Background: The enlarged and globular brain is the most distinctive anatomical feature in human evolution that set us apart from our extinct and extant modern human relatives. In a petite evolutionary time the magnitude of human brain is three fold expanded as compared to our closest living kin chimpanzee. Major episodes of human brain size expansion occurred during the upper Pliocene to early Pleistocene era and yet again in middle Pleistocene epochs. The exact genetic basis of these evolutionary changes that bifurcate the highly cognitive human brain from supposedly lesser cognitive nonhuman hominids brain still remain enigmatic. However, it is presumed that complex and larger human brain emerged by essential changes in genes and noncoding regulatory elements. One approach to comprehending the evolution of human brain is to scrutinize the evolution of genes indispensable for normal brain development. Although brain development is genetically complex process, genes associated with early brain development are the best candidate genes in order to understand the mechanism involved in the evolutionary expansion of human brain size. Primary microcephaly genes were selected as their key role in early brain development and mutations in these genes cause severe reduction in cerebral cortex size that is most notably expanded during recent human history. The brain size of microcephalic patients is similar with the size of Pan troglodyte brain and the very early hominid the gracile australopithecine Australopithecus afarensis (average brain size of Australopithecines is 450 cm3), suggesting that primary microcephaly genes likely to have been evolutionary targets in the enlargement of human brain evolution. In this study, the implications of primary microcephaly genes in the evolutionary enlargement of human brain size has been explored by executing a comprehensive evolutionary analysis on ten newly identified microcephaly genes (WDR62, STIL, CEP135, ZNF335, PHC1, CDK6, SASS6, MFSD2A, CIT, and KIF14) across 48 euthrian species. Subsequently also try to explored what are the mechanisms that associate the evolutionary expansion of human brain size with Parkinson‘s disease by studying the molecular evolution of Parkinson‘s disorder linkedalpha synuclein gene. Results: By employing codon substitutions site models based on maximum likelihood method, signatures of pervasive positive selection were identified in five MCPH genes (KIF14, ZNF335, SASS6, CIT and KIF14). For primates, positive selection was found solely in KIF14. Whereas, in nonprimate placental mammals four genes STIL, ZNF335, SASS6, and CIT have exhibit the signature of adaptive evolution. However, pervasive positive selection has acted in STIL, ZNF335 and KIF14 for placental mammals. This study also identified acceleration in the coding sequences of WDR62 and STIL for human terminal branch both by codon substitutions and frequency based methods. However, acceleration in STIL gene is not significant by codon substitutions based method. Furthermore, the signatures of divergent selection constraints between clades are significant for only two genes STIL and SASS6. In the present study, in an endeavor to elucidate whether and why Parkinson‘s disorder affects solely Homo sapiens. Evolutionary study of Parkinson‘s disease associated α synuclein gene revealed that α synuclein gene has been originated specifically at the root of jawed vertebrates and no evolutionary substitutions was accumulated in the α synuclein amino acid sequence during the last 35 million years of evolution. Furthermore, structural dynamics enlighten that during the course of vertebrate evolutionary history, region of amino terminal domain (32 to 58 amino acids) of α synuclein was continuously evolved at structural level, in spite of high sequence conservation at sequence level. Conclusion: This study concluded that evolutionary enlargement of human brain size during Pliocene-Pleistocene period might have not associated to the human MCPH coding sequences exclusively. The joint human specific changes in coding and noncoding regions of human microcephaly loci might have been conducive to the modification in the function of MCPH genes in humans that likely to be responsible for the human brain evolution during the last two million years. Current study on evolution of α synuclein gene provide that region encompassing 3258 amino acid residues of amino terminal domain is critical for normal cellular function and Parkinson‘s disease pathogenesis.
Penelitian ini dilaksanakan di Program Studi S1 Manajemen UIN Suska Riau dan Program Studi S1 Manajemen UIN Sunan Gunung Djati Bandung , Penelitian ini menggunakan metode Komparatif membandingkan dua gejalan pada objek yang berbeda,dalam penelitian ini jumlah sampel sebesar 96 responden yaitu masing masing 48 sampel adalah mahasiswa program studi S1 Manajemen UIN Suska Riau dan 48 sampel adalah mahasiswa program studi S1 Manajemen UIN Sunan Gunung Djati Bandung, Nilai R2 ( R Square ) Mahasiswa Program Studi S1 Manajemen 0.490 atau 49 % dan Nilai R2 ( R Square ) Mahasiswa Program Studi S1 Manajemen UIN Sunan Gunung Djati Bandung. Saran dalam penelitian ini bahwa seharusnya mahasiswa UIN Suska Riau lebih bijak menggunakan fasilitas internet dan sebaiknya kecanduang internat pada mahasiswa UIN Bandung digunakan untuk meningkatkan motivasi belajar dan untuk kegiatan bisnis seperti bisnis online yang sangat menarik dalam prospek bisnis di era digitalisasi sekarang.
The study addresses the significance of biomedical data to be analyzed by Statistical Community in collaboration with the expertise of personnel in the biomedical field. The data has its own particular constraints and difficulties being privacy-sensitive, heterogeneous and voluminous data. The mathematical understanding of patterns and structures and estimation procedures may be fundamentally different from those of data collected in other fields. For the purpose complicated genomic data of leukemia cancer type of Golub et al (1999) is selected for the study. This dataset comes from a study of gene expression in two types of acute leukemia’s, acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). The training data set consisted of 38 bone marrow samples, 27 of which were taken from ALL patients (19 B-ALL and 8 T-ALL) and 11 of which were taken from AML patients. Each gene expression is the quantitative level of messenger RNA found in the cells. Understanding the genetic underpinnings of disease is important for screening, treatment, drug development, and basic biological insight. Thus exploring genomic data has drawn on mathematical, statistical, and computational methods to discover meaningful genetic relationships from large-scale measurements of genes. Since this is a continuously growing area and is constantly being seeded with new approaches and interpretations. Most of this new material is easily accessible given a familiarity with basic genetics and multivariate statistics. The application of multivariate techniques need a thorough study of the data in hand and the primary objective in the study has been to “let the data speak for itself”. For the proper interpretation of these data, experimental and computational genomics need to have a firm grasp of statistical methodology. An aspect of prime importance, keenly taken into consideration in the 1study. For the multivariate genomic data of leukemia cancer type an initial exploratory data analysis has been performed in the study with the graphical tools of Histograms and Box plots in conjunction with one another. This has exposed that such a data set has a thorough fit for the extreme value distributions, which apart for the study undertaken has not been found in literature for the data type. The fitting of extreme value distributions has opened many new avenues for the data type for the new researchers to work on. Another output of the exploratory data analysis is the application of an appropriate transformation (the classical Box Cox transformation) to deal with the sharp skewness the data, and not relying only on the traditionally used logarithmic transformation. The appropriate data transformation has been another high point in the application of PCA for visualizing clusters present in the data set. Previously PCA and other complicated techniques like SOM and SVM has been applied and new adaptations are continuously being tried on these apart from the traditional clustering methodologies. Here the focus has not been just on the application of multivariate techniques to locate the clusters as predefined by the biological knowledge, rather it is on the methodologically simple yet most appropriate technique to be applied after a thorough look into the interior of the data set. Thus the data set revealed a patterned correlation matrix which in itself explained the number and configuration of clusters. This provided a groundwork for the application of PCA on box cox transformed data using the patterned correlation matrix as the interrelationship matrix. Indeed a comparison has been made with other interrelationship matrices as well. The clear cluster structure presented was, with no any misclassification in the configuration of clusters and exactly coincided with the prior biological knowledge. Therefore as per our hopes this introduction to prototypical methods for 2studying the data and interpreting in the context of biological genomic knowledge has been successful to get started. Addressing the next immediate issue in the study of the biomedical genomic data was finding genes that may be specific for one leukemia type or the cluster. The initial exploratory data analysis exposed certain data values that were of prime biological significance and played statistically significant role in the specification of genes for each cluster defined or the leukemia type. Resultantly a criterion developed from the data set, classifying each gene into its specific single cluster, or two of the three clusters or in all of the three clusters (the common genes).Thus a classified data set of the most variant genes across all the samples was taken as a training data set. Based on the classified grouping a linear discriminant analysis was successfully performed to find the discriminating genes for the specific leukemia type with 99.97% probability of correct classification. The collections of the discriminating genes from the three clusters formed were then needed to be checked for the previously found externally valid cluster structure. PCA was then applied in a new dimension as a check for the discriminating genes. For the discriminating genes the cluster formed for the sample expression profiles were expected to be distinctively clear for the genes to term as a leukemia type specific or cluster specific. Thus the clusters formed were very clearly distinguishable from one and other in contrast to the clusters of the sample expression profiles comprising of the common genes in all. These presented no any distinctive cluster rather a big bulk of a cluster that did not showed any difference in the biologically different leukemia types. The two major issues of the biomedical genomic data have been addressed successfully with an appropriate proposed model for the data type. Thus the study has been based on methodologically simple yet appropriate statistical techniques for such a data type filling 3the inevitable space left in for a statistical community the Pakistani statistical community for the very first time for such a internationally important field, the genomic biomedical field. With the results being unequivocal: Simplest is best! Can cluster genes, cell samples, or both. Yet the study has explored many new dimensions that need to be explored to establish relationship between an experiment based leukemia class and its subclass and a clinical out come. Since the data has many dimensions and concentrating on few precisely has been a difficult task yet accomplished.