Maternal and Infant Dietary Patterns Are Not Related to Food Allergy Risk in Singapore Children: GUSTO Cohort Study.

BACKGROUND: We previously reported that delayed allergenic food introduction in infancy did not increase food allergy risk until age 4 y within our prospective cohort. However, it remains unclear whether other aspects of maternal or infant diet play roles in the development of childhood food allergy. OBJECTIVES: We examined the relationship between maternal pregnancy and infant dietary patterns and the development of food allergies until age 8 y. METHODS: Among 1152 Singapore Growing Up in Singapore Towards healthy Outcomes study mother-infant dyads, the infant's diet was ascertained using food frequency questionnaires at 18 mo. Maternal dietary patterns during pregnancy were derived from 24-h diet recalls. Food allergy was determined through interviewer-administered questionnaires at regular time points from infancy to age 8 y and defined as a positive history of allergic reactions, alongside skin prick tests at 18 mo, 3, 5, and 8 y. RESULTS: Food allergy prevalence was 2.5% (22/883) at 12 mo and generally decreased over time by 8 y (1.9%; 14/736). Higher maternal dietary quality was associated with increased risk of food allergy (P ≤ 0.016); however, odds ratios were modest. Offspring food allergy risk ≤8 y showed no associations with measures of infant diet including timing of solids/food introduction (adjusted odds ratio [aOR]: 0.90; 95% confidence interval [CI]: 0.42, 1.92), infant's diet quality (aOR: 0.93; 95% CI: 0.88, 0.99) or diet diversity (aOR: 0.84; 95% CI: 0.6, 1.19). Most infants (89%) were first introduced to cow milk protein within the first month of life, while egg and peanut introduction were delayed (58.3% introduced by mean age 8.8 mo and 59.8% by mean age 18.1 mo, respectively). CONCLUSIONS: Apart from maternal diet quality showing a modest association, infant's allergenic food introduction, diet quality, and dietary diversity were not associated with food allergy development in this Asian pediatric population. Interventional studies are needed to evaluate the efficacy of these approaches to food allergy prevention across different populations.

Elastic-Net Copula Granger Causality for Inference of Biological Networks.

AIM: In bioinformatics, the inference of biological networks is one of the most active research areas. It involves decoding various complex biological networks that are responsible for performing diverse functions in human body. Among these networks analysis, most of the research focus is towards understanding effective brain connectivity and gene networks in order to cure and prevent related diseases like Alzheimer and cancer respectively. However, with recent advances in data procurement technology, such as DNA microarray analysis and fMRI that can simultaneously process a large amount of data, it yields high-dimensional data sets. These high dimensional dataset analyses possess challenges for the analyst. BACKGROUND: Traditional methods of Granger causality inference use ordinary least-squares methods for structure estimation, which confront dimensionality issues when applied to high-dimensional data. Apart from dimensionality issues, most existing methods were designed to capture only the linear inferences from time series data. METHOD AND CONCLUSION: In this paper, we address the issues involved in assessing Granger causality for both linear and nonlinear high-dimensional data by proposing an elegant form of the existing LASSO-based method that we call "Elastic-Net Copula Granger causality". This method provides a more stable way to infer biological networks which has been verified using rigorous experimentation. We have compared the proposed method with the existing method and demonstrated that this new strategy outperforms the existing method on all measures: precision, false detection rate, recall, and F1 score. We have also applied both methods to real HeLa cell data and StarPlus fMRI datasets and presented a comparison of the effectiveness of both methods.

Inference of biological networks using Bi-directional Random Forest Granger causality.

The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality. This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets, i.e., fMRI and HeLa cell. fMRI data was used to map brain network involved in deductive reasoning while HeLa cell dataset was used to map gene network involved in cancer.

Random forest Granger causality for detection of effective brain connectivity using high-dimensional data.

Studies have shown that the brain functions are not localized to isolated areas and connections but rather depend on the intricate network of connections and regions inside the brain. These networks are commonly analyzed using Granger causality (GC) that utilizes the ordinary least squares (OLS) method for its standard implementation. In the past, several approaches have shown to solve the limitations of OLS by using diverse regularization systems. However, there are still some shortcomings in terms of accuracy, precision, and false discovery rate (FDR). In this paper, we are proposing a new strategy to use Random Forest as a regularization technique for computing GC that will improve these shortcomings. We have demonstrated the effectiveness of our proposed methodology by comparing the results with existing Least absolute shrinkage and selection operator (LASSO), and Elastic-Net regularized implementations of GC using simulated dataset. Later, we have used our proposed approach to map the network involved during deductive reasoning using real StarPlus dataset.