Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy

Batch growth of Kluyveromyces lactis cells from deproteinized whey: Response surface methodology versus Artificial neural network \u2013 Genetic algorithm approach

Deproteinized cheese making whey (CMW) was investigated as an alternative medium for the production of Kluyveromyces lactis as single-cell protein. Batch runs were performed according to a Full Factorial Design (FFD) on CMW supplemented with yeast extract, magnesium sulfate and ammonium sulfate in different concentrations. These independent variables were tested in duplicate at three levels, while dry biomass productivity was used as the response. The results were used to construct two models, one based on Response Surface Methodology (RSM) and another on Artificial Neural Network (ANN). Two different training methods (10-fold cross validation and training/testing) were utilized to obtain two different network architectures, while a genetic algorithm was utilized to obtain optimal concentrations of the above medium components. A quadratic regression by RSM (R2=0.840) was the best modeling and optimization tool under the specific conditions selected here. The highest biomass productivity (approximately 2.14 gDW/L h) was ensured by the following optimal levels: 7.04-9.99 % (w/v) yeast extract, 0.430-0.503 % (w/v) magnesium sulfate and 4.0 % (w/v) ammonium sulfate. These results demonstrate the feasibility of using CMW as an interesting alternative to produce single-cell protein

Avaliação e tratamento da dor perineal no pós-parto vaginal Evaluación y tratamiento del dolor perineal en el posparto vaginal Evaluation and treatment of perineal pain in vaginal postpartum

OBJETIVOS: Identificar a prevalência, intensidade e medidas terapêuticas de alívio da dor perineal, após o parto vaginal. MÉTODOS: Estudo transversal realizado na Unidade de Alojamento Conjunto do Hospital Universitário da Universidade de São Paulo e os dados foram colhidos por entrevista, junto a 303 puérperas que tiveram parto vaginal, com escala numérica (0 a 10) para avaliar a dor perineal, avaliação perineal e dados do prontuário. RESULTADOS: A prevalência da dor perineal foi de 18,5%, com intensidade moderada (51,8%), associada à presença de episiotomia (p=0,001). Houve 303 partos vaginais; 80,5% apresentaram trauma perineal, 75,4% episiotomias e 24,6% lacerações. O analgésico oral foi o método mais utilizado para alívio da dor perineal. CONCLUSÃO: Há diversos tratamentos para o alívio da dor perineal; não há método com completa eficácia para a resolução do problema.<br>OBJETIVOS: Identificar la prevalencia, intensidad y medidas terapéuticas de alivio del dolor perineal en el posparto vaginal. MÉTODOS: Estudio transversal realizado en la Unidad de Alojamiento Conjunto del Hospital Universitario de la Universidad de Sao Paulo; los datos fueron recolectados por medio de entrevista a 303 puérperas que tuvieron parto vaginal (escala numérica de 0 a 10) para evaluar: el dolor perineal, la evaluación perineal y los datos de la ficha médica. RESULTADOS: La prevalencia del dolor perineal fue de 18,5%, con intensidad moderada (51,8%), asociada a la presencia de episiotomía (p=0,001). Hubo 303 partos vaginales; 80,5% presentaron trauma perineal, 75,4% episiotomías y 24,6% laceraciones. El analgésico oral fue el método más utilizado para aliviar el dolor perineal. CONCLUSIÓN: Existen diversos tratamientos para el alivio del dolor perineal y no existen métodos con completa eficacia para la resolución del problema.<br>OBJECTIVES: To identify the prevalence, intensity and therapeutic measures for relief of perineal pain in the vaginal postpartum. METHODS: Cross-sectional study in a University Hospital Rooming Unit of the University of Sao Paulo; data were collected through interviews with 303 postpartum women who delivered vaginally (numeric scale from 0 to 10) to assess: perineal pain, perineal assessment and medical record data. RESULTS: The prevalence of perineal pain was 18.5%, with moderate intensity (51.8%) associated with presence of episiotomy (p = 0.001). There were 303 vaginal deliveries, 80.5% had perineal trauma, episiotomy, 75.4% and 24.6% lacerations. The oral analgesic was the method used to relieve perineal pain. CONCLUSION: There are several treatments for perineal pain relief and there are no effective methods for complete resolution of the problem