, vehicle verification is especially challenging due to the enormous variability of vehicles in size, color, pose, etc. In this paper, a new approach based on supervised learning using Principal Component Analysis (PCA) is proposed that addresses the main limitations of existing methods. Namely, in contrast to classical approaches which train a single classifier regardless of the relative position of the candidate (thus ignoring valuable pose information), a region-dependent analysis is performed by considering four different areas. In addition, a study on the evolution of the classification performance according to the dimensionality of the principal subspace is carried out using PCA features within a SVM-based classification scheme. Indeed, the experiments performed on a publicly available database prove that PCA dimensionality requirements are region-dependent. Hence, in this work, the optimal configuration is adapted to each of them, rendering very good vehicle verification results.

Using hybrid latent class model for city-HUBs´users behaviour analysis

Data from an attitudinal survey and stated preference ranking experiment conducted in two urban European interchanges (i.e. City-HUBs) in Madrid (Spain) and Thessaloniki (Greece) show that the importance that City-HUBs users attach to the intermodal infrastructure varies strongly as a function of their perceptions of time spent in the interchange (i.e.intermodal transfer and waiting time). A principal components analysis allocates respondents (i.e. city-HUB users) to two classes with substantially different perceptions of time saving when they make a transfer and of time using during their waiting time.

Class-Conditional Probabilistic Principal Component Analysis: application to gender recognition

Este trabajo presenta una solución al problema del reconocimiento del género de un rostro humano a partir de una imagen. Adoptamos una aproximación que utiliza la cara completa a través de la textura de la cara normalizada y redimensionada como entrada a un clasificador Näive Bayes. Presentamos la técnica de Análisis de Componentes Principales Probabilístico Condicionado-a-la-Clase (CC-PPCA) para reducir la dimensionalidad de los vectores de características para la clasificación y asegurar la asunción de independencia para el clasificador. Esta nueva aproximación tiene la deseable propiedad de presentar un modelo paramétrico sencillo para las marginales. Además, este modelo puede estimarse con muy pocos datos. En los experimentos que hemos desarrollados mostramos que CC-PPCA obtiene un 90% de acierto en la clasificación, resultado muy similar al mejor presentado en la literatura---ABSTRACT---This paper presents a solution to the problem of recognizing the gender of a human face from an image. We adopt a holistic approach by using the cropped and normalized texture of the face as input to a Naïve Bayes classifier. First it is introduced the Class-Conditional Probabilistic Principal Component Analysis (CC-PPCA) technique to reduce the dimensionality of the classification attribute vector and enforce the independence assumption of the classifier. This new approach has the desirable property of a simple parametric model for the marginals. Moreover this model can be estimated with very few data. In the experiments conducted we show that using CCPPCA we get 90% classification accuracy, which is similar result to the best in the literature. The proposed method is very simple to train and implement.

Using the R package Shiny to create web applications that facilitate quantitative gene expression analysis

Thesis (Master's)--University of Washington, 2016-06

Using multivariate analysis to predict the behaviour of soils under effluent irrigation

Onsite wastewater treatment systems aim to assimilate domestic effluent into the environment. Unfortunately failure of such systems is common and inadequate effluent treatment can have serious environmental implications. The capacity of a particular soil to treat wastewater will change over time. The physical properties influence the rate of effluent movement through the soil and its chemical properties dictate the ability to renovate effluent. A research project was undertaken to determine the role that physical and chemical soil properties play in predicting the long-term behaviour of soil under effluent irrigation and to determine if they have a potential function as early indicators of adverse effects of effluent irrigation on treatment sustainability. Principal Component Analysis (PCA) and Cluster Analysis grouped the soils independently of their soil classifications and allowed us to distinguish the most suitable soils for sustainable long term effluent irrigation and determine the most influential soil parameters to characterise them. Multivariate analysis allowed a clear distinction between soils based on the cation exchange capacities. This in turn correlated well with the soil mineralogy. Mixed mineralogy soils in particular sodium or magnesium dominant soils are the most susceptible to dispersion under effluent irrigation. The soil Exchangeable Sodium Percentage (ESP) was identified as a crucial parameter and was highly correlated with percentage clay, electrical conductivity, exchangeable sodium, exchangeable magnesium and low Ca:Mg ratios (less than 0.5).

Novel visualization methods for protein data

Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).