MicroRNA (miRNA) are well known to target 3’ untranslated regions (3’UTR) in mRNAs to silence gene expression at post-transcriptional levels. Multiple reports have also indicated the capability of miRNAs to target protein-coding sequences (CDS); however, miRNAs have been generally believed to function in a similar mechanism(s) regardless of the location of their action sites. We herein report a class of miRNA recognition elements (MREs) that exclusively function in CDS regions in humans. Through functional and mechanistic characterization of these “unusual” MREs, we demonstrate that CDS-targeted miRNAs require extensive base pairings in the 3’ side rather than the 5’ seed; cause gene silencing in an Argonaute-dependent, but GW182-independent manner; and repress translation by inducing transient ribosome stalling instead of mRNA destabilization. These findings reveal distinct mechanisms and functional consequences for miRNAs to target CDS versus 3’UTR and suggest that CDS-targeted miRNAs may enlist a translational quality control (QC)-related mechanism to regulate translation in mammalian cells.
In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity.
In the face of growing demand for air traffic, there is a clear need to measure how difficult a given air traffic situation looks. Currently, the concept of air traffic complexity is usually used to describe the air traffic situation. As yet, not enough work has been done on the between-aircraft proximity from the perspective of structure. This paper proposed a method for description of structural characteristics of air traffic situation based on the theory of complex network, which provides a new clue to precisely describe the air traffic situation complexity. The routinely recorded radar data are collected to construct air traffic situation network with aircraft regarded as node, and the between-aircraft proximity relation as edge. The air traffic situation network under three thresholds were statistically analyzed using network topology indices including degree, edge, connection rate, and network structure entropy. The results show that the inherent structure characteristics can provide an intuitionistic and accessible metrics to measure air traffic situation. For example, node degree can distinguish the key aircraft in the sector; the network connection rate reflects the proximity of aircraft; the network structure entropy reflects the homogeneity of aircraft node degrees.
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