No abstract

Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper) and knowledge-based method (traditional hydrological model) may booster simulation accuracy. In this study, we proposed a new back-propagation (BP) neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ) model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.

Goat is an important agricultural animal for meat production. Functional studies have demonstrated that microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and play an important role in various biological processes. Although studies on miRNAs expression profiles have been performed in various animals, relatively limited information about goat muscle miRNAs has been reported. To investigate the miRNAs involved in regulating different periods of skeletal muscle development, we herein performed a comprehensive research for expression profiles of caprine miRNAs during two developmental stages of skeletal muscles: fetal stage and six month-old stage. As a result, 15,627,457 and 15,593,721 clean reads were obtained from the fetal goat library (FC) and the six month old goat library (SMC), respectively. 464 known miRNAs and 83 novel miRNA candidates were identified. Furthermore, by comparing the miRNA profile, 336 differentially expressed miRNAs were identified and then the potential targets of the differentially expressed miRNAs were predicted. To understand the regulatory network of miRNAs during muscle development, the mRNA expression profiles for the two development stages were characterized and 7322 differentially expressed genes (DEGs) were identified. Then the potential targets of miRNAs were compared to the DEGs, the intersection of the two gene sets were screened out and called differentially expressed targets (DE-targets), which were involved in 231 pathways. Ten of the 231 pathways that have smallest P-value were shown as network figures. Based on the analysis of pathways and networks, we found that miR-424-5p and miR-29a might have important regulatory effect on muscle development, which needed to be further studied. This study provided the first global view of the miRNAs in caprine muscle tissues. Our results help elucidation of complex regulatory networks between miRNAs and mRNAs and for the study of muscle development.

The power-law length (L) divergence of thermal conductivity (κ) in one-dimensional (1D) systems, i.e., κ ∼ L α , has been predicted by theories and also corroborated by experiments. The theoretical predictions of the exponent α are usually ranging from 0.2 to 0.5; however sometimes, the experimental observations can be higher, e.g., α = 0.6-0.8. This dispute has not yet been settled. Here we show the first convincing evidence that an exponent of α 0.7 that falls within experimental observations, can occur in a theoretical model of 1D long-range interacting Fermi-Pasta-Ulam chain. This, for the first time, theoretically supports the possibility of a higher divergent exponent and thus sheds new light on understanding of extremely high thermal conductivity in 1D materials at macroscopic scales.