Serosurvey of selected avian pathogens in brazilian commercial Rheas (Rhea americana) and Ostriches (Struthio camelus)

Ratite farming of has expanded worldwide. Due to the intensive farming methods used by ratite producers, preventive medicine practices should be established. In this context, the surveillance and control of some avian pathogens are essential for the success of the ratite industry; however, little is known on the health status of ratites in Brazil. Therefore, the prevalence of antibodies against Newcastle Disease virus, Chlamydophila psittaci, Mycoplasma gallisepticum, Mycoplasma synoviae, and Salmonella Pullorum were evaluated in 100 serum samples collected from commercial ostriches and in 80 serum samples from commercial rheas reared in Brazil. All sampled animals were clinically healthy. The results showed that all ostriches and rheas were serologically negative to Newcastle disease virus, Chlamydophila psittaci, Mycoplasma gallisepticum, and Mycoplasma synoviae. Positive antibody responses against Salmonella Pullorum antigen were not detected in ostrich sera, but were detected in two rhea serum samples. These results can be considered as a warning as to the presence of Salmonella spp. in ratite farms. Therefore, the implementation of good health management and surveillance programs in ratite farms may contribute to improve not only animal production, but also public health conditions.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvi-mento Científico e Tecnológico (CNPq

Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity

Gene-gene interaction affects coronary artery disease risk.

Introdução: Existem vários estudos que comparam doentes coronários e controlos, no sentido de determinar quais os polimorfismos que apresentam risco acrescido de doença das artérias coronárias (DC). Os seus resultados têm sido muitas vezes contraditórios, mas apresentam uma limitação suplementar: avaliam os polimorfismos um a um, quando na natureza os polimorfismos não existem isolados. Põe-se a questão se serão mais importantes associações de polimorfismos mutados no mesmo gene ou em genes diferentes. Objectivo: Com o presente trabalho pretendemos avaliar o risco da associação de polimorfismos em termos de aparecimento de DC no mesmo gene ou em genes diferentes. Metodologia: Estudámos em 298 doentes coronários e 298 controlos sãos o risco associado aos polimorfismos (genótipos considerados de risco), DD da Enzima de Converaão da Angiotensina (ECA) I/D; GG da ECA 8, MM do Angiotensinogénio (AGT) 174; TT do AGT 235; TT da Metiltetrahidrofolato Reductase (MTHFR) 677; AA da MTHFR 1298;RR da Paraoxonase1 (PON1) 192 e MM da PON1 55. Posteriormente avaliámos o risco ligado às associações no mesmo gene (DD da ECA + GG da ECA 8; MM do AGT174 + TT do AGT 235; TT da MTHFR 677 + AA da MTHFR 1298). Finalmente, nos polimorfismos que isoladamente apresentavam significância, avaliámos o risco das associações de polimorfismos a níveis funcionais diferentes (ECA + AGT; ECA + MTHFR; ECA + PON1. Finalmente através de um modelo de regressão logística fomos determinar quais as variáveis que se relacionavam de forma significativa e independente com a DC. Resultados: Os polimorfismos isolados como: ECA DD [P<0.0001], ECA 8 GG [P=0,023], e MTHFR 1298 AA [P=0,049]), apresentaram uma frequência mais elevada nos casos, associando-se de forma significativa ao grupo com DC. A associação de polimorfismos no mesmo gene não teve efeito sinergístico ou aditivo e não aumentou o risco de DC. A associação polimórfica em genes diferentes aumentou o risco de DC quando comparada com o risco do polimorfismo isolado. No caso da associação da ECA DD ou ECA 8 GG com a PON1 192 RR, o risco quadruplicou (OR passou de 1,8 para 4,2). Após regressão logística o hábito tabágico, a história familiar, o fibrinogénio, diabetes, a associação ECA DD ou ECA 8 GG com a MTHFR 1298 AA e a interacção ECA DD ou ECA 8 GG com a PON1 192 RR permaneceram na equação, mostrando ser factores de risco independente para DC. Conclusões: A associação de polimorfismos mutados no mesmo gene nunca aumentou o risco do polimorfismo isolado. A associação com interacção de polimorfismos mutados em genes diferentes, pertencentes a sistemas fisiopatológicos e enzimáticos diferentes, esteve sempre associada a maior risco do que cada polimorfismo por si. Este trabalho levanta, pela primeira vez, a possibilidade de tentativa de compreensão do risco genético coronário em conjunto e não de cada polimorfismo por si.INTRODUCTION: Various studies have compared coronary artery disease (CAD) patients with controls in order to determine which polymorphisms are associated with a higher risk of disease. The results have often been contradictory. Moreover, these studies evaluated polymorphisms in isolation and not in association, which is the way they occur in nature. OBJECTIVE: Our purpose was to evaluate the risk of CAD in patients with associated polymorphisms in the same gene or in differen genes. METHODS: We evaluated the risk associated with ACE DD, ACE 8 CC, ACT 174MM, AGT 235TT, MTHFR 677TT, MTHFR 1298AA, PON1 192RR and PON1 55MM in 298 CAD patients and 298 healthy individuals. We then evaluated the risk of associated polymorphisms in the same gene (ACE DD + ACE 8GG; AGT 174MM + AGT 235TT; MTHFR 677TT + MTHFR 1298AA). Finally, for the isolated polymorphisms which were significant, we evaluated the risk of polymorphism associations at different functional levels (ACE + AGT; ACE + MTHFR; ACE + PON1). Multiple logistic regression was used to identify independent risk factors for CAD. RESULTS: Isolated polymorphisms including ACE DD(p < 0.0001), ACE 8 gg (p=0.023), and MTHFR 1298AA (p = 0.049) presented with a significantly higher frequency in the CAD group. An association of polymorphisms in the same gene did not have an additive or synergistic effect, nor did it increase the risk of CAD. Polymorphic associations in different genes increased the risk of CAD, compared with the isolated polymorphisms. The association of ACE DD or ACE 8 GG with PON1 192RR increased the risk of CA fourfold (1.8 to 4.2). After logistic regression analysis, current smoking, family history, fibrinogen, diabetes, and the ACE DD or ACE 8 GG + MTHFR 1298AA and ACE DD or ACE 8 GG + PON1 192RR associations remained in the, model and proved to be independent predictors of CAD. CONCLUSIONS: The association of polymorphisms in the same gene did not increase the risk of the isolated polymorphism. The association of polymorphisms in genes belonging to different enzyme systems was always linked to increased risk compared to the isolated polymorphisms. This study may contribute to a better understanding of overall genetic risk for CAD rather than that associated with each polymorphism in isolation.info:eu-repo/semantics/publishedVersio

How does predicate invention affect human comprehensibility?

During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as that of Mitchell, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present the results of experiments testing human comprehensibility of logic programs learned with and without predicate invention. Results indicate that comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols

Human paraoxonase gene polymorphisms and coronary artery disease risk.

Introdução: As doenças complexas como a doença das artérias coronárias (DAC), a hipertensão e a diabetes, são usualmente causadas pela susceptibilidade individual a múltiplos genes, factores ambientais e pela interacção entre eles. As enzimas da paraoxonase humana (PON), particularmente a PON1, têm sido implicadas na patogenia da aterosclerose e da DAC. Dois polimorfismos comuns na região codificante do gene, com substituição Glutamina (Q) /Arginina (R) na posição 192 e Leucina /Metionina na posição 55 influenciam a actividade da PON1. Vários estudos têm investigado a associação entre os polimorfismos da PON1 e a DAC, com resultados contraditórios. Objectivo: 1- Avaliar a associação dos polimorfismos da PON1 com o risco de DAC. 2-Estudar a interacção destes polimorfismos com outros situados em genes candidatos diferentes, na susceptibilidade para o aparecimento da DAC. Material e Métodos: Estudámos em 298 doentes coronários e 298 controlos saudáveis, através de um estudo caso/controlo, o risco de DAC associado aos polimorfismos da PON1, 192Q/R e 55L/M. Numa segunda fase avaliámos o risco das interacções polimórficas PON1 192 RR + MTHFR 1298 AA; PON1 192 R/R + ECA DD; PON1 192 R/R + ECA 8 GG. Finalmente construímos um modelo de regressão logística (no qual entraram todas as variáveis genéticas, ambientais e bioquímicas, que tinham mostrado significância estatística na análise univariada), para determinar quais as que se relacionavam de forma significativa e independente com DAC. Resultados: Verificámos que o genótipo PON1 55 MM tinha uma distribuição superior na população doente mas não atingia significância estatística como factor de risco para DAC. O PON1 199 RR apresentou um risco relativo 80% superior relativamente à população que o não possuía (p=0,04). A interacção da PON1 192 RR e da MTHFR 1298 AA, polimorfismos sedeados em genes diferentes, apresentou um risco relativo de DAC de 2,76 (OR=2,76;IC=1,20- 6,47; P=0,009), bastante superior ao risco de cada polimorfismo isolado, assim como a associação da PON1 RR + ECA DD (com polimorfismos também sedeados em genes diferentes), que apresentou um risco 337% superior relativamente aos que não possuíam esta associação (OR=4,37;IC=1,47- 13,87; P=0,002). Da mesma forma a associação entre a PON1 RR e ECA 8 GG, revelou um risco ainda mais elevado (OR=6;23; IC=1,67- 27,37; P<0,001). Após modelo de Regressão Logística as variáveis que ficaram na equação representando factores de risco significativos e independentes para DAC, foram os hábitos tabágicos, doença familiar, diabetes, fibrinogénio, Lp (a) e a associação PON1 192 RR + ECA 8 GG. Esta última associação apresentou, na regressão logística, um OR=14,113; p=0,018 Conclusões: O genótipo PON1 192 RR apresentou, se avaliado isoladamente, um risco relativo de DAC 80% superior relativamente à população que não possuía este genótipo. A associação deste polimorfismo com outros polimorfismos sedeados em genes diferentes, codificando para diferentes enzimas e pertencendo a sistemas fisiopatológicos distintos (MTHFR1298 AA, ECA DD e ECA 8 GG), aumentou sempre o risco de eclosão da DAC. Após correcção para os outros factores de risco clássicos e bioquímicos, a associação PON1 192 RR + ECA 8 GG, continuou a ser um factor de risco significativo e independente para CAD.BACKGROUND: Complex diseases such as coronary artery disease (CAD), hypertension and diabetes are usually caused by individual susceptibility to multiple genes, environmental factors, and the interaction between them. The paraoxonase 1 (PON1) enzyme has been implicated in the pathogenesis of atherosclerosis and CAD. Two common polymorphisms in the coding region of the PON1 gene, which lead to a glutamine (Q)/arginine (R) substitution at position 192 and a leucine (L)/methionine (M) substitution at position 55, influence PON1 activity. Studies have investigated the association between these polymorphisms and CAD, but with conflicting results. AIMS: 1) To evaluate the association between PON1 polymorphisms and CAD risk; and 2) to study the interaction between PON1 polymorphisms and others in different candidate genes. METHODS: We evaluated the risk of CAD associated with PON1 Q192R and L55M polymorphisms in 298 CAD patients and 298 healthy individuals. We then evaluated the risk associated with the interaction of the PON1 polymorphisms with ACE DD, ACE 8 GG and MTHFR 1298AA. Finally, using a logistic regression model, we evaluated which variables (genetic, biochemical and environmental) were linked significantly and independently with CAD. RESULTS: We found that the PON1 55MM genotype was more common in the CAD population, but this did not reach statistical significance as a risk factor for CAD, while PON1 192RR presented an 80% higher relative risk compared to the population without this polymorphism. The interaction between PON1 192RR and MTHFR 1298AA, sited in different genes, increased the risk for CAD, compared with the polymorphisms in isolation (OR=2.76; 95% CI=1.20-6.47; p=0.009), as did the association of PON1 192RR with ACE DD, which presented a 337% higher risk compared to the population without this polymorphic association (OR=4.37; 95% CI=1.47-13.87; p=0.002). Similarly, the association between PON1 192RR and ACE 8 GG was linked to an even higher risk (OR=6.23; 95% CI=1.67-27.37; p<0.001). After logistic regression, smoking, family history, fibrinogen, diabetes, Lp(a) and the association of PON1 192RR + ACE 8 GG remained in the regression model and proved to be significant and independent risk factors for CAD. In the regression model the latter association had OR=14.113; p=0.018. CONCLUSION: When analyzed separately, the PON1 192RR genotype presented a relative risk for CAD 80% higher than in the population without this genotype. Its association with other genetic polymorphisms sited in different genes, coding for different enzymes and belonging to different physiological systems, always increased the risk for CAD. After correction for other conventional and biochemical risk factors, the PON1 192RR + ACE 8 GG association remained a significant and independent risk factor for CAD.info:eu-repo/semantics/publishedVersio

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Sociodemographic characteristics determine dietary pattern adherence during pregnancy

OBJECTIVE:Sociodemographic factors may affect adherence to specific dietary patterns during pregnancy. The present study aimed to identify dietary patterns during pregnancy and associated factors among Brazilian pregnant women. DESIGN:A cross-sectional analysis. Dietary intake was evaluated with a semi-quantitative FFQ during the first postpartum week; the time frame included the second and third gestational trimesters. Principal component analysis was used to identify dietary patterns during pregnancy. Sociodemographic data were obtained using a structured questionnaire. Multiple linear regressions were applied to test the associations between the sociodemographic factors and dietary patterns. SETTING:Mesquita, Rio de Janeiro, Brazil, 2011. SUBJECTS:Postpartum women (n 327) who were 18-45 years of age and Mesquita residents. RESULTS:Three different dietary patterns were identified: 'healthy' (mainly comprising legumes, vegetables and fruits), 'mixed' (mainly comprising candy, butter and margarine, and snacks) and 'traditional' (mainly comprising beans and rice). Women with a higher monthly per capita family income (β=0·0006; 95% CI 0·0001, 0·001; P=0·011) and women of older age (β=0·021; 95% CI -0·001, 0·042; P=0·058) were more likely to adhere to the 'healthy' dietary pattern. Women with higher parity were less likely to adhere to the 'healthy' pattern (β=-0·097; 95% CI -0·184, -0·009; P=0·030) and were more likely to adhere to the 'traditional' pattern (β=0·098; 95% CI 0·021, 0·175; P=0·012). Although not statistically significant, older women were less likely to adhere to the 'mixed' (β=-0·017; 95% CI -0·037, 0·003; P=0·075) and 'traditional' (β=-0·018; 95% CI -0·037, 0·001; P=0·061) dietary patterns. CONCLUSIONS:Monthly per capita family income, parity and maternal age were factors associated with adherence to a healthy diet during pregnancy