Experimental and computational applications of microarray technology for malaria eradication in Africa

Various mutation assisted drug resistance evolved in Plasmodium falciparum strains and insecticide resistance to female Anopheles mosquito account for major biomedical catastrophes standing against all efforts to eradicate malaria in Sub-Saharan Africa. Malaria is endemic in more than 100 countries and by far the most costly disease in terms of human health causing major losses among many African nations including Nigeria. The fight against malaria is failing and DNA microarray analysis need to keep up the pace in order to unravel the evolving parasite’s gene expression profile which is a pointer to monitoring the genes involved in malaria’s infective metabolic pathway. Huge data is generated and biologists have the challenge of extracting useful information from volumes of microarray data. Expression levels for tens of thousands of genes can be simultaneously measured in a single hybridization experiment and are collectively called a “gene expression profile”. Gene expression profiles can also be used in studying various state of malaria development in which expression profiles of different disease states at different time points are collected and compared to each other to establish a classifying scheme for purposes such as diagnosis and treatments with adequate drugs. This paper examines microarray technology and its application as supported by appropriate software tools from experimental set-up to the level of data analysis. An assessment of the level of microarray technology in Africa, its availability and techniques required for malaria eradication and effective healthcare in Nigeria and Africa in general were also underscored

From Wooden to Digital Notice Board (DNB): Design and Implementation for University Administration

Many state-of-the-art and cutting-edge universities in the world rely on wooden notice board hanging on a wall to display announcements. The overreliance of this practice in a university is still not enough to pass relevant information round as some of these problems are encountered: (1) Multiple people struggle and cluster a single wooden notice board for information just released, e.g. Exam results. (2) People mutilate, remove or destroy paper notices from the notice board leaving other people to be uninformed.(3) Some people do not have the time to settle down and read all the relevant information posted on a notice board especially where digital printout cannot be made easily. (4) There is unregulated display of information, difficulty in storage and no efficient reference to past relevant information being posted. Taking a case study of Covenant University where information is a vital key for everybody’s progress on campus. This research work looks at the development of a Digital Notice Board (DNB) program, which can be installed on various computers connected together by local area network (LAN) so as to increase the rate at which relevant information is being disseminated to the public within a certain location. The major strength of DNB is its usability with large electronic screen for display of information on numerous strategic points on university campus

An Empirical Analysis of the Impact of Globalisation on Performance of Nigerian Commercial Banks in Post-Consolidation Period

The study examined the impact of globalization on performance of Nigerian commercial banks between 2005 and 2010. It specifically determined the effects of policies of foreign private investment, foreign trade and exchange rate on performance of Nigerian banks. The study utilized panel data econometrics in a pooled regression, where time series and cross-sectional observations were combined and estimated. The results of econometric panel regression analysis confirmed that globalization, i.e. foreign private investment, foreign trade and exchange rate have positive effects on the profit after tax of banks but the magnitude of such effects remains indeterminable because we discovered that there are variations in the data for performance of banks understudied. Based on these findings, the study recommends that banks in Nigeria should not relent in their interaction with their foreign counterpart in doing business in order to increase their foreign earnings. Banks should also spend more on information and communication technology since this has the capacity of increasing their profit. This information technology (IT) should be used to localize all the branches to a single branch networking. In spending more on information and communication, they should make use of satellite communication and very small aperture terminal (VSAT) technology and internet banking VSAT technology apart from making possible voice and video banking. We further recommend that more banks should be opened in foreign countries in order to increase foreign participation in home country’s banking

Clustering Plasmodium falciparum Genes to their Functional Roles Using k-means

We developed recently a new and novel Metric Matrics k-means (MMk-means) clustering algorithm to cluster genes to their functional roles with a view of obtaining further knowledge on many P. falciparum genes. To further pursue this aim, in this study, we compare three different k-means algorithms (including MMk-means) results from an in-vitro microarray data (Le Roch et al., Science, 2003) with the classification from an in-vivo microarray data (Daily et al., Nature, 2007) in other to perform a comparative functional classification of P. falciparum genes and further validate the effectiveness of our MMk-means algorithm. Results from this study indicate that the resulting distribution of the comparison of the three algorithms’ in vitro clusters against the in vivo clusters are similar thereby authenticating our MMk-means method and its effectiveness. However, Daily et al. claim that the physiological state (the environmental stress response) of P. falciparum in selected malaria-infected patients observed in one of their clusters can not be found in any in-vitro clusters is not true as our analysis reveal many in-vitro clusters representation in this cluster

Engineering a Ruled-Based Software Solution for Credit Rating and Worthiness Assessment in Financial Operations

In loan provision, the central worry is whether the borrower will default or payback. A good number of institutions world-wide have gone into distress owing to bad debt arising from inability to recover borrowed funds. Credit Rating is a technique that is widely used to evaluate applications tendered for credit, identify prospective borrowers, and manage existing credit accounts. This work is aimed at the development of a system capable of evaluating the credit worthiness of fund-seeking bank customers and other borrowers towards repayment capabilities of loan facility availed to them in due time. The method carefully examines who qualifies for a loan based on certain rules consisting of Payment History, Credit Owed, Credit Available, Age of Account, Crime Records, Medical Records, Amount to be borrowed. and other factors. Percentage weights for assessment of each of these factors were proposed including threshold percentage above which credit is predicted adequate to be given. This factor creates a sort of satisfaction and level-playing field for correct assessment of lending risk

Reducing the Time Requirement of k-Means Algorithm

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in ddimensional space Rd and an integer k. The problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARIHA). We found that when k is close to d, the quality is good (ARIHA.0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARIHA.0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data