A depressão na prespectiva da neuroinflamação

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2014

Mining web data for competency management

We present CORDER (COmmunity Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.

Evidence for the early clinical relevance of hallucinatory-delusional states in the general population

OBJECTIVE: To analyze, in a general population sample, clustering of delusional and hallucinatory experiences in relation to environmental exposures and clinical parameters. METHOD: General population-based household surveys of randomly selected adults between 18 and 65 years of age were carried out. SETTING: 52 countries participating in the World Health Organization's World Health Survey were included. PARTICIPANTS: 225 842 subjects (55.6% women), from nationally representative samples, with an individual response rate of 98.5% within households participated. RESULTS: Compared with isolated delusions and hallucinations, co-occurrence of the two phenomena was associated with poorer outcome including worse general health and functioning status (OR = 0.93; 95% CI: 0.92-0.93), greater severity of symptoms (OR = 2.5 95% CI: 2.0-3.0), higher probability of lifetime diagnosis of psychotic disorder (OR = 12.9; 95% CI: 11.5-14.4), lifetime treatment for psychotic disorder (OR = 19.7; 95% CI: 17.3-22.5), and depression during the last 12 months (OR = 11.6; 95% CI: 10.9-12.4). Co-occurrence was also associated with adversity and hearing problems (OR = 2.0; 95% CI: 1.8-2.3). CONCLUSION: The results suggest that the co-occurrence of hallucinations and delusions in populations is not random but instead can be seen, compared with either phenomenon in isolation, as the result of more etiologic loading leading to a more severe clinical state.

Psychosocial variables associated with the co-occurance of risky sexual behavior with alcohol consumption among university students

This study examined the association of theoretically guided and empirically identified psychosocial variables on the co-occurrence of risky sexual behavior with alcohol consumption among university students. The study utilized event analysis to determine whether risky sex occurred during the same event in which alcohol was consumed. Relevant conceptualizations included alcohol disinhibition, self-efficacy, and social network theories. Predictor variables included negative condom attitudes, general risk taking, drinking motives, mistrust, social group membership, and gender. Factor analysis was employed to identify dimensions of drinking motives. Measured risky sex behaviors were (a) sex without a condom, (b) sex with people not known very well, (c) sex with injecting drug users (IDUs), (d) sex with people without knowing whether they had a STD, and (e) sex with using drugs. A purposive sample was used and included 222 male and female students recruited from a major urban university. Chi-square analysis was used to determine whether participants were more likely to engage in risky sex behavior in different alcohol use contexts. These contexts were only when drinking, only when not drinking, and when drinking or not. The chi-square findings did not support the hypothesis that university students who use alcohol with sex will engage in riskier sex. These results added to the literature by extending other similar findings to a university student sample. For each of the observed risky sex behaviors, discriminant analysis methodology was used to determine whether the predictor variables would differentiate the drinking contexts, or whether the behavior occurred. Results from discriminant analyses indicated that sex with people not known very well was the only behavior for which there were significant discriminant functions. Gender and enhancement drinking motives were important constructs in the classification model. Limitations of the study and implications for future research, social work practice and policy are discussed.

Prevalencia de síntomas relacionados con desórdenes musculo esqueléticos en trabajadores de una empresa dedicada a la venta y distribución de medicamentos y equipos médicos - Colombia, 2015.

Introducción: Los Desórdenes Musculo-Esqueléticos (DME) tienen origen multifactorial. En Colombia corresponden al principal grupo diagnóstico en procesos relacionados con la determinación de origen y pérdida de capacidad laboral. Objetivo: Determinar la relación entre síntomas musculo-esqueléticos y factores relacionados con la carga física en trabajadores de una empresa dedicada a la venta y distribución de medicamentos y equipos médicos, Bogotá (Colombia), en el año 2015. Materiales y Métodos: Estudio de corte transversal en 235 trabajadores. Se incluyeron variables sociodemográficas, ocupacionales y las relacionadas con los síntomas musculoesqueléticos y carga física. Se utilizó en cuestionario ERGOPAR. Para el análisis se utilizó la Prueba Exacta de Fisher, el Odds Ratio (OR) con el Intervalo de Confianza (IC) del 95%. Se realizó el análisis Multivariado con Regresión Logística Binaria. Resultados: La prevalencia de síntomas relacionados con DME fue de 79,2%, siendo más prevalente en cuello, hombros y columna dorsal (48,1%), seguido por columna lumbar (35,3%). Se encontró una asociación entre síntomas en cuello, hombros y/o columna dorsal con el sexo femenino (p=0,005, OR=2,33, 95%IC: 1,2-4,2); adoptar postura bípeda menos de 30 minutos (p=0,004, OR=3,34, 95%IC: 1,4-7,6); adoptar postura cabeza/cuello inclinado hacia delante entre 30 minutos y 2 horas (p=0,007, OR=3,25, 95%IC :1,3-7,7) y en columna lumbar con adoptar postura espalda/tronco hacia delante entre 30 minutos y 2 horas (p=0,001, OR=4,27, 95%IC: 1,7-10,3); y la antigüedad en el cargo entre 1 y 5 años (p=0,009, OR=3,47, 95%IC: 1,3-8,8). Conclusión: Las posturas bípedas con y sin desplazamiento, inclinaciones de tronco y cabeza, transporte manual de cargas, sexo femenino, antigüedad en el cargo y edad están asociadas conjuntamente al riesgo para presentar DME.

Co-Infestation and Spatial Distribution of Bactrocera carambolae and Anastrepha spp. (Diptera: Tephritidae) in Common Guava in the Eastern Amazon.

Field infestation and spatial distribution of introduced Bactrocera carambolae Drew and Hancock and native species of Anastrepha in common guavas [Psidium guajava (L.)] were investigated in the eastern Amazon. Fruit sampling was carried out in the municipalities of Calc¸oene and Oiapoque in the state of Amapa, Brazil. The frequency distribution of larvae in fruit was fitted to the negative binomial distribution. Anastrepha striata was more abundant in both sampled areas in comparison to Anastrepha fraterculus (Wiedemann) and B. carambolae. The frequency distribution analysis of adults revealed an aggregated pattern for B. carambolae as well as for A. fraterculus and Anastrepha striata Schiner, described by the negative binomial distribution. Although the populations of Anastrepha spp. may have suffered some impact due to the presence of B. carambolae, the results are still not robust enough to indicate effective reduction in the abundance of Anastrepha spp. caused by B. carambolae in a general sense. The high degree of aggregation observed for both species suggests interspecific co-occurrence with the simultaneous presence of both species in the analysed fruit. Moreover, a significant fraction of uninfested guavas also indicated absence of competitive displacement.

Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation

To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.

Substance Treatment Options in Psychosis (STOP) : a new intervention for dual diagnosis

Despite the high co-occurrence of psychosis and substance abuse, there is very little research on the development of effective treatments for this problem. This paper describes a new intervention that facilitates reaching functional goals through collaboration between therapists, participants and families. Substance Treatment Options in Psychosis (STOP) integrates pharmacological and psycho-logical treatments for psychotic symptoms, with cognitive-behavioural approaches to substance abuse. STOP is tailored to participants' problems and abilities, and recognises that control of consumption and even engagement may take several attempts. Training in relevant skills is augmented by bibliotherapy, social support and environmental change. A case description illustrates the issues and challenges in implementation.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distri- butions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Treatment of comorbidity

Cormorbity means the co-occurrence of one or more diseases or disorders in an individual. The National Comorbity Project aims to highlight this type of comorbity and identify appropriate strategies and policies responses.

Substance use disorders

Basic competencies in assessing and treating substance use disorders should be core to the training of any clinical psychologist, because of the high frequency of risky or problematic substance use in the community, and its high co-occurrence with other problems. Skills in establishing trust and a therapeutic alliance are particularly important in addiction, given the stigma and potential for legal sanctions that surround it. The knowledge and skills of all clinical practitioners should be sufficient to allow valid screening and diagnosis of substance use disorders, accurate estimation of consumption and a basic functional analysis. Practitioners should also be able to undertake brief interventions including motivational interviews, and appropriately apply generic interventions such as problem solving or goal setting to addiction. Furthermore, clinical psychologists should have an understanding of the nature, evidence base and indications for biochemical assays, pharmacotherapies and other medical treatments, and ways these can be integrated with psychological practice. Specialists in addiction should have more sophisticated competencies in each of these areas. They need to have a detailed understating of current addiction theories and basic and applied research, be able to undertake and report on a detailed psychological assessment, and display expert competence in addiction treatment. These skills should include an ability to assess and manage complex or co-occurring problems, to adapt interventions to the needs of different groups, and to assist people who have not responded to basic treatments. They should also be able to provide consultation to others, undertake evaluations of their practice, and monitor and evaluate emerging research data in the field.

A new approach for finding an appropriate combination of texture parameters for classification

This paper suggests an approach for finding an appropriate combination of various parameters for extracting texture features (e.g. choice of spectral band for extracting texture feature, size of the moving window, quantization level of the image, and choice of texture feature etc.) to be used in the classification process. Gray level co-occurrence matrix (GLCM) method has been used for extracting texture from remotely sensed satellite image. Results of the classification of an Indian urban environment using spatial property (texture), derived from spectral and multi-resolution wavelet decomposed images have also been reported. A multivariate data analysis technique called ‘conjoint analysis’ has been used in the study to analyze the relative importance of these parameters. Results indicate that the choice of texture feature and window size have higher relative importance in the classification process than quantization level or the choice of image band for extracting texture feature. In case of texture features derived using wavelet decomposed image, the parameter ‘decomposition level’ has almost equal relative importance as the size of moving window and the decomposition of images up to level one is sufficient and there is no need to go for further decomposition. It was also observed that the classification incorporating texture features improves the overall classification accuracy in a statistically significant manner in comparison to pure spectral classification.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document’s initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur’s search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.