Importance of Diet, Genetic and Lifestyle on Human Aging, Health and Cognition | Book Publisher International

 Several intrinsic/extrinsic factors will interfere in our body, deciding human health, lifespan, and cognitive impairment, from conception to old age. It has become clear in recent years that the ageing process is not only a matter of years of life, but also a result of a number of biological processes involving multicausal factors correlated with age. To a certain degree, it is possible to regulate extrinsic factors, such as diet, environmental conditions, and lifestyle, differently from intrinsic factors such as genetic characteristics inherited. The aim of the book "Factors on Aging, Memory and Cognitive Decline" is to explain the effect of these various factors, which can either contribute to metabolic disorders and metabolic diseases or enhance metabolic equilibrium. A conceptualization of how these factors naturally interact is discussed in the first chapter, while the mechanisms responsible for cell and tissue degradation in human diseases and ageing are defined in Chapter 2. Chapters 3 to 6 identify the interaction of nutrient and non-nutrient food components and metabolites in general metabolism, health, memory and cognition, either improving or worsening the metabolic balance. Relevant foods and specific dietary habits that can lead to the preservation of metabolic equilibrium and cognitive function, retardation of normal ageing and related processes are discussed in Chapters 7 to 10. With most dietary profiles and patterns suggesting deficiency in some basic nutrients and over intake of unhealthy dietary components, Chapters 11 and 12 address the key issues prevailing. This up-to-date research collection extends the awareness and highlights the interrelationship between many sciences, such as food, chemistry, biochemistry, nutritional, medicinal, and pharmaceutical sciences, in a multidisciplinary approach. Some ideas are provided as potential perspectives on research in these fields.

Author(s) Details

Valdemiro Carlos Sgarbieri
Faculty of Food Engineering, State University of Campinas, Campinas, São Paulo, Brazil.

Maria Teresa Bertoldo Pacheco
Institute of Food Technology (ITAL), Campinas, São Paulo, Brazil.

Nádia Fátima Gibrin
"Universidade Paulista” (UNIP), Campinas, São Paulo, S.P., Brazil.

Denise Aparecida Gonçalves de Oliveira
“Universidade Paulista” (UNIP), Campus Jundiaí, S.P., Brazil.

Maria Elisa Caetano Silva
Institute of Food Technology (ITAL), Campinas, São Paulo, Brazil.

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Research on Tanimoto Coefficient Similarity Based Mean Shift Gentle Adaptive Boosted Clustering for Genomic Predictive Pattern Analytics | Chapter 5 | Recent Studies in Mathematics and Computer Science Vol. 2

Gene expression data clustering is a significant problem to be resolved as it provides functional relationships of genes in a biological process. Finding co-expressed groups of genes is a challenging problem. To identify interesting patterns from the given gene expression data set, a Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering (TCS-MSGABC) Model is proposed. TCS-MSGABC model comprises two processes namely feature selection and clustering. In first process, Tanimoto Coefficient Similarity Measurement based Feature selection (TCSM-FS) is introduced to identify relevant gene features based on the similarity value for performing the genomic expression clustering. Tanimoto Coefficient Similarity Value ranges from ‘ ’ to ‘ ’ where ‘ ’ is highest similarity. The gene feature with higher similarity value is taken to perform clustering process. After feature selection, Mean Shift Gentle Adaptive Boosted Clustering (MSGABC) algorithm is carried out in TCS-MSGABC model to cluster the similar gene expression data based on the selected features. The MSGABC algorithm is a boosting method for combining the many weak clustering results into one strong learner. By this way, the similar gene expression data are clustered with higher accuracy with minimal time. Experimental evaluation of TCS-MSGABC model is carried out on factors such as clustering accuracy, clustering time and error rate with respect to number of gene data. The experimental results show that the TCS-MSGABC model is able to increases the clustering accuracy and also minimizes clustering time of genomic predictive pattern analytics as compared to state-of-the-art works.

Author(s) Details

Marrynal S. Eastaff 
Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, India.

V. Saravaan
Department of IT, Hindusthan College of Arts and Science, Coimbatore, India.

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