Hematopoietic mosaic chromosomal alterations in the New England Centenarian Study

Mosaic chromosomal alterations (mCAs) are structural alterations that include deletions, duplications, or copy-neutral loss of heterozygosity. mCAs are reported to be associated with survival, age, cancer, and cardiovascular disease. Previous studies of mCAs in large population-based cohorts (UK Biobank, MGBB, BioBank Japan, and FinnGen) have demonstrated a steady increase of mCAs as people age. The distribution of mCAs in centenarians and their offspring is not well characterized. We applied MOsaic CHromosomal Alteration (MoChA) caller on 2298 genome-wide genotype samples of 1582 centenarians, 443 centenarians’ offspring, and 273 unrelated controls from the New England Centenarian Study (NECS). Integrating Log R ratio and B-allele frequency (BAF) intensities with genotype phase information, MoChA employs a Hidden Markov Model to detect mCA-induced deviations in allelic balance at heterozygous sites consistent with genotype phase in the DNA microarray data. We analyzed mCAs spanning over 100 k base pairs, with an estimated cell fraction less than 50%, within samples with genome-wide BAF phase concordance across phased heterozygous sites less than 0.51, and with LOD score of more than 10 for the model based on BAF and genotype phase. Our analysis showed that somatic mCAs increase with older age up to approximately 102 years, but the prevalence of the subjects with mCAs tend to decrease after that age, thus suggesting that accumulation of mCAs is less prevalent in long-lived individuals. We also used Poisson regression to show that centenarians and their offspring tend to accumulate less mCA (RR = 0.63, p=0.045) compared to the controls.

Momentum Backpropagation Optimization for Cancer Detection Based on DNA Microarray Data

Early detection of cancer can increase the success of treatment in patients with cancer. In the latest research, cancer can be detected through DNA Microarrays. Someone who suffers from cancer will experience changes in the value of certain gene expression. In previous studies, the Genetic Algorithm as a feature selection method and the Momentum Backpropagation algorithm as a classification method provide a fairly high classification performance, but the Momentum Backpropagation algorithm still has a low convergence rate because the learning rate used is still static. The low convergence rate makes the training process need more time to converge. Therefore, in this research an optimization of the Momentum Backpropagation algorithm is done by adding an adaptive learning rate scheme. The proposed scheme is proven to reduce the number of epochs needed in the training process from 390 epochs to 76 epochs compared to the Momentum Backpropagation algorithm. The proposed scheme can gain high accuracy of 90.51% for Colon Tumor data, and 100% for Leukemia, Lung Cancer, and Ovarian Cancer data.

Obesity associated pro-fibrotic protein augments fibrosis in heart

Chronic sterile inflammation in visceral fat has causal roles for systemic metabolic disorders in obesity. Inflamed visceral adipose tissue secretes pro-inflammatory adipokines, and this contributes to tissue remodeling under a metabolically stressed condition. Various kinds of white adipokines are broadly studied, however, roles of brown adipose tissue (BAT) derived adipokines (BATokine) remain to be explored. In this project, we tried to characterize pathogenic role of BATokine in obesity related fibrotic disorders, especially focusing on heart failure with preserved ejection fraction (HFpEF). For this purpose, we analyzed two sets of DNA microarray data, and identified an obesity associated pro-fibrotic protein (OAFP) as a possible pathogenic BATokine. Our biobank studies showed OAFP increased in patients with diastolic dysfunction, and E/e' analyzed with cardiac echo increased in direct proportion to circulating OAFP level in humans. We generated dietary obese mice model, and found OAFP increased both in BAT and circulation. We generated a murine systemic or BAT specific OAFP knockout (KO) models, and found that obesity-induced diastolic dysfunction ameliorated in these models. Cardiac fibrosis was also suppressed by genetic depletion of OAFP. We found OAFP increased in circulation in aged humans and mice, and studies in chronologically aged mice showed this molecule increased in BAT with aging. Our results indicate that OAFP is secreted predominantly from BAT, and mediates pathogenic roles by augmenting cardiac fibrosis in dietary obesity or aging. Suppression of OAFP may become a therapy for HFpEF. Funding Acknowledgement Type of funding source: None

Microarray Data Classification using Artificial Neural Network

The accurate cancer classification is very important task for cancer treatment. Recently the informative genes are identified from the thousands of genes for correct cancer classification. The collection of microscopic Deoxyribo Nucleic Acid (DNA) microarray is attached in the solid surface. In this study, DNA microarray data is used for cancer classification. The system uses Artificial Neural Network (ANN) for DNA Microarray Data Classification (MDC). Initially, the preprocessing step is made by using log transformation method to remove the raw data and feature selection. These selected features are classified by using ANN. REctified Linear Unit (RELU) activation function is used as the activation function in each ANN layer. Softmax is used for classification. The performance of the system is made by using leukemia dataset. MDC system produces the classification accuracy of 91.65% by using ANN

Towards Framing an Integrated Model for Optimization and Clustering (IMOC) High-Dimensional Non-Linear DNA Data Processing using ACO and DENCLUE

The Gene Expression data contains important information about the biological reactions that are carried out in creatures that are based on the surroundings. For understanding those processes in a better manner, the hidden expressions from the high-dimensional non-linear data are to be processed effectively. Moreover, the intricacy of biological data increases the challenges in High-dimensional non-linear gene data processing. For handling the complications, incorporation of clustering techniques is employed for identifying the patterns appropriately. Furthermore, this makes clarity in gene functions, regulations, inherent expressions, categories from noisy data input. In this paper, an Integrated Model for Optimization and Clustering (IMOC) is developed for efficient gene data processing for cancer detection application. Two efficient algorithms are integrated in this work, Ant Colony Optimization for effectively determine the features of gene data and Density based Clustering (DENCLUE) is for clustering the DNA data based on the determined features. For evaluating the proposed model, the benchmark datasets such as DNA Microarray Data of Leukemia and DNA Microarray Data of Colon Cancer are used. Further, the results show that the proposed model outperforms the existing models in accuracy and efficiency rates.