Short Synthesis and Biological Evaluation of 5-chloro-7-benzoyl 2,3-dihydrobenzo[b]furan -3-carboxylic Acid(brl-37959) and Its Analogs.

The synthesis of BRL-37959 has previously been reported. As an NSAID, the compound was tested for and found that it had very low gastric irritancy. Commercially available NSAIDs are non-selective COX inhibitors that enhance the risk of gastric, duodenal mucosal injury or erosions and ulcer problems as well as lead to nephrotoxicity. The COX-2 selective inhibitor Celecoxib (Celebrex) increases the risk of serious cardiovascular (CV) thrombotic events, myocardial infarction and stroke as well as increased nephrotoxicity. COX inhibitors are active against inflammation, pain, fever and different types of cancers. Considering the enormous potential benefits and side effects of non-selective COX inhibitors and selective COX-2 inhibitor. Our research goal was to find a COX inhibitor that binds with both COX-1 and COX-2 in such a ratio that it would be a safer drug. Another aim was to develop a synthesis method of the target inhibitor that will be simple, cost-effective and ensure high yield. Benzofuran is an important building block of BRL- 37959. In Dr. Hossain\u27s lab, Matt Dudley had developed a novel, unprecedented, one pot procedure to synthesize various benzofuran derivatives. We have followed the efficient, large scale potential, high yield and simple process to synthesize benzofuran. Based on the synthesis of benzofuran, we have developed a short and more cost-effective procedure for the synthesis of BRL-37959. The older method was tedious and resulted in very low yields (≤ 5%). This new method is simple and uses inexpensive starting materials, as well as giving high overall yields (62%). In enzyme screening, we found that BRL-37959 selectively binds with COX-1. We followed our improved method and successfully made some analogs with higher yield

Impacts of ICT Integration in the Higher Education Classrooms: Bangladesh Perspective

For the last few years, ICT integration in education has been the topic of discussion for researchers. Very few researches have been conducted on ICT integration in the context of higher education, especially in Bangladesh. The purpose of this study is to explore the ICT integration in higher education teaching - learning in Bangladesh. This study is qualitative in design. Data were collected from students and teachers in the University of Dhaka through semi-structured interview schedule, focused group discussion and classroom observation schedule. The major findings of this study reveal that ICT is not integrated effectively in higher education teaching-learning. Furthermore, several obstacles have been identified that impede the effective integration of ICT. The obstacles are teachers’ lack of knowledge and skills, teachers’ lack of time to take preparations for class, lack of adequate equipment and access to internet and inadequate technical support. It is asserted that proper teachers’ training about integrating ICT in education will be able to change the scenario to a great extent. This study has, therefore, implications for policy developers, teachers and students of various departments. Keywords: ICT, Pedagogy, Social Interaction, Technology, Teaching – learning

Computing Large-scale Distance Matrices on GPU

Abstract-A distance matrix is simply an n×n two-dimensional array that contains pairwise distances of a set of n points in a metric space. It has a wide range of usage in several fields of scientific research e.g., data clustering, machine learning, pattern recognition, image analysis, information retrieval, signal processing, bioinformatics etc. However, as the size of n increases, the computation of distance matrix becomes very slow or incomputable on traditional general purpose computers. In this paper, we propose an inexpensive and scalable data-parallel solution to this problem by dividing the computational tasks and data on GPUs. We demonstrate the performance of our method on a set of real-world biological networks constructed from a renowned breast cancer study

An effective hotel recommendation system through processing heterogeneous data

Recommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists routinely rely on textual reviews, numerical ratings, and points of interest to select hotels in cities worldwide. To attract more customers, online hotel booking systems typically rank their hotels based on the recommendations from their customers. In this paper, we present a framework that can rank hotels by analyzing hotels’ customer reviews and nearby amenities. In addition, a framework is presented that combines the scores generated from user reviews and surrounding facilities. We perform experiments using datasets from online hotel booking platforms such as TripAdvisor and Booking to evaluate the effectiveness and applicability of the proposed framework. We first store the keywords extracted from reviews and assign weights to each considered unigram and bigram keywords and, then, we give a numerical score to each considered keyword. Finally, our proposed system aggregates the scores generated from the reviews and surrounding environments from different categories of the facilities. Experimental results confirm the effectiveness of the proposed recommendation framework

An integrated, fast and scalable approach for large-scale biological network analysis

Research Doctorate - Computer ScienceTHE amount of data in our world has been exploding. Computer-based methods used to analyze data ten years ago are impractical today, as the continuously evolving data acquiring technologies are producing more raw data than these methods can handle. For instance, today’s high throughput technologies like DNA microarrays can produce millions of data elements from a particular experiment, where most of the relevant analysis tools are designed to work with only a few tens of thousands. Even though the scalability of these methods/tools may be improved by porting the relevant implementations to a highly expensive super-computer or a cluster of computers, their existing fully connected data representation model can still pose many other restrictions. In this work, instead of using the traditional distance matrix based microarray data analysis model, we propose to use a novel, fast and scalable κ-Nearest Neighbor (κNN) graph-based approach. Moreover, instead of constructing the graph/network on a highly expensive system, we show its construction on graphics processing units (GPUs), which are now widely available as inexpensive, highly parallel devices. The outcome of our κNN graph construction method (termed as GPU-FS-κNN) can be used to carry out many other important computational tasks. In particular, we demonstrate its applications in two popular data analysis methods: clustering and centrality analysis. To do this, we first propose a GPU-based fast method for constructing minimum spanning trees (MST) from the κNN graphs (termed as κNN-Borůvka) and a method for partitioning the trees in an agglomerative fashion (termed as κNN-Borůvka-Agglomerative). Then, we demonstrate the use of κNN graphs in accelerating and scaling the computations of two degree-based (e.g., degree and eigenvectors) and three shortest path based (closeness, eccentricity and betweenness) centrality metrics. At the end, we integrate the developed methods and combinedly apply them on two publicly available gene-expression data sets (Alzheimer’s disease and breast cancer) and their large-scale artificial expansions. Our investigations show that the proposed integrated approach can find both numerically and biologically significant results. We also demonstrate the method’s application in extracting a robust set of gene markers that may warrant further investigations, due to their conspicuous positions in our results

A GPU-based method for computing eigenvector centrality of gene-expression networks

In this paper, we present a fast and scalable method for computing eigenvector centrality using graphics processing units (GPUs). The method is designed to compute the centrality on gene-expression networks, where the network is pre-constructed in the form of kNN graphs from DNA microarray data sets