Superimposition of Metanarrative through Counter Narrative in Political Tweets of Maryam Nawaz

This study aims at investigating superimposition of metanarrative by counter narratives in the political tweets of Maryam Nawaz. The data was confined to the tweets of Maryam Nawaz over the period of two months (February and March 2018). The thematic framework of Riessman et.al (2008) was used as a theoretical lens to interpret the data. The study reveals that Maryam Nawaz is inclined to develop counter narratives in her communication through Tweets. These narratives eclipse the often claimed, propagated and manipulated metanarrative (declared in the party Manifesto) in the pursuit of her personal interests. The study shows that the counter narratives are perpetuated against rival parties, institutions and sometimes against the party Manifesto to generate conflict and to instigate the followers for protests. The study also reveals that metanarrative of the party was not communicated by Maryam Nawaz even during significant political events. Not speaking of the party manifesto/metanarrative, she has been using the virtual space interaction to counter attack her rival political parties. This study provides a direction to future studies to investigate the role of social media in helping Pakistani politicians for getting their voice out to a larger community mostly a direct appeal to their voters for information, persuasion and mobilization

Higher Education Students' Perceptions of E-Learning Quality

The aim of this study was to evaluate the quality of e-learning provision at Sultan Qaboos University, Oman, from the students’ perspective. The study employed a 48-item survey comprising six domains of e-learning provision. The questionnaire was administered to a sample of 1,858 male and female students across all colleges. The results revealed that the quality of the e-learning provision in four of the domains was at an intermediate level. The results also showed statistically significant differences in the third domain (System Effectiveness) in favor of male students, whereas there were no statistically significant differences in the other domains or the overall score. The findings of the study lead to a set of recommendations that may help to contribute to the dissemination and improvement of e-learning culture in general and its quality in particular, as well as furthering its integration in the educational process

Biotransfomation products from Clarius batrachus oil

Biotransformation can be defined as an application that utilized natural and recombinant organisms’ enzymes secreted by yeast, fungi and bacteria or whole cells as catalyst in synthesis of organic compound. Therefore, Pseudomonas aeruginosa bacterium has been chosen to be the biocatalyst for biotransformation of ω fatty acids extracted from Malaysian catfish, Clarias batrachus emphasizing on bioconversion of arachidonic acid. In addition, arachidonic acid was one of prostaglandin precursor which exerts a variety of pharmacological effects on human and animals. In this study, the fatty acids were extracted from the catfish using modified Folch method where the fish flesh was freeze dried prior to homogenization with the chloroform and methanol system. Then, the crude lipid extract was added to the bacterial culture and incubated for 4 days. After incubation, the biotransformation product was extracted and analyzed by using gas chromatography and mass spectrometer (GC-MS) to identify the fatty acids and other compounds. It was found that several fatty acids, especially ω-fatty acids were converted to cholesterol. This indicates that ω-fatty acids can be used as starting materials for other bioactive metabolites for pharmaceutical purposes

Context-aware convolutional neural network for grading of colorectal cancer histology images

Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224 × 224) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of 1792 × 1792 pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. We evaluated the proposed method on two colorectal cancer datasets for the task of cancer grading. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods. We also presented a comprehensive analysis of different variants of the proposed method

A pilot study on the use of low level laser therapy in treatment of temporomandibular disorder

Temporomandibular disorders (TMDs) is a collective term that embracing a number of clinical problems that involve the masticatory muscles, Temporomandibular Joint (TMJs), and the associated structures. It characterized by facial pain in the area of TMJ and muscle of mastication, restriction and sound during mandibular movement. Recently physical therapy such as Low Level Laser Therapy (LLLT) is used as one of the treatment modalities and it is believed to promote wound healing, tissue repair and induce analgesia. Materials and methods: Convenience sampling was used which consist of 22 volunteered patients, 14 were treated with conventional treatment and 8 were treated with combination of LLLT and conventional therapy. Laser machine used was Waterlase/Biolase © 2007 with irradiation 0.5 W- 30 Hz daily for three consecutive days, then once a week review treatment for two weeks. The space between laser beam and skin is 3 cm, applied as small circles for 2-3 minutes. Pain intensity before and after the treatment was recorded by using numerical rating scale (NRS). Statistical data analysis was conducted using SPSS software. Wilcoxon-sign ranked-test and Mann-Whitney U test were used. Results: Pain intensity was reduced significantly in patients whom treated by combination of LLLT and conventional therapy. (p<0.05). Pain intensity after treatment for female were higher (M=1.20, SD=1.10) than for male (M=0.00, SD=0.00). Younger patients have higher pain intensity than older patients. LLLT is effective to be used as adjunct to the current conventional treatment in relieving pain in TMD

Micro-Net: A unified model for segmentation of various objects in microscopy images

Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms