Development of a water quality index based on a European classification scheme

This study comprised the development of a new index called the ‘universal water quality index (UWQI)’. This index has advantages over pre-existing indices by reflecting the appropriateness of water for specific use, e.g. drinking water supply rather than general supply, and has been developed by studying the supranational standard, i.e. the European Community Standard. Three classification schemes for water quality are proposed for surface water quality assessment. Water quality determinants of the new index are cadmium, cyanide, mercury, selenium, arsenic, fluoride, nitrate-nitrogen, dissolved oxygen, biochemical oxygen demand, total phosphorus, pH and total coliform. The mathematical equations to transform the actual concentration values into quality indices have been formulated. The weighted sum method was proposed to obtain overall index scores based on individual index (sub-index) values. The application of the new index was demonstrated at a sampling station on Tahtali Reservoir in Turkey based on observed water quality data. Results revealed that the overall quality of the surface water falls under the ‘excellent’ class. On the other hand water quality was strongly affected by agricultural and domestic uses. This technique is believed to assist decision makers in reporting the state of the water quality, as well as investigating spatial and temporal changes. It is also useful to determine the level of acceptability for the individual parameter by referring to the concentration ranges defined in the proposed classification scheme

Selection Of A Novel Aptamer Against Vitronectin Using Capillary Electrophoresis And Next Generation Sequencing

Breast cancer (BC) results in ≃40,000 deaths each year in the United States and even among survivors treatment of the disease may have devastating consequences, including increased risk for heart disease and cognitive impairment resulting from the toxic effects of chemotherapy. Aptamer-mediated drug delivery can contribute to improved treatment outcomes through the selective delivery of chemotherapy to BC cells, provided suitable cancer-specific antigens can be identified. We report here the use of capillary electrophoresis in conjunction with next generation sequencing to develop the first vitronectin (VN) binding aptamer (VBA-01; Kd 405 nmol/l, the first aptamer to vitronectin (VN; Kd = 405 nmol/l), a protein that plays an important role in wound healing and that is present at elevated levels in BC tissue and in the blood of BC patients relative to the corresponding nonmalignant tissues. We used VBA-01 to develop DVBA-01, a dimeric aptamer complex, and conjugated doxorubicin (Dox) to DVBA-01 (7:1 ratio) using pH-sensitive, covalent linkages. Dox conjugation enhanced the thermal stability of the complex (60.2 versus 46.5°C) and did not decrease affinity for the VN target. The resulting DVBA-01-Dox complex displayed increased cytotoxicity to MDA-MB-231 BC cells that were cultured on plasticware coated with VN (1.8 × 10⁻⁶mol/l) relative to uncoated plates (2.4 × 10⁻⁶ mol/l), or plates coated with the related protein fibronectin (2.1 × 10⁻⁶ mol/l). The VBA-01 aptamer was evaluated for binding to human BC tissue using immunohistochemistry and displayed tissue specific binding and apparent association with BC cells. In contrast, a monoclonal antibody that preferentially binds to multimeric VN primarily stained extracellular matrix and vessel walls of BC tissue. Our results indicate a strong potential for using VN-targeting aptamers to improve drug delivery to treat BC

Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks