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

A new accuracy measure based on bounded relative error for time series forecasting

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred

Incorporating Emerging Technologies in the Forensic Analysis of Construction Project Delays

Considering the significant role of the construction industry in the global economy, its continuous adoption of new technological advances is both desirable and inevitable. These advances include Building Information Modelling (BIM) and Artificial Intelligence (AI)/Machine Learning (ML). However, not all sections of the industry currently embrace these developments. Forensic Delay Analysis (FDA) is an activity of specialists in extracting and presenting evidence contractual claims disputes that relate to project delays. Such delays are frequent and expensive, but the FDA process has benefitted little from these new technologies. The paper reports the initial work of a collaborative PhD project funded under the Intensive Industrial Innovation Programme of the European Regional Development Fund. The project explores the integration of BIM and AI/ML technologies within the FDA process. The potential of emerging technologies in different parts of the FDA process is first considered, followed by a systematic literature review (SLR) of published work that might support, refute, or exemplify such contributions. The findings show that BIM and AI/ML offer promising solutions to the current challenges of FDA and opportunities for enhancing the effectiveness of dispute resolution, but further work is needed to test the proposed improvements on real-world project workflows and to collect expert feedback to assess their effectiveness

Quantum size effect on the phonon-induced Zeeman splitting in a GaAs quantum dot with Gaussian and parabolic confining potentials

The Zeeman splitting of the ground and the first excited level of a Gaussian GaAs quantum dot is studied in the presence of electron-longitudinal-optical (LO)-phonon interaction incorporating the spin of the electron and is compared with the case of a parabolic dot. It is shown that the Zeeman splitting is suppressed because of the polaronic interaction and becomes strongly size dependent, but the parabolic confinement overestimates this Zeeman suppression. It is also shown that although the energy levels are split because of the spin-field interaction, the cyclotron frequencies and the Zeeman lines are independent of the electron spin in the dipole transition. © 2008 Elsevier B.V. All rights reserved

Polaronic effects in a Gaussian quantum dot

The problem of an electron interacting with longitudinal-optical (LO) phonons is investigated in an N-dimensional quantum dot with symmetric Gaussian confinement in all directions using the Rayleigh-Schrödinger perturbation theory, a variant of the canonical transformation method of Lee-Low-Pines, and the sophisticated apparatus of the Feynman-Haken path-integral technique for the entire range of the coupling parameters and the results for N = 2 and N = 3 are obtained as special cases. It is shown that the polaronic effects are quite significant for small dots with deep confining potential well and the parabolic potential is only a poor approximation of the Gaussian confinement. The Feynman-Haken path-integral technique in general gives a good upper bound to the ground state energy for all values of the system parameters and therefore is used as a benchmark for comparison between different methods. It is shown that the perturbation theory yields for the ground state polaron self-energy a simple closed-form analytic expression containing only Gamma functions and in the weak-coupling regime it provides the lowest energy because of an efficient partitioning of the Gaussian potential and the subsequent use of a mean-field kind of treatment. The polarization potential, the polaron radius and the number of virtual phonons in the polaron cloud are obtained using the Lee-Low-Pines-Huybrechts method and their variations with respect to different parameters of the system are discussed. © 2007 Elsevier Ltd. All rights reserved