A sensitive and selective electrochemical method for the determination of dopamine (DA) was developed using a 4-(2-Pyridylazo)-Resorcinol (PAR) polymer film modified glassy carbon electrode (GCE). The PAR polymer film modified electrode shows excellent electrocatalytic activity toward the oxidation of DA in a phosphate buffer solution (PBS) (pH 4.0). The linear range of 5.0 Â 10 À6 -3.0 Â 10 À5 M and detection limit of 2.0 Â 10 À7 M were observed. Simultaneous detection of AA, DA and UA has also been demonstrated on the modified electrode. This work provides a simple and easy approach to selective detection of DA in the presence of AA and UA.Keywords: Poly-PAR modified GCE, Electrocatalysis, DA, UA, AA DOI: 10.1002/elan.200603755 DA is an important neurotransmitter molecule of catecholamines. It plays a very important role in the functioning of central nervous, renal, hormonal and cardiovascular systems [1]. Its deficiency will lead to brain disorder such as Parkinsons disease and schizophrenia [2 -4]. UA is the primary end product of purine metabolism. Its abnormal concentration levels will lead to several diseases such as hyperuricemia and gout. Other diseases such as leukemia and pneumonia are also associated with enhanced urate levels [5]. AA is the agent which prevents scurvy and is known to take part in several biological reactions. DA, UA and AA usually coexist in physiological samples. Therefore, the development of voltammetric sensors for detection UA, DA and AA of neurotransmitters in the extracellular fluid of the central nervous system has received much interest during the past few decades.It is generally believed that direct redox reactions of these species at bare electrodes are irreversible and high overpotentials are usually required for their amperometric detections. Moreover, the direct redox reactions of these species take place at very similar potentials and often suffer from a pronounced fouling effect, which results in rather poor selectivity and reproducibility. The ability to determine DA, UA and AA selectively has been a major goal of electroanalytical researches [6]. Various approaches have been attempted to solve the problems encountering in simultaneous determination of DA, UA and AA [7 -16]. For example, Nafion [15], poly(ester sulfonic acid) [16], and poly(4-vinylpyridine) [14] modified electrodes have been proposed to detect DA in the presence of AA and UA.However, the drawbacks of these ion exchange membrane modified electrodes are their memory effect, non-uniform thickness and poor reproducibility arising from the solvent evaporation method used in the film preparation [17]. Polymer-modified electrodes prepared by electropolymerization have received extensive interest in the detection of analytes because of its high selectivity and sensitivity due to the film homogeneity in electrochemical deposition, strong adherence to the electrode surface and chemical stability of the films [18,19]. Fabrication of conducting polymer films is flexible and controllable, and hence it p...
Abstract-Software defect prediction generally builds models from intra-project data. Lack of training data at the early stage of software testing limits the efficiency of prediction in practice. Thereby researchers proposed cross-project defect prediction using the data from other projects. Most previous efforts assumed the cross-project defect data have the same metrics set which means the metrics used and size of metrics set are same in the data of projects. However, in real scenarios, this assumption may not hold. In addition, software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis for deriving a joint feature space for associating crossproject data and propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. Experiments on public heterogeneous datasets from different projects show that our method is more effective, compared to state-of-the-art methods.
Objective To perform conventional, morphological, and T2 mapping compositional MRI imaging to assess the cartilage degeneration and osteoarthritic progression in patients with medial meniscus posterior root tears (MMPRTs) who underwent trans-posterior cruciate ligament (PCL) all-inside repair or partial meniscectomy. Design Patients with MMPRTs after trans-PCL all-inside repair (group AR) or partial meniscectomy (group PM) between 2015 and 2018 were retrospectively identified. Preoperative and postoperative conventional MRI were collected to assess medial meniscus extrusion (MME) and the whole-organ magnetic resonance imaging score (WORMS). Postoperative morphological MRI and T2 mapping compositional MRI were collected to evaluate the quantitative cartilage thickness/volume and cartilage composition. Results The final cohort consisted of 21 patients in group AR and 22 patients in group PM, with no differences in demographic data and baseline patient characteristics between the 2 groups. Group AR demonstrated less progression of articular cartilage wear ( P < 0.05) and decreased meniscal extrusion ( P = 0.008) than group PM at the final follow-up. In addition, group AR demonstrated less extracellular matrix degeneration in the cartilage subregion of the medial compartment ( P < 0.05) than group PM with lower T2 relaxation times in the superficial layer of the articular cartilage. Conclusion Trans-PCL all-inside repair of MMPRTs could delay the initial cartilage deterioration and morphological cartilage degeneration compared with partial meniscectomy. However, the amount of residual meniscal extrusion is clinically important, and an improved root repair fixation method should be investigated.
Cross-project defect prediction trains a prediction model using historical data from source projects and applies the model to target projects. Most previous efforts assumed the cross-project data have the same metrics set, which means the metrics used and the size of metrics set are the same. However, this assumption may not hold in practical scenarios. In addition, software defect datasets have the class-imbalance problem which increases the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis by deriving a joint feature space for associating cross-project data. We also propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. The experimental results show that our method is more effective compared to state-of-the-art methods.
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