MicroRNAs (miRNAs) are a class of highly conserved, single-stranded RNA molecules (length, 18–25 nt) that regulate the expression of their target mRNAs. Previous studies have demonstrated that miRNAs may be novel biomarkers in the diagnosis of certain diseases. In order to evaluate the diagnostic value of miRNAs in childhood tuberculosis (TB), the circulating miRNA profile was determined using microarray analysis. An miRNA-gene network was constructed to identify closely associated miRNAs and these miRNAs were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A receiver operational curve (ROC) was used to evaluate the diagnostic sensitivity and specificity of confirmed miRNAs. The microarray data demonstrated that 29 miRNAs were altered with 15 upregulated and 14 downregulated. The network showed indicated 14 miRNAs that are critical in childhood TB. RT-qPCR validated that miR-1, miR-155, miR-31, miR-146a, miR-10a, miR-125b and miR-150 were downregulated in while miR-29 was upregulated in children with TB compared with uninfected children. The ROC curve data indicated the diagnostic value of single miRNA was as follows: miR-150>miR-146a>miR-125b>miR-31>miR-10a>miR-1>miR-155>miR-29. Notably, a combination of these miRNAs exhibited increased diagnostic value compared with any single miRNA. To the best of our knowledge, the present study is the first to identify the expression profile of circulating miRNAs in childhood TB and demonstrated that miRNAs may be a novel, non-invasive and effective biomarker for the early diagnosis of childhood TB.

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The type 2 immune response, induced by infection of respiratory syncytial virus (RSV), has been linked to asthma development, but it remains unclear how the response is initiated. Here, we reported that the high-mobility group box-1 (HMGB1) protein promotes the type 2 response in the later stage of RSV infection. In mice, we found that type 2 cytokines were elevated in the later stages, which were strongly diminished after administration of anti-HMGB1 antibodies. Further investigation revealed that HMGB1 expression was localized to CC10+ club cells in the lung. In the clinic, levels of HMGB1 in nasopharyngeal aspirates in hospitalized infants with RSV bronchiolitis [median (interquartile range) 161.20 ng/ml (68.06–221.30)] were significantly higher than those without lower respiratory tract infections [21.94 ng/ml (12.12–59.82); P < 0.001]. Moreover, higher levels of HMGB1 correlated with clinical severity. These results reveal a link between viral infection and the asthma-like type 2 responses that are associated with long-term consequences.

Artificial neural networks have been successfully incorporated into variational Monte Carlo method (VMC) to study quantum many-body systems. However, there have been few systematic studies of exploring quantum many-body physics using deep neural networks (DNNs), despite of the tremendous success enjoyed by DNNs in many other areas in recent years. One main challenge of implementing DNN in VMC is the inefficiency of optimizing such networks with large number of parameters. We introduce an importance sampling gradient optimization (ISGO) algorithm, which significantly improves the computational speed of training DNN in VMC. We design an efficient convolutional DNN architecture to compute the ground state of a one-dimensional (1D) SU (N ) spin chain. Our numerical results of the ground-state energies with up to 16 layers of DNN show excellent agreement with the Bethe-Ansatz exact solution. Furthermore, we also calculate the loop correlation function using the wave function obtained. Our work demonstrates the feasibility and advantages of applying DNNs to numerical quantum many-body calculations.Network Architectures -We consider a homogeneous 1D SU (N ) spin chain with N site spins, which is the simplest prototypical model with SU (N ) symmetry, governed by the arXiv:1905.10730v1 [cond-mat.str-el]