Using Mobile Technologies for Enhancing Student Academic Experience: University of Jordan Case Study

This paper presents an approach to enhance students’ engagement with their university, as well as assisting them in understanding their current academic state by using mobile technologies. This approach simplifies the presentation, awareness of university rules and regulation in regards to graduation requirements, in credit hour system, through the development of a friendly mobile environment called UniApp. A test case is presented of an interactive mobile learning (m-learning) environment in higher education institutions that adhere to university rules and regulations. Normally, students login frequently to their university account in order to use some of the provided facilities, such as marks and registered modules. However, students need to be aware of what they are studying and what learning outcome they need to achieve. They also need to be aware of how this can benefit them in completing their major, as well as having an enjoyable learning experienc

Genetic signatures for a rodent model of Parkinson's disease using combinatorial optimization methods

This chapter illustrates the use of the combinatorial optimization models presented in Chapter 19 for the Feature Set selection and Gene Ordering problems to find genetic signatures for diseases using microarray data. We demonstrate the quality of this approach by using a microarray dataset from a mouse model of Parkinson's disease. The results are accompanied by a description of the currently known molecular functions and biological processes of the genes in the signatures

Genetic biomarkers for brain hemisphere differentiation in Parkinson's Disease

This work presents a study on the genetic profile of the left and right hemispheres of the brain of a mouse model of Parkinson's disease (PO). The goal is to characterize, in a genetic basis, PO as a disease that affects these two brain regions in different ways. Using the same whole-genome microarray expression data introduced by Brown et al., we could find significant differences in the expression of some key genes, well-known to be involved in the mechanisms of dopamine production control and PD. The problem of selecting such genes was modeled as the MIN (a,/3)-FEATURE SET problem; a similar approach to that employed previously to find biomarkers for different types of cancer using gene expression microarray data. The Feature Selection method produced a series of genetic signatures for PO, with distinct expression profiles in the Parkinson's model and control mice experiments. In addition, a close examination of the genes composing those signatures shows that many of them belong to genetic pathways or have ontology annotations considered to be involved in the onset and development of PD. Such elements could provide new clues on which mechanisms are implicated in hemisphere differentiation in PD

Investigating the change of the hierarchical pattern of gene expression in the normal and Parkinson's brain using a combinatorial optimization based unsupervised clustering method

Previous works on Parkinson's disease (PD) mainly focused on genes differentially expressed between the anterior and the posterior sections of the brains of a normal mouse and the one with PD. However, no work has been done in finding a hierarchical pattern of gene expression between the different regions of a brain. Such a hierarchy is useful to locate genetic specializations within a normal brain, thus in analyzing how brain infirmities affect these specializations. We use a recently proposed method of robust hierarchical clustering using arithmeticharmonic cut to construct the hierarchical relation between different regions of the brain. Then, we show how similar regions of the normal and PD brain differ in gene expressions, indicating a functional variation due to Parkinson's disease in a few high-level clusters of brain regions

Genes related with Alzheimer's disease: A comparison of evolutionary search, statistical and integer programming approaches

Three different methodologies have been applied to microarray data from brains of Alzheimer diagnosed patients and healthy patients taken as control. A clear pattern of differential gene expression results which can be regarded as a molecular signature of the disease. The results show the complementarity of the different methodologies, suggesting that a unified approach may help to uncover complex genetic risk factors not currently discovered with a single method. We also compare the set of genes in these differential patterns with those already reported in the literature