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.     

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.     

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.     

Adaptational changes in cellular fatty acid branching and unsaturation of Aeromonas species as a response to growth temperature and salinity

The effects of growth temperature and salinity on the cellular fatty acids were investigated on Aeromonas caviae, Aeromonas hydrophila and Aeromonas sobria. Under optimal growth conditions, fatty acids patterns were dominated by even-numbered chains C(16:0), C(16:1cis9), C(18:1cis11), C(12:0) and C(14:0). Growth temperature modifications induced, in the three Aeromonas species, important changes in fatty acid (i) unsaturation, (ii) branching and (iii) chain length. An important decrease in the C(18:1cis11) fatty acid content was observed for the three species below 15 degrees C and above 25 degrees C. The evolution of C(18:1cis11) and C(16:0) showed a mirror image for the three Aeromonas species. Low NaCl concentrations did not elicit significant changes in the fatty acids content of the three Aeromonas species. However, for high NaCl concentration in the medium, the growth ability was related to an important decrease of the unsaturated to saturated fatty acids ratio indicating a membrane rigidification. Thermal and salinity adaptations were branched fatty acid-dependent for A. caviae, whereas this phenomenon was less significant for A. hydrophila and A. sobria.     

Electrodeposition of lead dioxide on Fe electrode: application to the degradation of Indigo Carmine dye

Journal of Applied Electrochemistry - The modification of an iron electrode was carried out according to the following two steps. In a first step, a cathodic reduction is performed to form a film...     

The Effect of Ti Addition on Microstructure and Magnetic Properties of Nanocrystalline FeAl40 Alloy Powders Prepared by Mechanical Alloying

Powder Metallurgy and Metal Ceramics - Recent research on nanocrystalline FeAl alloys has shown that these alloys are of high importance due to their promising structural and mechanical properties,...