Effects of galactooligosaccharides on maternal gut microbiota, glucose metabolism, lipid metabolism and inflammation in pregnancy: A randomized controlled pilot study

BackgroundGut microbiota of pregnant women change with the gestational week. On the one hand, they participate in the metabolic adaptation of pregnant women. On the other hand, the abnormal composition of gut microbiota of pregnant women is more likely to suffer from gestational diabetes mellitus (GDM). Therefore, gut microbiota targeted treatment through dietary supplements is particularly important for prevention or treatment. Prebiotic supplements containing galactooligosaccharides (GOS) may be an intervention method, but the effect is still unclear.ObjectiveThis study aims to evaluate the feasibility and acceptability of prebiotic intervention in healthy pregnant women during pregnancy, and to explore the possible effects of intervention on pregnant women and the influence on gut microbiota as preliminaries.MethodsAfter recruitment in first trimester, 52 pregnant women were randomly assigned to receive GOS intervention or placebo containing fructooligosaccharides. 16S rRNA sequencing technology was used to detect the composition, diversity and differential flora of gut microbiota. Lipid metabolism, glucose metabolism and inflammatory factors during pregnancy were also analyzed.ResultsThe adverse symptoms of GOS intervention are mild and relatively safe. For pregnant women, there was no significant difference in the GDM incidence rates and gestational weight gain (GWG) in the GOS group compared with placebo (P > 0.05). Compared with the placebo group, the levels of FPG, TG, TC, HDL-C LDL-C, and IL-6 had no significant difference in GOS group (P > 0.05). For newborns, there was no significant difference between GOS group and placebo group in the following variables including gestational week, birth weight, birth length, head circumference, chest circumference, sex, and delivery mode (P > 0.05). And compared with the placebo group, the GOS group had a higher abundance of Paraprevotella and Dorea, but lower abundance of LachnospiraceaeUCG_001.ConclusionsGOS prebiotics appear to be safe and acceptable for the enrolled pregnancies. Although GOS intervention did not show the robust benefits on glucose and lipid metabolism. However, the intervention had a certain impact on the compostion of gut microbiota. GOS can be considered as a dietary supplement during pregnancy, and further clinical studies are needed to explore this in the future

Detailed simulation of LOX/GCH4 flame-vortex interaction in supercritical Taylor-Green flows with machine learning

Accurate and affordable simulation of supercritical reacting flow is of practical importance for developing advanced engine systems for liquid rockets, heavy-duty powertrains, and next-generation gas turbines. In this work, we present detailed numerical simulations of LOX/GCH4 flame-vortex interaction under supercritical conditions. The well-established benchmark configuration of three-dimensional Taylor-Green vortex (TGV) embedded with a diffusion flame is modified for real fluid simulations. Both ideal gas and Peng-Robinson (PR) cubic equation of states are studied to reveal the real fluid effects on the TGV evolution and flame-vortex interaction. The results show intensified flame stretching and quenching arising from the intrinsic large density gradients of real gases, as compared to that for the idea gases. Furthermore, to reduce the computational cost associated with real fluid thermophysical property calculations, a machine learning-based strategy utilising deep neural networks (DNNs) is developed and then assessed using the three-dimensional reactive TGV. Generally good prediction accuracy is achieved by the DNN, meanwhile providing a computational speed-up of 13 times over the convectional approach. The profound physics involved in flame-vortex interaction under supercritical conditions demonstrated by this study provides a benchmark for future related studies, and the machine learning modelling approach proposed is promising for practical high-fidelity simulation of supercritical combustion

Local and global convolutional transformer-based motor imagery EEG classification

Transformer, a deep learning model with the self-attention mechanism, combined with the convolution neural network (CNN) has been successfully applied for decoding electroencephalogram (EEG) signals in Motor Imagery (MI) Brain-Computer Interface (BCI). However, the extremely non-linear, nonstationary characteristics of the EEG signals limits the effectiveness and efficiency of the deep learning methods. In addition, the variety of subjects and the experimental sessions impact the model adaptability. In this study, we propose a local and global convolutional transformer-based approach for MI-EEG classification. The local transformer encoder is combined to dynamically extract temporal features and make up for the shortcomings of the CNN model. The spatial features from all channels and the difference in hemispheres are obtained to improve the robustness of the model. To acquire adequate temporal-spatial feature representations, we combine the global transformer encoder and Densely Connected Network to improve the information flow and reuse. To validate the performance of the proposed model, three scenarios including within-session, cross-session and two-session are designed. In the experiments, the proposed method achieves up to 1.46%, 7.49% and 7.46% accuracy improvement respectively in the three scenarios for the public Korean dataset compared with current state-of-the-art models. For the BCI competition IV 2a dataset, the proposed model also achieves a 2.12% and 2.21% improvement for the cross-session and two-session scenarios respectively. The results confirm that the proposed approach can effectively extract much richer set of MI features from the EEG signals and improve the performance in the BCI applications

BiOBr nanoflakes with strong Kerr nonlinearity towards hybrid integrated photonic devices

© 2020 SPIE. As a new group of advanced 2D layered materials, bismuth oxyhalides, i.e., BiOX (X = Cl, Br, I), have recently become of great interest. In this work, we characterize the third-order optical nonlinearities of BiOBr, an important member of the BiOX family. The nonlinear absorption and Kerr nonlinearity of BiOBr nanoflakes at both 800 nm and 1550 nm are characterized via the Z-Scan technique. Experimental results show that BiOBr nanoflakes exhibit a large nonlinear absorption coefficient β ∼ 10-7 m/W as well as a large Kerr coefficient n2 ∼ 10-14 m2/W. We also note that the n2 of BiOBr reverses sign from negative to positive as the wavelength is changed from 800 nm to 1550 nm. We further characterize the thickness-dependent nonlinear optical properties of BiOBr nanoflakes, finding that the magnitudes of β and n2 increase with decreasing thickness of the BiOBr nanoflakes. Finally, we integrate BiOBr nanoflakes into silicon integrated waveguides and measure their insertion loss, with the extracted waveguide propagation loss showing good agreement with mode simulations based on ellipsometry measurements. These results confirm the strong potential of BiOBr as a promising nonlinear optical material for high-performance hybrid integrated photonic devices