A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/

Exploring the associations between microRNA expression profiles and environmental pollutants in human placenta from the National Children's Study (NCS)

The placenta is the principal regulator of the in utero environment, and disruptions to this environment can result in adverse offspring health outcomes. To better characterize the impact of in utero perturbations, we assessed the influence of known environmental pollutants on the expression of microRNA (miRNA) in placental samples collected from the National Children's Study (NCS) Vanguard birth cohort. This study analyzed the expression of 654 miRNAs in 110 term placentas. Environmental pollutants measured in these placentas included dichlorodiphenyldichloroethylene (DDE), bisphenol A (BPA), polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), arsenic (As), mercury (Hg), lead (Pb), and cadmium (Cd). A moderated t-test was used to identify a panel of differentially expressed miRNAs, which were further analyzed using generalized linear models. We observed 112 miRNAs consistently expressed in >70% of the samples. Consistent with the literature, miRNAs located within the imprinted placenta-specific C19MC cluster, specifically mir-517a, mir-517c, mir-522, and mir-23a, are among the top expressed miRNA in our study. We observed a positive association between PBDE 209 and miR-188–5p and an inverse association between PBDE 99 and let-7c. Both PCBs and Cd were positively associated with miR-1537 expression level. In addition, multiple let-7 family members were downregulated with increasing levels of Hg and Pb. We did not observe DDE or BPA levels to be associated with placental miRNA expression. This is the first birth cohort study linking environmental pollutants and placental expression of miRNAs. Our results suggest that placental miRNA profiles may signal in utero exposures to environmental chemicals

Tooth biomarkers to characterize the temporal dynamics of the fetal and early-life exposome

BackgroundTeeth have unique histology that make this biomatrix a time-capsule for retrospective exposure analysis of fetal and early life. However, most analytic methods require pulverizing the whole tooth, which eliminates exposure timing information. Further, the range of chemicals and endogenous exposures that can be measured in teeth has yet to be fully characterized.MethodsWe performed untargeted metabolomics on micro-dissected layers from naturally shed deciduous teeth. Using four liquid-chromatography high-resolution mass spectrometry analytical modes, we profiled small molecules (<1000 Da) from prenatal and postnatal tooth fractions. In addition, we employed linear regression on the tooth fraction pairs from 31 children to identify metabolites that discriminate between prenatal and postnatal exposures.ResultsOf over 10,000 features measured in teeth dentin, 390 unique compounds were annotated from 62 chemical classes. The class with the largest number of compounds was carboxylic acids and their derivatives (36%). Of the annotated exogenous metabolites (phthalates, parabens, perfluoroalkyl compounds, and cotinine) and endogenous metabolites (fatty acids, steroids, carnitines, amino acids, and others), 91 are linked to 256 health conditions through published literature. Differential analysis revealed 267 metabolites significantly different between the prenatal and the postnatal tooth fractions (adj. p-value < 0.05, Bonferroni correction), and 21 metabolites exclusive to the prenatal fraction.ConclusionsThe prenatal and early postnatal exposome revealed from dental biomarkers represents a broad range of endogenous and exogenous metabolites for a comprehensive characterization in environmental health research. Most importantly, this technology provides a direct window into fetal exposures that is not possible by maternal biomarkers. Indeed, we identified several metabolites exclusively in the prenatal fraction, suggesting unique fetal exposures that are markedly different to postnatal exposures. Expansion of databases that include tooth matrix metabolites will strengthen biological interpretation and shed light on exposures during gestation and early life that may be causally linked with later health conditions