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.     

Boosting the activity of FeOOH via integration of ZIF-12 and graphene to efficiently catalyze the oxygen evolution reaction

Transition metal oxyhydroxides have been used as promising electrocatalysts for water splitting however, their catalytic activity is restricted due to low surface area and poor conductivity. Herein, we report novel composite FeOOH@ZIF-12/graphene composite as electrocatalyst for water oxidation, whereby ZIF-12 provide extra surface for the FeOOH dispersion whilst graphene act as excellent electron mediator. The composite shows a low overpotential value of 291 mV to attain a current density of 10 mA cm^?2 and a low Tafel slope value of 78 mV dec^?1. The catalyst offers a maximum current density of 101 mA cm^?2, while it gives a turnover frequency (TOF) value of 0.031 s^?1 at an overpotential of 291 mV only. The excellent activity and remarkable stability of composite is attributed to highly conductive and porous support.     

Characterization of Failure Mechanisms of Duplex and Graded Thermal Barrier Coatings Exposed to Thermal Shock Test

The beginning of failure of a (ZrO2-7%Y2O3)/(Ni-22%Co-17%Cr-12.5%Al-0.6%Y) duplex andgraded coating systems on lnconel 617 and IN738LC in burner rig tests has been characterized.The test conditions are 40 s heating up to 75O℃ substrate temperature followed by 80 s aircooling. Failure is considered at the appearance of the first bright spot during heating period.Stresses due to thermal expansion mismatch strains on cooling are the probable cause of life-limiting in this conditions of testing.     

Sarcoplasmic reticular Ca2+ pump ATPase activity in congestive heart failure due to myocardial infarction

OBJECTIVE: Earlier studies have shown a depression in the sarcoplasmic reticular (SR) Ca2+ uptake and gene expression in Ca2+ pump ATPase protein in congestive heart failure subsequent to myocardial infarction. It is the objective of this study to understand further the mechanisms of depressed SR Ca2+ pump activity in the failing heart. METHODS: Heart failure in rats was induced by occluding the left coronary artery for 16 weeks and the viable left ventricle was processed for the isolation of SR membranes. Sham-operated animals were used as control. The characteristics of SR Ca2+ pump ATPase in the presence of different concentrations of K+, Ca2+ and ATP were examined and the purity of these membranes was monitored by determining the marker enzyme activities. In addition to measuring changes in cyclic adenosine monophosphate (cAMP) protein kinase and Ca(2+)-calmodulin induced phosphorylation, alterations in SR phospholipid composition as well as sulfhydryl (SH) group content were investigated. RESULTS: Ca(2+)-stimulated ATPase activity, unlike Mg(2+)-ATPase activity, was depressed in the left ventricular SR from failing hearts as compared to control. The decrease in Ca(2+)-stimulated ATPase activity was seen at different concentrations of Ca2+, K+ and ATP but no changes in the affinities of the enzyme for Ca2+ and ATP were evident. The SR Ca(2+)-stimulated ATPase activities in the presence of both cAMP-dependent protein kinase and Ca(2+)-calmodulin were markedly decreased in the failing hearts when compared to control preparations. Furthermore, the 32P incorporation in the presence of cAMP-dependent protein kinase or Ca(2+)-calmodulin was also reduced in the experimental heart SR membranes. The phospholipid composition of the SR membranes from the failing heart was markedly altered. No changes in SH-group or the degree of cross contamination with other membranes were apparent in the failing heart SR. CONCLUSIONS: These results suggest that abnormalities in membrane phospholipid composition and phosphorylation of the enzyme may partly explain the observed depression in SR Ca2+ pump ATPase activity in heart failure following myocardial infarction.     

Study of Lateral Spread of Ions Emitted from 2.3 kJ Plasma Focus with Hydrogen and Nitrogen Gases

The characteristics of ion beams of hydrogen and nitrogen with different filling pressures emitted from the plasma focus device of 2.3 kJ energy are investigated. CR-39 SSNTDs are employed for the registration of tracks of ions. The exposed detectors are etched in 6N NaOH solution at 70°C and then examined with an optical microscope. The ion flux is estimated to be of the order of 10^5–6 tracks/cm². The flux with the radial position does not exhibit any regular pattern of variation.     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.     

Effect of Zn and Pb as alloying elements on the electrochemical behavior of brass in NaCl solutions

The electrochemical behavior of brasses with various Zn content (5.5–38 mass%) and brass (Cu–38Zn) with different Pb contents (1–3.4 mass%) in 0.6 M NaCl was investigated. The effects of temperature, immersion time, and concentration of chloride ions on the behavior of the different alloys were studied. The pitting corrosion behavior of Cu–Zn alloys and leaded–brass alloys in 0.6 M NaCl solution was also investigated. Open-circuit potential measurements (OCP), polarization techniques and electrochemical impedance spectroscopy (EIS) were used. The results show that the increase in the Zn content increases the corrosion rate of the brass alloys in chloride solutions, while the increase of Pb content in Cu–38Zn–Pb decreases the corrosion rate of the alloy. Long immersion time of the alloys in the aqueous electrolyte improves their stability due to the formation of passive film on the alloy surface. The breakdown potential is shifted to more negative direction with increasing the Zn content, whereas it shifts towards positive values with increasing Pb content. Equivalent circuit model for the electrode/electrolyte interface under different conditions was proposed to illustrate the electrochemical processes taking place at the interface. The electrochemical behavior of the different alloys was discussed in view of the fitting results.     

A localization algorithm for large scale mobile wireless sensor networks: a learning approach

Localization is a crucial problem in wireless sensor networks and most of the localization algorithms given in the literature are non-adaptive and designed for fixed sensor networks. In this paper, we propose a learning based localization algorithm for mobile wireless sensor networks. By this technique, mobility in the network will be discovered by two crucial methods in the beacons: position and distance checks methods. These two methods help to have accurate localization and constrain communication just when it is necessary. The proposed method localizes the nodes based on connectivity information (hop count), which doesn’t need extra hardware and is cost efficient. The experimental results show that the proposed algorithm is scalable with a small set of beacons in large scale network with a high density of nodes. The given algorithm is fast and free from a pre-deployment requirement. The simulation results show the high performance of the proposed algorithm.     

Speaker recognition utilizing distributed DCT-II based Mel frequency cepstral coefficients and fuzzy vector quantization

In this paper, a new and novel Automatic Speaker Recognition (ASR) system is presented. The new ASR system includes novel feature extraction and vector classification steps utilizing distributed Discrete Cosine Transform (DCT-II) based Mel Frequency Cepstral Coefficients (MFCC) and Fuzzy Vector Quantization (FVQ). The ASR algorithm utilizes an approach based on MFCC to identify dynamic features that are used for Speaker Recognition (SR). A series of experiments were performed utilizing three different feature extraction methods: (1) conventional MFCC; (2) Delta-Delta MFCC (DDMFCC); and (3) DCT-II based DDMFCC. The experiments were then expanded to include four classifiers: (1) FVQ; (2) K-means Vector Quantization (VQ); (3) Linde, Buzo and Gray VQ; and (4) Gaussian Mixed Model (GMM). The combination of DCT-II based MFCC, DMFCC and DDMFCC with FVQ was found to have the lowest Equal Error Rate for the VQ based classifiers. The results found were an improvement over previously reported non-GMM methods and approached the results achieved for the computationally expensive GMM based method. Speaker verification tests carried out highlighted the overall performance improvement for the new ASR system. The National Institute of Standards and Technology Speaker Recognition Evaluation corpora was used to provide speaker source data for the experiments.