In Defence of Absolutes: The Evolution of Aphra Behns Political Views

The evolution of Aphra Behns political views is a point of scholarly contention. The analysis of her dramatic works starts with her early tragicomedies, like The Young King and The Amorous Prince, and continues through her well- known Exclusion Crisiss sexual comedies, like The Roundheads and Sir Patient Fancy. This paper argues that Behns on- stage royalism was considerably diverse, reflecting various degrees of support for the monarchy. Behn altered her political positions in response to the development of the fierce rivalry between different political parties. Following her concerns and discontent about the kings ability to rule the country in her early plays, Behn developed a remarkable tendency for supporting Charles II and created an image of an impeccable king beyond any criticism. The results suggest that Behn’s Toryism did not reflect an unwavering and unchanging support for the newly restored monarchy, as was assumed previously

Extremely High Frequency Resolution and Low Harmonic Distortion Digital Look-Up-Table Sinusoidal Oscillators

New techniques to efficiently increase the frequency resolution of digital sinusoidal oscillators based on look-up-table (LUT) methods are proposed. The increase in frequency resolution is achieved while maintaining very low level of spurious harmonic distortion. The proposed techniques increase the LUT length to a level at which the spurious harmonic distortion is negligible. The first proposed technique is based on partitioning the address register into three sets and dividing the available LUT length into three smaller tables addressed according to the content of the address register sets. The second proposed technique utilizes one LUT and interpolates the values of the samples that are not stored in the table. The third proposed technique is similar to the first technique with the advantage of simpler implementation and lower levels of spurious harmonic distortion. The proposed techniques are simulated and their performance is compared with that of the direct LUT and trigonometric interpolation methods. The simulation results show that the proposed techniques are superior to both direct LUT and trigonometric interpolation methods

Protein contact map prediction using multi-stage hybrid intelligence inference systems

AbstractProteins are one of the most important molecules in organisms. Protein function can be inferred from its 3D structure. The gap between the number of discovered protein sequences and the number of structures determined by the experimental methods is increasing. Accurate prediction of protein contact map is an important step toward the reconstruction of the protein’s 3D structure. In spite of continuous progress in developing contact map predictors, highly accurate prediction is still unresolved problem. In this paper, we introduce a new predictor, JUSTcon, which consists of multiple parallel stages that are based on adaptive neuro-fuzzy inference System (ANFIS) and K nearest neighbors (KNNs) classifier. A smart filtering operation is performed on the final outputs to ensure normal connectivity behaviors of amino acids pairs. The window size of the filter is selected by a simple expert system. The dataset was divided into testing dataset of 50 proteins and training dataset of 450 proteins. The system produced an average accuracy of 45.2% for the sequence separation of six amino acids. In addition, JUSTcon outperformed SVMcon and PROFcon predictors in the cases of large separation distances. JUSTcon produced an average accuracy of 15% for the sequence separation of 24 amino acids after applying it on CASP9 targets

Parallel Implementation of Systolic Array Design for Developing Medical Image Rotation

Many image-processing algorithms are particularly suited to parallel computing, as they process images that are difficult and time consuming to analyse. In particular, medical images of tissues tend to be very complex with great irregularity and variability in shapes. Furthermore, existing algorithms contain explicit parallelism, which can be efficiently exploited by processing arrays. A good example of an image processing operation is the geometric rotation of a rectangular bitmap. This paper presents a set of systolic array designs for implementing the geometric rotation algorithms of images on VLSI processing arrays. The examined algorithm performs a trigonometric transformation on each pixel in an image.  The design is implemented as a distributed computing system of networked computers using Parallel Virtual Machine (PVM) model. Each node (computer) in the network takes part in the task in hand – such as image processing – using message passing. Comments and conclusions about the implementation of the design as a distributed computing system are discussed. Keywords: parallel computing, distributed computing. PVM, image rotation, systolic array

Evaluation of the remineralisation of enamel by different formulations and concentrations of fluoride toothpastes in vitro

Aims: To investigate the remineralising potential of toothpastes with different formulations of fluoride (F): amine fluoride (AmF), sodium monofluorophosphate (MFP), sodium fluoride (NaF) and stannous fluoride (SnF) on artificial subsurface caries lesions in vitro. A secondary aim was to investigate the remineralising potential of toothpastes containing sodium fluoride (NaF) formulation at different F concentrations (500, 1000, 1450, 2800 and 5000 ppm F) on artificial subsurface caries lesions in vitro. Materials and methods: Bovine enamel slabs were subjected to a pH cycling model after 2 weeks of immersion in a demineralisation buffer, to produce subsurface enamel lesions. The pH cycling regime ran for 28 days. Enamel subsurface lesion images were taken using a Quantitative Light-Induced Fluorescence (QLF) system under controlled conditions at baseline and endpoint of the experiment. All fluorescence images were examined with analysing software (QA2 version 1.16; Inspektor Research Systems). Results: For the different F compounds, significant (p < 0.05) remineralising potential was observed for the NaF, SnF and MFP groups in descending order. Lesion remineralisation for the AmF and F-free groups was not significant. As for the different fluoride concentrations, all fluoride concentrations showed significant (p < 0.05) remineralisation potential when compared to the 0 ppm F control group, but no significance was found between groups. Conclusions: From the results of phase A of this in vitro study, it was concluded that: A statistically significant remineralisation of enamel subsurface lesions in comparison with the baseline was found in all groups except the AmF group. Furthermore, NaF toothpaste had the highest remineralising potential on artificial subsurface carious lesions in vitro, followed by SnF then MFP, while AmF was less than the F-free toothpaste. The results of phase B of this in vitro study, concluded that: A statistically significant remineralisation of enamel subsurface lesions in comparison with the baseline was found in all groups. However, there was no difference in the effect of toothpastes with sodium fluoride (NaF) formulation and different concentrations (500, 1000, 1450, 2800, and 5000 ppm F) on remineralisation of artificial subsurface carious lesions in vitro, and no apparent dose response was present related to the concentration of fluoride

Image Content Analysis Using Neural Networks and Genetic Algorithms

The analysis of digital images for content discovery is a process of identifying and classifying patterns and sub-images that can lead to recognizing contents of the processed image. The image content analysis system presented in this paper aims to provide the machine with the capability to simulate in some sense, a similar capability in human beings. The developed system consists of three levels. In the low level, image clustering is performed to extract features of the input data and to reduce dimensionality of the feature space. Classification of the scene images are carried out using a single layer neural network, trained through Kohonen's self-organizing algorithm, with conscience function, to produce a set of equi-probable weights vector. The intermediate level consists of two parts. In the first part an image is partitioned into homogeneous regions with respect to the connectivity property between pixels, which is an important concept used in establishing boundaries of objects and component regions in an image. For each component, connected components can be determined by a process of component labeling. In the second part, feature extraction process is performed to capture significant properties of objects present in the image. In the high level; extracted features and relations of each region in the image are matched against the stored object models using the genetic algorithm approach. The implemented system is used in the analysis and recognition of colored images that represent natural scenes. Keywords: genetic algorithms, neural networks, image segmentation, clustering, image content analysis

Metastatic Invasive Lobular Carcinoma of the Breast Masquerading as a Primary Renal Malignancy

Breast cancer is known to metastasise to different organs in the body, but an initial presentation of breast cancer with loin pain secondary to a metastatic renal mass is extremely rare. We report a 58-year-old woman who presented with recurrent left loin pain due to a metastatic deposit of invasive lobular carcinoma of the breast. The detection of a renal mass on computed tomography led to the assumption of a renal pelvic malignancy. The diagnostic dilemma posed by the detection of a breast mass during staging and the usefulness of immunohistochemistry in the confirmation of diagnosis are discussed