System Dynamics Modeling for Childhood Obesity

Effective strategies for prevention of obesity, particularly in youths, have been elusive since the recognition of obesity as a major public health issue two decades ago. In general, obesity is a result of chronic, quantitative imbalance between energy intake and energy expenditure, which is influenced by a combination of genetic, environmental, psychological and social factors. Therefore, a systems perspective is needed to examine effective obesity prevention strategies. In this study, a systems dynamics model was developed using the data from the Girls health Enrichment Multi-site Studies (GEMS). GEMS tested the efficacy of a 2-year family-based intervention to reduce excessive increase in body mass index (BMI) in 8-10 year old African American girls. First, an optimum model was built by systematically adding variables to fit the observed data by regression analysis for 50 randomly selected individuals from the cohort. The final model included nutrition, physical activity, and several environmental factors. Next, the model was used to compare two intervention strategies used in the GEMS study. Consistent with previous reports, we found that the two strategies did not affect the BMI increases observed in this cohort. Interestingly however, the model predicted that a 10 min increase in exercise would decrease BMI in the group receiving behavioral counseling. Our work suggests that system dynamics modeling may be useful for testing potential intervention strategies in complex disorders such as obesit

Isolasi Senyawa Fenolat dari Fraksi Etil Asetat Kulit Batang Tumbuhan Gandaria

Telah dilakukan isolasi senyawa fenolat dari fraksi etil asetat kulit batang tumbuhan Gandaria (Bouea macrophylla Griff). Ekstraksi dilakukan dengan metode maserasi dan pemisahan senyawa hasil isolasi dilakukan dengan teknik kromatografi. Hasil isolasi berupa kristal berwarna putih dengan titik leleh 185-187_C. Spektrum UV dalam pelarut etil asetat menunjukkan serapan maksimum pada 289 nm, mengindikasikan adanya ikatan rangkap terkonjugasi yang lazimnya merupakan cincin aromatis. Analisa spektrum IR menunjukkan adanya gugus −OH, C−H alifatik, C=O, C=C, C−H, C−O, C=C−H. Berdasarkan data-data spektrum UV, IR, serta berdasarkan uji fitokimia diduga senyawa hasil isolasi ini merupakan senyawa golongan fenolat yang tersubtitusi gugus alifatik dan gugus karbonil

Statistical Shrinkage Methods for Classification, Prediction, and Feature Extraction Using Genomewide Gene Expression Data and Small Sample Sizes

With advent of new technologies, more data is being collected than ever before. Data is pouring in from every conceivable direction: from operational and transactional systems, from Micro array experiments and Genome Wide Association Studies, from inbound and outbound customer contact points, from mobile media and the Web to mention a few. Researchers and investigators in many fields are faced with the problem of identifying important effects among thousands of variables in high dimensional data sets. This process often results in non or weekly identified effects. Nowadays a common problem when processing data sets with large number of variables compared to small sample sizes is to estimate the parameters associated with each variable. When the number of variables far exceeds the number of samples, the parameter estimation becomes very difficult. The attempt to find important variables deriving different phenomena based on single variable analysis is more likely to not give a comprehensive picture due to complexity of the phenomena and presence of several predictors with potentially significant effects. Thus, methods based on single variable analysis are too simple to give a comprehensive picture of phenotype architecture. Therefore, more statistically challenging models which are able to accommodate simultaneous analysis of a large number of variables despite small sample sizes are essential in these cohorts.In this thesis, we developed several novel methods for sample classification, prediction and feature extraction in cohorts with large number of variables compared to small sample sizes using Bayesian shrinkage methods as well as non-parametric methods such as Support Vector Machines and Random Forests. We utilized Generalized Double Pareto and Double Exponential prior distributions on parameters in Bayesian Generalized Linear Models setting. These distributions have a spike at zero shrinking the parameters towards zero which imposes sparsity in the model. We utilized Markov Chain Monte Carlo (MCMC) method based on Gibbs sampling algorithm to estimate the parameters. The models were applied to Microarray data sets such as prostate cancer, leukemia, and breast cancer cohorts. In order to obtain more robust results 50 resampling on train and test data was performed and average performance of the models in 50 runs were reported. We investigated the classification accuracy, feature extraction ability, and prediction ability of the models. Based on our findings, the Bayesian hierarchical models developed obtain high classification accuracy as well as result in more cohesive variable sets compared to other common methods used for the same purpose. We show that using few predictors obtained from our models, we achieve higher performance compared to other competitive methods. We also investigated the use of literature to aid the selection of initial predictors used in the model. Our finding suggests that even though in some instances use of literature will result in better prediction and classification, this is not unanimously true and in some cases it results in poorer performance. This is mainly due to the fact that literature based predictor sets can be weak signals in the data set at hand as well as our information about the variables deriving different phenomena based on literature is not fully complete. Ideally, we would like to use literature to tune and prioritize signals directly coming from the experiment. To this end, we developed a literature aided sparse Bayesian Generalized linear model that uses literature information a priori to guide the choice of hyper parameters and amount of shrinkage imposed in the model. The developed model not only achieves high classification accuracy, sensitivity, and specificity but also, results is substantially more relevant genesets which turns out to explain the underlying mechanisms of phetotypes better

A Bayesian approach for inducing sparsity in generalized linear models with multi-category response

Background: The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. Several approaches exist to reduce the number of variables with respect to small sample sizes. In this study, we utilized the Generalized Double Pareto (GDP) prior to induce sparsity in a Bayesian Generalized Linear Model (GLM) setting. The approach was evaluated using a publicly available microarray dataset containing 99 samples corresponding to four different prostate cancer subtypes. Results: A hierarchical Sparse Bayesian GLM using GDP prior (SBGG) was developed to take into account the progressive nature of the response variable. We obtained an average overall classification accuracy between 82.5% and 94%, which was higher than Support Vector Machine, Random Forest or a Sparse Bayesian GLM using double exponential priors. Additionally, SBGG outperforms the other 3 methods in correctly identifying pre-metastatic stages of cancer progression, which can prove extremely valuable for therapeutic and diagnostic purposes. Importantly, using Geneset Cohesion Analysis Tool, we found that the top 100 genes produced by SBGG had an average functional cohesion p-value of 2.0E-4 compared to 0.007 to 0.131 produced by the other methods. Conclusions: Using GDP in a Bayesian GLM model applied to cancer progression data results in better subclass prediction. In particular, the method identifies pre-metastatic stages of prostate cancer with substantially better accuracy and produces more functionally relevant gene sets

Additional file 2 of Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts

Tables S1A, S1B, S1C, S2A, S2B, S3, S4A and S4B. Microsoft Excel 2013 workbook ’S11-S1.xlsx’ contains supplementary tables 1A, 1B, 1C, 2A, 2B, 3, 4A and 4B in separate tabs. (XLSX 32.5 KB