A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Loss of substance of the lateral region of the face

The cardiovascular effects of a partially purified extract of fish oil, enriched in the n-3 series fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), were studied in stroke-prone spontaneously hypertensive rats (SHR-SP) fed with high- and low-sodium diets during 5 weeks. Addition of salt to the low-salt control diet at a level commonly found in human food items (6% NaCl of the dry weight of the diet) produced a remarkable rise in blood pressure, an increase in left ventricular weight-to-body weight ratio (LVH-index) and an increase in kidney weight-to-body weight ratio (RH-index). Fish oil (20% of the dry weight of the diet) did not significantly influence the blood pressure or LVH-index or RH-index during the low-salt control diet. However, fish oil completely prevented the remarkable rise in blood pressure and clearly antagonized the rise of both LVH- and RH-indices, induced by the high-salt diet. The fish oil supplementation increased the levels of the polyunsaturated fatty acids of the n-3 series and decreased those of the n-6 series in plasma and kidney, irrespective of the salt content of the diet. Fish oil lowered serum thromboxane B2 concentration by approximately 75%. During the high-salt diet, fish oil markedly decreased water intake and urine volume, and increased urinary sodium concentration by about 60%. Our findings show that, in addition to an antihypertensive effect, fish oil also decreases LVH and RH. These effects appear to be due to an improved ability to excrete sodium and could be explained by the observed changes in the fatty acid composition and metabolism.     

Large-scale linked data integration using probabilistic reasoning and crowdsourcing

We tackle the problems of semiautomatically matching linked data sets and of linking large collections of Web pages to linked data. Our system, ZenCrowd, (1) uses a three-stage blocking technique in order to obtain the best possible instance matches while minimizing both computational complexity and latency, and (2) identifies entities from natural language text using state-of-the-art techniques and automatically connects them to the linked open data cloud. First, we use structured inverted indices to quickly find potential candidate results from entities that have been indexed in our system. Our system then analyzes the candidate matches and refines them whenever deemed necessary using computationally more expensive queries on a graph database. Finally, we resort to human computation by dynamically generating crowdsourcing tasks in case the algorithmic components fail to come up with convincing results. We integrate all results from the inverted indices, from the graph database and from the crowd using a probabilistic framework in order to make sensible decisions about candidate matches and to identify unreliable human workers. In the following, we give an overview of the architecture of our system and describe in detail our novel three-stage blocking technique and our probabilistic decision framework. We also report on a series of experimental results on a standard data set, showing that our system can achieve a 95 % average accuracy on instance matching (as compared to the initial 88 % average accuracy of the purely automatic baseline) while drastically limiting the amount of work performed by the crowd. The experimental evaluation of our system on the entity linking task shows an average relative improvement of 14 % over our best automatic approach.     

Global stabilization of a class of delay discrete-time nonlinear systems via state and output feedback

This paper deals with stabilization of a class of delay discrete-time nonlinear systems through state and output feedback. We provide an explicit bounded state feedback law as an extension of the Jurdjevic-Quinn method, from nonlinear theory, to this class of systems. Next, we present a useful and systematic approach to design an observer for the same class of systems. Then, we show how the global stabilization problem via dynamic output feedback can be solved by using the two previous results. Finally, numerical examples are given to illustrate the effectiveness of the proposed design method.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Averaged n-Dependence Estimators (AnDE) is an approach to probabilistic classification learning that learns by extrapolation from marginal to full-multivariate probability distributions. It utilizes a single parameter that transforms the approach between a low-variance high-bias learner (Naive Bayes) and a high-variance low-bias learner with Bayes optimal asymptotic error. It extends the underlying strategy of Averaged One-Dependence Estimators (AODE), which relaxes the Naive Bayes independence assumption while retaining many of Naive Bayes’ desirable computational and theoretical properties. AnDE further relaxes the independence assumption by generalizing AODE to higher-levels of dependence. Extensive experimental evaluation shows that the bias-variance trade-off for Averaged 2-Dependence Estimators results in strong predictive accuracy over a wide range of data sets. It has training time linear with respect to the number of examples, learns in a single pass through the training data, supports incremental learning, handles directly missing values, and is robust in the face of noise. Beyond the practical utility of its lower-dimensional variants, AnDE is of interest in that it demonstrates that it is possible to create low-bias high-variance generative learners and suggests strategies for developing even more powerful classifiers.     

Detection of pathogenic Escherichia coli strains in groundwater in the Yaoundé region (Cameroon,Central Africa)

This study was aimed at assessing the prevalence of pathogenic Escherichia coli strains, relative to the total count of E. coli, faecal coliforms and other heterotrophic mesophilic aerobic bacteria (HMAB) isolated in groundwater in the equatorial region of Cameroon (Central Africa). Bacteria were isolated using standard methods. Pathogenic E. coli strains were then identified using haemagglutination and antisera tests. The maximum abundance of HMAB, faecal coliforms and E. coli strains were 4.9 × 10⁶, 5.6 × 10³ and 1 × 10³ colony‐forming units (CFU)/100 mL, respectively. The count of pathogenic E. coli strains reached 3 CFU/100 mL. The counts of commensal and pathogenic E. coli strains underwent temporal and spatial fluctuations. In 21% of sampling sites, the abundance of faecal coliforms was significantly correlated to that of E. coli (P < 0.05). However, the isolated bacterial count was not significantly correlated to that of the pathogenic E. coli strains (P > 0.05). The bacteria abundance dynamics may be impacted by many interacting factors.     

Combined microwave and enzymatic treatments for ��-lactoglobulin and bovine whey proteins and their effect on the IgE immunoreactivity

Effect of combined microwave (MW) and enzymatic hydrolysis on the human immunoglobulin E (IgE)-binding properties of ??-lactoglobulin (??-lg) and other whey proteins (WP) was investigated. Separated ??-lg and full whey protein isolate (WPI) were hydrolyzed with trypsin, chymotrypsin, mixture of trypsin/chymotrypsin, and pepsin at three microwave power levels: 50?W during 1 and 5?min, 100 and 200?W during 1 and 3?min. The immunoreactivity of the obtained hydrolysates resulting from combined microwave protease treatment was assessed using sera of young patients allergic to bovine whey proteins. The application of microwave treatment at 200?W enhances the hydrolysis of ??-lg by pepsin in 3?min and decreases significantly its immunoreactivity. The extensive hydrolysis of the microwave-treated ??-lg and WPI with trypsin, chymotrypsin, and the mixture of trypsin with chymotrypsin did not have an impact on the IgE binding of the products obtained in all the studied conditions.     

Vancomycin dimerization and chiral recognition studied by high-performance liquid chromatography

The retention and separation of D,L-dansylvaline enantiomers (used as test solutes) were investigated using silica gel as stationary phase and vancomycin as chiral mobile-phase additive. A retention model was developed to describe the mechanistic aspects of the interaction between solute and vancomycin in the chromatographic system. It considered the formation of vancomycin dimers both "free" in the mobile phase and adsorbed on silica. By fitting the model equation to experimental data, it appeared clearly that the approach taking into account the vancomycin dimerization described accurately the retention behavior of the compounds. The examination of the model equation parameters showed that the glycopeptide dimerization increased the enantioselectivity by a factor of approximately 3.7. This study demonstrated the preponderant role of the vancomycin dimerization on the chiral recognition process of D,L-dansylvaline. Also, an additional analysis on a vancomycin chiral stationary phase indicated that the addition of vancomycin in the mobile phase promoted a greater enantioselectivity mediated by the formation of dimers in the stationary phase.