ObjectiveFetal growth restriction (FGR) is a devastating pregnancy complication that increases the risk of perinatal mortality and morbidity. This study aims to determine the combined and relative effects of genetic and intrauterine environments on neonatal microbial communities and to explore selective FGR-induced gut microbiota disruption, metabolic profile disturbances and possible outcomes.DesignWe profiled and compared the gut microbial colonisation of 150 pairs of twin neonates who were classified into four groups based on their chorionicity and discordance of fetal birth weight. Gut microbiota dysbiosis and faecal metabolic alterations were determined by 16S ribosomal RNA and metagenomic sequencing and metabolomics, and the long-term effects were explored by surveys of physical and neurocognitive development conducted after 2~3 years of follow-up.ResultsAdverse intrauterine environmental factors related to selective FGR dominate genetics in their effects of elevating bacterial diversity and altering the composition of early-life gut microbiota, and this effect is positively related to the severity of selective FGR in twins. The influence of genetic factors on gut microbes diminishes in the context of selective FGR. Gut microbiota dysbiosis in twin neonates with selective FGR and faecal metabolic alterations features decreased abundances of Enterococcus and Acinetobacter and downregulated methionine and cysteine levels. Correlation analysis indicates that the faecal cysteine level in early life is positively correlated with the physical and neurocognitive development of infants.ConclusionDysbiotic microbiota profiles and pronounced metabolic alterations are associated with selective FGR affected by adverse intrauterine environments, emphasising the possible effects of dysbiosis on long-term neurobehavioural development.
The UK Biobank is a rich national health resource that provides enormous opportunities for international researchers to examine, model, and analyze census-like multisource healthcare data. The archive presents several challenges related to aggregation and harmonization of complex data elements, feature heterogeneity and salience, and health analytics. Using 7,614 imaging, clinical, and phenotypic features of 9,914 subjects we performed deep computed phenotyping using unsupervised clustering and derived two distinct sub-cohorts. Using parametric and nonparametric tests, we determined the top 20 most salient features contributing to the cluster separation. Our approach generated decision rules to predict the presence and progression of depression or other mental illnesses by jointly representing and modeling the significant clinical and demographic variables along with the derived salient neuroimaging features. We reported consistency and reliability measures of the derived computed phenotypes and the top salient imaging biomarkers that contributed to the unsupervised clustering. This clinical decision support system identified and utilized holistically the most critical biomarkers for predicting mental health, e.g., depression. External validation of this technique on different populations may lead to reducing healthcare expenses and improving the processes of diagnosis, forecasting, and tracking of normal and pathological aging.
The theoretical foundations of Big Data Science are not fully developed, yet. This study proposes a new scalable framework for Big Data representation, high-throughput analytics (variable selection and noise reduction), and model-free inference. Specifically, we explore the core principles of distribution-free and model-agnostic methods for scientific inference based on Big Data sets. Compressive Big Data analytics (CBDA) iteratively generates random (sub)samples from a big and complex dataset. This subsampling with replacement is conducted on the feature and case levels and results in samples that are not necessarily consistent or congruent across iterations. The approach relies on an ensemble predictor where established model-based or model-free inference techniques are iteratively applied to preprocessed and harmonized samples. Repeating the subsampling and prediction steps many times, yields derived likelihoods, probabilities, or parameter estimates, which can be used to assess the algorithm reliability and accuracy of findings via bootstrapping methods, or to extract important features via controlled variable selection. CBDA provides a scalable algorithm for addressing some of the challenges associated with handling complex, incongruent, incomplete and multi-source data and analytics challenges. Albeit not fully developed yet, a CBDA mathematical framework will enable the study of the ergodic properties and the asymptotics of the specific statistical inference approaches via CBDA. We implemented the high-throughput CBDA method using pure R as well as via the graphical pipeline environment. To validate the technique, we used several simulated datasets as well as a real neuroimaging-genetics of Alzheimer’s disease case-study. The CBDA approach may be customized to provide generic representation of complex multimodal datasets and to provide stable scientific inference for large, incomplete, and multisource datasets.
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