Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Fate Hazardous elements in soils surrounding a coal-fired power plant complex

Proceedings of the 13th International UFZ-Deltares Conference on Sustainable Use and Management of Soil, Sediment and Water Resources - 9–12 June 2015 • Copenhagen, Denmark

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Spatial analysis of crime evolution in Portugal between 1995 and 2013

The main objective of this survey was to perform descriptive analysis of crime evolution in Portugal between 1995 and 2013. The main focus of this survey was to analyse spatial crime evolution patterns in Portuguese NUTS III regions. Most important crime types have been included into analysis. The main idea was to uncover relation between local patterns and global crime evolution; to define regions which have contributed to global crime evolution of some specific crime types and to define how they have contributed. There were many statistical reports and scientific papers which have analysed some particular crime types, but one global spatial-temporal analysis has not been found. Principal Component Analysis and multidimensional descriptive data analysis technique STATIS have been the base of the analysis. The results of this survey has shown that strong spatial and temporal crime patterns exist. It was possible to describe global crime evolution patterns and to define crime evolution patterns in NUTS III regions. It was possible to define three to four groups of crimes where each group shows similar spatial crime dynamics.

Fatores de risco auto reportados associados aos acidentes rodoviários : um estudo sobre os condutores portugueses de veículos ligeiros

RESUMO - Objetivos: Anualmente morrem cerca de 1,3 milhões de pessoas, a nível mundial, devido aos acidentes de viação. Também mais de 20 milhões de pessoas sofrem ferimentos ligeiros ou graves devido aos acidentes de viação que resultam em incapacidade temporária ou permanente. Desta forma, consideram-se os acidentes de viação, um grave problema de saúde pública, com custos elevados para as sociedades afetando a saúde das populações e economias de cada país. Este estudo pretendeu descrever e caracterizar os condutores de veículos ligeiros, residentes em Portugal Continental, abrangendo características sociodemográficas, experiência de condução e questões relativas a atitudes, opiniões e comportamentos. Por outro lado procurou-se analisar a associação entre as opiniões, atitudes e comportamentos, auto reportados e a ocorrência de um acidente de viação nos últimos três anos a fim de construir um modelo final preditivo do risco de sofrer um acidente de viação. Método: Foi realizado um estudo observacional analítico transversal baseado num questionário traduzido para a língua portuguesa e com origem no projeto europeu SARTRE 4. A população-alvo foram todos os condutores de veículos ligeiros possuidores de uma licença de condução e residentes em Portugal Continental, baseado numa amostra de igual dimensão à definida no estudo europeu SARTRE 4 (600 condutores de veículos ligeiros). Das 52 perguntas existentes, selecionaram-se pela análise de componentes principais (ACP) variáveis potencialmente independentes e complementares para as componentes opiniões, atitudes e comportamentos. Para além das medidas descritivas usuais, recorreu-se à regressão logística binária para analisar associações e obter um modelo que permitisse estimar a probabilidade de sofrer um acidente rodoviário em função das variáveis selecionadas referentes às opiniões, atitudes e comportamentos auto reportados. Resultados: Dos 612 condutores inquiridos, 62,7% (383) responderam não ter sofrido nenhum acidente de viação nos últimos três anos enquanto 37,3% (228) respondeu ter estado envolvido em pelo menos um acidente de viação com danos materiais ou feridos, no mesmo período. De uma forma geral, o típico condutor que referiu ter sofrido um acidente nos últimos três anos é homem com mais de 65 anos de idade, com o 1º ensino básico, viúvo e sem filhos, não empregado e reside numa área urbana. Os condutores residentes numa área suburbana apresentaram um risco 5,368 mais elevado de sofrer um acidente de viação em relação aos condutores que habitam numa zona rural (IC 95%: 2,344-12,297; p<0,001). Os condutores que foram apenas submetidos uma vez a um controlo de álcool, nos últimos três anos, durante o exercício da condução apresentaram um risco 3,009 superior de sofrer um acidente de viação em relação aos condutores que nunca foram fiscalizados pela polícia (IC 95%: 1,949-4,647, p<0,001). Os condutores que referiram muito frequentemente parar para dormir quando se sentem cansados a conduzir têm uma probabilidade inferior de 81% de sofrer um acidente de viação em relação aos condutores que nunca o fazem (IC 95%: 0,058-0,620; p=0,006). Os condutores que quando cansados raramente bebem um café/bebida energética têm um risco de 4,829 superior de sofrer um acidente de viação do que os condutores que sempre o referiram fazer (IC 95%:1,807-12,903; p=0,002). Conclusões: Os resultados obtidos em relação aos fatores comportamentais vão ao encontro da maioria dos fatores de risco associados aos acidentes de viação referidos na literatura. Ainda assim, foram identificadas novas associações entre o risco de sofrer um acidente e as opiniões e as atitudes auto reportadas que através de estudos de maiores dimensões populacionais poderão vir a ser mais exploradas. Este trabalho vem reforçar a necessidade urgente de novas estratégias de intervenção, principalmente na componente comportamental, direcionadas aos grupos de risco, mantendo as existentes.

The role of ascorbate peroxidase, guaiacol peroxidase, and polysaccharides in cassava (Manihot esculenta Crantz) roots under postharvest physiological deterioration

Abstract This study aimed to investigate the role of ascorbate peroxidase (APX), guaiacol peroxidase (GPX), polysaccharides, and protein contents associated with the early events of postharvest physiological deterioration (PPD) in cassava roots. Increases in APX and GPX activity, as well as total protein contents occurred from 3 to 5 days of storage and were correlated with the delay of PPD. Cassava samples stained with periodic acid-Schiff (PAS) highlighted the presence of starch and cellulose. Degradation of starch granules during PPD was also detected. Slight metachromatic reaction with toluidine blue is indicative of increasing of acidic polysaccharides and may play an important role in PPD delay. Principal component analysis (PCA) classified samples according to their levels of enzymatic activity based on the decision tree model which showed GPX and total protein amounts to be correlated with PPD. The Oriental (ORI) cultivar was more susceptible to PPD.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Seasonal and spatial variations in limnological conditions of a floodplain lake (lake Catalão) connected to both the Solimões and Negro Rivers, Central Amazonia

Lakes play an important role in biogeochemical, ecological and hydrological processes in the river-floodplain system. The aim of this study was to evaluate the dynamics of the limnological conditions of Catalão Lake, an Amazon floodplain lake. Thus, some of the main limnological environment variables (O2, temperature, pH, nutrient, electrical conductivity) of the Catalão Lake were analyzed under temporal and spacial scales. The study was conducted between November/2004 and August/2005. Sampling excursion were carried out every three months; one excursion for each of the four different hydrological periods (low water, rising water, high water and falling water). Sampling points were chosen so that it could be obtained a gradient of the distance from Negro River. Limnological profiles in Catalão Lake showed generally acidic to slightly alcaline water, with low levels of dissolved oxygen and low concentrations of soluble reactive phosphorous. The Negro River seems to exert the main influence during the rising water period, while the Solimões River is the principal controlling river during peak water. The Principal Component Analysis (PCA) grouped the seasonal collections by hydrological period, showing the formation of a north-south spatial gradient within the lake in relation to the limnological variables. Multivariate dispersion analysis based on distance-to-centroid method demonstrated an increase in similarity over the course of the hydrological cycle, as the lake was inundated in response to the flood pulse of the main river channels. However, the largest spatial homogeneity in the lake was observed in the epilimnion layer, during the falling water period. The daily analysis of variation indicated an oligomitic pattern during the years in which the lake was permanently connected to the Negro River. Although Catalão Lake receives large quantities of both black water from the Negro River and sediment-filled water from the Solimões River, the physical and chemical characteristics of the lake are more similar to those of the Solimões (várzea lake) than the Negro (blackwater lake).

Systems biology approaches for the design of novel Saccharomyces cerevisiae winemaking strains for enhanced flavour compounds synthesis

Tese de Doutoramento em Biologia Ambiental e Molecular

Deteção remota na avaliação das alterações da cobertura do solo no Parque Nacional da Peneda-Gerês

Dissertação de mestrado integrado em Engenharia Civil

Exploring the factor structure of neurocognitive measures in older individuals

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the "best fit" model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate.

Analysis of functional magnetic resonance imaging time series by independent component analysis

Independent Component Analysis, Time Series Analysis, Functional Magnetic Resonance Imaging

Intraspecific ecomorphological variations in Poecilia reticulata (Actinopterygii, Cyprinodontiformes): comparing populations of distinct environments

ABSTRACT Morphological variations, according to the principles of ecomorphology, can be related to different aspects of the organism way of life, such as occupation of habitats and feeding behavior. The present study sought to examine the intraspecific variation in two populations of Poecilia reticulata Peters, 1859, that occur in two types of environments, a lotic (Maringá Stream) and a lentic (Jaboti Lake). Due to a marked sexual dimorphism, males and females were analyzed separately. Thus, the proposed hypotheses were that the populations that occur in distinct environments present morphological differences. The morphological variables were obtained using morphometric measurements and the ecomorphological indexes. The data were summarized in a Principal Component Analysis (PCA). A Multivariate Analysis of Variance (Manova) was made to verify significant differences in morphology between the populations. Males and females showed similar ecomorphological patterns according to the environment they occur. In general the population from Maringá Stream had fins with major areas, and the Jaboti Lake population eyes located more dorsally. Additionally, others morphological differences such as wider mouth of the males from Maringá Stream, wider heads on Jaboti Lake females and more protractible mouths on males from Jaboti Lake suggest a set of environmental variables that can possibly influence the ecomorphological patterns of the populations, as the water current, availability of food resources and predation. In summary, the initial hypotheses could be confirmed, evidencing the occurrence of distinct ecomorphotypes in the same species according to the environment type.

Objective analysis of gait coordination for total knee arthroplasty outcome

Introduction: Coordination is a strategy chosen by the central nervous system to control the movements and maintain stability during gait. Coordinated multi-joint movements require a complex interaction between nervous outputs, biomechanical constraints, and pro-prioception. Quantitatively understanding and modeling gait coordination still remain a challenge. Surgeons lack a way to model and appreciate the coordination of patients before and after surgery of the lower limbs. Patients alter their gait patterns and their kinematic synergies when they walk faster or slower than normal speed to maintain their stability and minimize the energy cost of locomotion. The goal of this study was to provide a dynamical system approach to quantitatively describe human gait coordination and apply it to patients before and after total knee arthroplasty. Methods: A new method of quantitative analysis of interjoint coordination during gait was designed, providing a general model to capture the whole dynamics and showing the kinematic synergies at various walking speeds. The proposed model imposed a relationship among lower limb joint angles (hips and knees) to parameterize the dynamics of locomotion of each individual. An integration of different analysis tools such as Harmonic analysis, Principal Component Analysis, and Artificial Neural Network helped overcome high-dimensionality, temporal dependence, and non-linear relationships of the gait patterns. Ten patients were studied using an ambulatory gait device (Physilog®). Each participant was asked to perform two walking trials of 30m long at 3 different speeds and to complete an EQ-5D questionnaire, a WOMAC and Knee Society Score. Lower limbs rotations were measured by four miniature angular rate sensors mounted respectively, on each shank and thigh. The outcomes of the eight patients undergoing total knee arthroplasty, recorded pre-operatively and post-operatively at 6 weeks, 3 months, 6 months and 1 year were compared to 2 age-matched healthy subjects. Results: The new method provided coordination scores at various walking speeds, ranged between 0 and 10. It determined the overall coordination of the lower limbs as well as the contribution of each joint to the total coordination. The difference between the pre-operative and post-operative coordination values were correlated with the improvements of the subjective outcome scores. Although the study group was small, the results showed a new way to objectively quantify gait coordination of patients undergoing total knee arthroplasty, using only portable body-fixed sensors. Conclusion: A new method for objective gait coordination analysis has been developed with very encouraging results regarding the objective outcome of lower limb surgery.