Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

Breast cancer prognostic classification in the molecular era: the role of histological grade.

Breast cancer is a heterogeneous disease with varied morphological appearances, molecular features, behavior, and response to therapy. Current routine clinical management of breast cancer relies on the availability of robust clinical and pathological prognostic and predictive factors to support clinical and patient decision making in which potentially suitable treatment options are increasingly available. One of the best-established prognostic factors in breast cancer is histological grade, which represents the morphological assessment of tumor biological characteristics and has been shown to be able to generate important information related to the clinical behavior of breast cancers. Genome-wide microarray-based expression profiling studies have unraveled several characteristics of breast cancer biology and have provided further evidence that the biological features captured by histological grade are important in determining tumor behavior. Also, expression profiling studies have generated clinically useful data that have significantly improved our understanding of the biology of breast cancer, and these studies are undergoing evaluation as improved prognostic and predictive tools in clinical practice. Clinical acceptance of these molecular assays will require them to be more than expensive surrogates of established traditional factors such as histological grade. It is essential that they provide additional prognostic or predictive information above and beyond that offered by current parameters. Here, we present an analysis of the validity of histological grade as a prognostic factor and a consensus view on the significance of histological grade and its role in breast cancer classification and staging systems in this era of emerging clinical use of molecular classifiers.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Desenvolupament d'algorismes biològicament inspirats basats en Support Vector Machines

L'objectiu d'aquest projecte ha estat el desenvolupament d'algorismes biològicament inspirats per a l'olfacció artificial. Per a assolir-lo ens hem basat en el paradigma de les màquines amb suport vectorial. Hem construit algoritmes que imitaven els processos computacionals dels diferents sistemes que formen el sistema olfactiu dels insectes, especialment de la llagosta Schistocerca gregaria. Ens hem centrat en el lòbuls de les antenes, i en el cos fungiforme. El primer està considerat un dispositiu de codificació de les olors, que a partir de la resposta temporal dels receptors olfactius a les antenes genera un patró d'activació espaial i temporal. Quant al cos fungiforme es considera que la seva funció és la d'una memòria per als olors, així com un centre per a la integració multi-sensorial. El primer pas ha estat la construcció de models detallats dels dos sistemes. A continuació, hem utilitzat aquests models per a processar diferents tipus de senyals amb l'objectiu de abstraure els principis computacionals subjacents. Finalment, hem avaluat les capacitats d'aquests models abstractes, i els hem utilitzat per al processat de dades provinents de sensors de gasos. Els resultats mostren que el models abstractes tenen millor comportament front el soroll i més capacitat d'emmagatzematge de records que altres models més clàssics, com ara les memòries associatives de Hopfield o fins i tot en determinades circumstàncies que les mateixes Support Vector Machines.

A hybrid method for sag source location in power network

The work presented in this paper belongs to the power quality knowledge area and deals with the voltage sags in power transmission and distribution systems. Propagating throughout the power network, voltage sags can cause plenty of problems for domestic and industrial loads that can financially cost a lot. To impose penalties to responsible party and to improve monitoring and mitigation strategies, sags must be located in the power network. With such a worthwhile objective, this paper comes up with a new method for associating a sag waveform with its origin in transmission and distribution networks. It solves this problem through developing hybrid methods which hire multiway principal component analysis (MPCA) as a dimension reduction tool. MPCA reexpresses sag waveforms in a new subspace just in a few scores. We train some well-known classifiers with these scores and exploit them for classification of future sags. The capabilities of the proposed method for dimension reduction and classification are examined using the real data gathered from three substations in Catalonia, Spain. The obtained classification rates certify the goodness and powerfulness of the developed hybrid methods as brand-new tools for sag classification

Primary motor cortex involvement in Alzheimer disease.

In Alzheimer disease (AD) the involvement of entorhinal cortex, hippocampus, and associative cortical areas is well established. Regarding the involvement of the primary motor cortex the reported data are contradictory. In order to determine whether the primary motor cortex is involved in AD, the brains of 29 autopsy cases were studied, including, 17 cases with severe cortical AD-type changes with definite diagnoses of AD, 7 age-matched cases with discrete to moderate cortical AD-type changes, and 5 control cases without any AD-type cortical changes. Morphometric analysis of the cortical surface occupied by senile plaques (SPs) on beta-amyloid-immunostained sections and quantitative analysis of neurofibrillary tangles (NFTs) on Gallyas-stained sections was performed in 5 different cortical areas including the primary motor cortex. The percentage of cortical surface occupied by SPs was similar in all cortical areas, without significant difference and corresponded to 16.7% in entorhinal cortex, 21.3% in frontal associative, 16% in parietal associative, and 15.8% in primary motor cortex. The number of NFTs in the entorhinal cortex was significantly higher (41 per 0.4 mm2), compared with those in other cortical areas (20.5 in frontal, 17.9 in parietal and 11.5 in the primary motor cortex). Our findings indicate that the primary motor cortex is significantly involved in AD and suggest the appearance of motor dysfunction in late and terminal stages of the disease.

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

A genetic algorithm to ensemble feature selection

We are going to implement the "GA-SEFS" by Tsymbal and analyse experimentally its performance depending on the classifier algorithms used in the fitness function (NB, MNge, SMO). We are also going to study the effect of adding to the fitness function a measure to control complexity of the base classifiers.

Advance care planning for institutionalised older people: an integrative review of the literature.

BACKGROUND: This integrative review of the literature describes the evolution in knowledge and the paradigm shift that is necessary to switch from advance directives to advance care planning. AIMS AND OBJECTIVES: It presents an analysis of concepts, trends, models and experiments that enables identification of the best treatment strategies, particularly for older people living in nursing homes. DESIGN: Based on 23 articles published between 1999 and 2012, this review distinguishes theoretical from empirical research and presents a classification of studies based on their methodological robustness (descriptive, qualitative, associative or experimental). RESULTS: It thus provides nursing professionals with evidence-based information in the form of a synthetic vision and conceptual framework to support the development of innovative care practices in the end-of-life context. While theoretical work places particular emphasis on the impact of changes in practice on the quality of care received by residents, empirical research highlights the importance of communication between the different persons involved about care preferences at the end of life and the need for agreement between them. CONCLUSIONS: The concept of quality of life and the dimensions and factors that compose it form the basis of Advance care planning (ACP) and enable the identification of the similarities and differences between various actors. They inform professionals of the need to ease off the biomedical approach to consider the attributes prioritised by those concerned, whether patients or families, so as to improve the quality of care at the end of life. IMPLICATIONS FOR PRACTICE: It is particularly recommended that all professionals involved take into account key stakeholders' expectations concerning what is essential at the end of life, to enable enhanced communication and decision-making when faced with this difficult subject.

Brain tissue properties differentiate between motor and limbic basal ganglia circuits.

Despite advances in understanding basic organizational principles of the human basal ganglia, accurate in vivo assessment of their anatomical properties is essential to improve early diagnosis in disorders with corticosubcortical pathology and optimize target planning in deep brain stimulation. Main goal of this study was the detailed topological characterization of limbic, associative, and motor subdivisions of the subthalamic nucleus (STN) in relation to corresponding corticosubcortical circuits. To this aim, we used magnetic resonance imaging and investigated independently anatomical connectivity via white matter tracts next to brain tissue properties. On the basis of probabilistic diffusion tractography we identified STN subregions with predominantly motor, associative, and limbic connectivity. We then computed for each of the nonoverlapping STN subregions the covariance between local brain tissue properties and the rest of the brain using high-resolution maps of magnetization transfer (MT) saturation and longitudinal (R1) and transverse relaxation rate (R2*). The demonstrated spatial distribution pattern of covariance between brain tissue properties linked to myelin (R1 and MT) and iron (R2*) content clearly segregates between motor and limbic basal ganglia circuits. We interpret the demonstrated covariance pattern as evidence for shared tissue properties within a functional circuit, which is closely linked to its function. Our findings open new possibilities for investigation of changes in the established covariance pattern aiming at accurate diagnosis of basal ganglia disorders and prediction of treatment outcome.

Improving the organisation and commercialisation of small coffee and cocoa producers in the Northern Amazon Region of Ecuador

Coffee and cocoa represent the main sources of income for small farmers in the Northern Amazon Region of Ecuador. The provinces of Orellana and Sucumbios, as border areas, have benefited from investments made by many public and private institutions. Many of the projects carried out in the area have been aimed at energising the production of coffee and cocoa, strengthening the producers’ associations and providing commercialisation infrastructure. Improving the quality of life of this population threatened by poverty and high migration flows mainly from Colombia is a significant challenge. This paper presents research highlighting the importance of associative commercialisation to raising income from coffee and cocoa. The research draws on primary information obtained during field work, and from official information from the Ministry of Agriculture. The study presents an overview of current organisational structures, initiatives of associative commercialisation, stockpiling of infrastructure and ownership regimes, as well as estimates for both ‘robusta’ coffee and national cocoa production and income. The analysis of the main constraints presents different alternatives for the implementation of public land policies. These policies are aimed at mitigating the problems associated with the organisational structure of the producers, with processes of commercialisation and with environmental aspects, among others.

Automatic Detection of Non-deverbal Event Nouns for Quick Lexicon Production

In this work we present the results of experimental work on the development of lexical class-based lexica by automatic means. Our purpose is to assess the use of linguistic lexical-class based information as a feature selection methodology for the use of classifiers in quick lexical development. The results show that the approach can help reduce the human effort required in the development of language resources significantly.

Quantitative distribution of parvalbumin, calretinin, and calbindin D-28k immunoreactive neurons in the visual cortex of normal and Alzheimer cases.

The distribution of parvalbumin (PV), calretinin (CR), and calbindin (CB) immunoreactive neurons was studied with the help of an image analysis system (Vidas/Zeiss) in the primary visual area 17 and associative area 18 (Brodmann) of Alzheimer and control brains. In neither of these areas was there a significant difference between Alzheimer and control groups in the mean number of PV, CR, or CB immunoreactive neuronal profiles, counted in a cortical column going from pia to white matter. Significant differences in the mean densities (numbers per square millimeter of cortex) of PV, CR, and CB immunoreactive neuronal profiles were not observed either between groups or areas, but only between superficial, middle, and deep layers within areas 17 and 18. The optical density of the immunoreactive neuropil was also similar in Alzheimer and controls, correlating with the numerical density of immunoreactive profiles in superficial, middle, and deep layers. The frequency distribution of neuronal areas indicated significant differences between PV, CR, and CB immunoreactive neuronal profiles in both areas 17 and 18, with more large PV than CR and CB positive profiles. There were also significantly more small and less large PV and CR immunoreactive neuronal profiles in Alzheimer than in controls. Our data show that, although the brain pathology is moderate to severe, there is no prominent decrease of PV, CR and CB positive neurons in the visual cortex of Alzheimer brains, but only selective changes in neuronal perikarya.

Social Representations Of Violence Against Rural Women: Unveiling Senses In Multiple Views

We analyze the social representations of violence against women from the perspective of city managers, professionals and health workers in rural settings of the southern half of Rio Grande do Sul. The study has a qualitative approach and adds a theoretical/methodological perspective of social representations. The data were generated by means of the associative method, question-stimulus of words and expressions emergence. The analysis of word association was performed with EVOC software, considering frequency and order of association with inducing terms. Participants recognize violence against women as gender destination that induces consent, resignation, guilt and fear, and results in naturalization and trivialization of this social phenomenon. We highlight the need to produce ruptures in established and traditional forms of health care, in the conservative and stereotypical views of violence, favoring access to friendly service and avoiding the reproduction of gender inequalities.


Développement d'une puce à ADN métabolique et application à l'étude d'un modèle murin d'obésité et de diabète de type II

Résumé tout public : Le développement du diabète de type II et de l'obésité est causé par l'interaction entre des gènes de susceptibilité et des facteurs environnementaux, en particulier une alimentation riche en calories et une activité physique insuffisante. Afín d'évaluer le rôle de l'alimentation en absence d'hétérogénéité génétique, nous avons nourri une lignée de souris génétiquement pure avec un régime extrêmement gras. Ce régime a conduit à l'établissement de différents phénotypes parmi ces souris, soit : un diabète et une obésité (ObD), un diabète mais pas d'obésité (LD) ou ni un diabète, ni une obésité (LnD). Nous avons fait l'hypothèse que ces adaptations différentes au stress nutritionnel induit par le régime gras étaient dues à l'établissement de programmes génétiques différents dans les principaux organes impliqués dans le maintien de l'équilibre énergétique. Afin d'évaluer cette hypothèse, nous avons développé une puce à ADN contenant approximativement 700 gènes du métabolisme. Cette puce à ADN, en rendant possible la mesure simultanée de l'expression de nombreux gènes, nous a permis d'établir les profils d'expression des gènes caractéristiques de chaque groupe de souris nourries avec le régime gras, dans le foie et le muscle squelettique. Les données que nous avons obtenues à partir de ces profils d'expression ont montré que des changements d'expression marqués se produisaient dans le foie et le muscle entre les différents groupes de souris nourries avec le régime gras. Dans l'ensemble, ces changements suggèrent que l'établissement du diabète de type II et de l'obésité induits par un régime gras est associé à une synthèse accrue de lipides par le foie et à un flux augmenté de lipides du foie jusqu'à la périphérie (muscles squelettiques). Dans un deuxième temps, ces profils d'expression des gènes ont été utilisés pour sélectionner un sous-ensemble de gènes suffisamment discriminants pour pouvoir distinguer entre les différents phénotypes. Ce sous-ensemble de gènes nous a permis de construire un classificateur phénotypique capable de prédire avec une précision relativement élevée le phénotype des souris. Dans le futur, de tels « prédicteurs » basés sur l'expression des gènes pourraient servir d'outils pour le diagnostic de pathologies liées au métabolisme. Summary: Aetiology of obesity and type II diabetes is multifactorial, involving both genetic and environmental factors, such as calory-rich diets or lack of exercice. Genetically homogenous C57BL/6J mice fed a high fat diet (HFD) up to nine months develop differential adaptation, becoming either obese and diabetic (ObD) or remaining lean in the presence (LD) or absence (LnD) of diabetes development. Each phenotype is associated with diverse metabolic alterations, which may result from diverse molecular adaptations of key organs involved in the control of energy homeostasis. In this study, we evaluated if specific patterns of gene expression could be associated with each different phenotype of HFD mice in the liver and the skeletal muscles. To perform this, we constructed a metabolic cDNA microarray containing approximately 700 cDNA representing genes involved in the main metabolic pathways of energy homeostasis. Our data indicate that the development of diet-induced obesity and type II diabetes is linked to some defects in lipid metabolism, involving a preserved hepatic lipogenesis and increased levels of very low density lipoproteins (VLDL). In skeletal muscles, an increase in fatty acids uptake, as suggested by the increased expression of lipoprotein lipase, would contribute to the increased level of insulin resistance observed in the ObD mice. Conversely, both groups of lean mice showed a reduced expression in lipogenic genes, particularly stearoyl-CoA desaturase 1 (Scd-1), a gene linked to sensitivity to diet-induced obesity. Secondly, we identified a subset of genes from expression profiles that classified with relative accuracy the different groups of mice. Such classifiers may be used in the future as diagnostic tools of each metabolic state in each tissue. Résumé Développement d'une puce à ADN métabolique et application à l'étude d'un modèle murin d'obésité et de diabète de type II L'étiologie de l'obésité et du diabète de type II est multifactorielle, impliquant à la fois des facteurs génétiques et environnementaux, tels que des régimes riches en calories ou un manque d'exercice physique. Des souris génétiquement homogènes C57BL/6J nourries avec un régime extrêmement gras (HFD) pendant 9 mois développent une adaptation métabolique différentielle, soit en devenant obèses et diabétiques (ObD), soit en restant minces en présence (LD) ou en absence (LnD) d'un diabète. Chaque phénotype est associé à diverses altérations métaboliques, qui pourraient résulter de diverses adaptations moléculaires des organes impliqués dans le contrôle de l'homéostasie énergétique. Dans cette étude, nous avons évalué si des profils d'expression des gènes dans le foie et le muscle squelettique pouvaient être associés à chacun des phénotypes de souris HFD. Dans ce but, nous avons développé une puce à ADN métabolique contenant approximativement 700 ADNc représentant des gènes impliqués dans les différentes voies métaboliques de l'homéostasie énergétique. Nos données indiquent que le développement de l'obésité et du diabète de type II induit par un régime gras est associé à certains défauts du métabolisme lipidique, impliquant une lipogenèse hépatique préservée et des niveaux de lipoprotéines de très faible densité (VLDL) augmentés. Au niveau du muscle squelettique, une augmentation du captage des acides gras, suggéré par l'expression augmentée de la lipoprotéine lipase, contribuerait à expliquer la résistance à l'insuline plus marquée observée chez les souris ObD. Au contraire, les souris minces ont montré une réduction marquée de l'expression des gènes lipogéniques, en particulier de la stéaroyl-CoA désaturase 1 (scd-1), un gène associé à la sensibilité au développement de l'obésité par un régime gras. Dans un deuxième temps, nous avons identifié un sous-ensemble de gènes à partir des profils d'expression, qui permettent de classifier avec une précision relativement élevée les différents groupes de souris. De tels classificateurs pourraient être utilisés dans le futur comme outils pour le diagnostic de l'état métabolique d'un tissu donné.