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GAPDH is a conserved enzyme that binds diverse proteins, such as Siah during apoptotic nuclear translocation. There is one somatic GAPDH gene, but over 60 pseudogenes, the expression of which is nebulous. A single nucleotide polymorphism (SNP) in the GAPDHP44 pseudogene exhibits a beneficial allele in AD. The objective of this study was to examine the P44 gene and to propose a mechanism for the putative protein and its impact on AD. We examined the sequences in the putative coding region of the human GAPDHP44 gene and the upstream genetic elements usinga bioinformatics approach. We compared the amino acid sequences of the putative gene product with that of the parent GAPDH protein. There is a TATA box 24 nt upstream from, and a Kozak sequence at, putative transcription and translation start sites, respecttively. The upstream region also has sequences (7 - 16 nt) paralogous to those in parent gene introns; one shows homology to a known enhancer element. The resulting protein would contain 139 aa due to a stop codon, roughly the same size as the dinucleotide domain (151 aa) of the parent protein. The SNP is in a region (residues 80 - 120) that binds to the protein GOSPEL. We propose that the beneficial SNP may cause a glutamine to glutamate substitution. NMDA-stmulated neurons undergo GAPDH nitrosylation, Siah translocation, but can be rescued by GOSPEL binding to GAPDH. Our model suggests that the putative P44 protein may regulate GAPDH-GO-SPEL interaction and the beneficial SNPmay ameliorate AD.
Humanity suffers an ever-present threat of crises. In the event of a crisis, the population in affected areas will be in danger and will need to be evacuated to a safer in order to protect their lives. One of the difficulties in emergency management is quickly and accurately selecting suitably safe areas of refuge. This paper aims to explain an evacuation shelter selection process that uses rough set theory and a geographical information system (GIS). The proposed approach uses rough set theory concepts to classify shelters and selects suitable shelters on the basis of three factors: distance, capacity, and the availability of life requirements. The preparation of data and reporting of results are performed via the GIS environment. The proposed approach was implemented using Masoura,Egypt, as a case study and the re- sults of this implementation are presented.