Lightning mapping observation of a terrestrial gamma-ray flash

We report the observation with the North Alabama Lightning Mapping Array (LMA) related to a terrestrial gamma-ray flash (TGF) detected by RHESSI on 26 July 2008. The LMA data explicitly show the TGF was produced during the initial development of a compact intracloud (IC) lightning flash between a negative charge region centered at about 8.5 km above sea level (-22C temperature level) a higher positive region centered at 13 km, both confined to the convective core of an isolated storm in close proximity to the RHESSI footprint. After the occurrence of an LMA source with a high peak power (26 kW), the initial lightning evolution caused an unusually large IC current moment that became detectable 2 ms after the first LMA source and increased for another 2 ms, during which the burst of gamma-rays was produced. This slowly building current moment was most likely associated with the upward leader progression, which produced an uncommonly large IC charge moment change (+90 Ckm) in 3 ms while being punctuated by a sequence of fast discharge. These observations suggest that the leader development may be involved in the TGF production. Copyright © 2010 by the American Geophysical Union.

Computational Methods for RNA Structure Validation and Improvement.

With increasing recognition of the roles RNA molecules and RNA/protein complexes play in an unexpected variety of biological processes, understanding of RNA structure-function relationships is of high current importance. To make clean biological interpretations from three-dimensional structures, it is imperative to have high-quality, accurate RNA crystal structures available, and the community has thoroughly embraced that goal. However, due to the many degrees of freedom inherent in RNA structure (especially for the backbone), it is a significant challenge to succeed in building accurate experimental models for RNA structures. This chapter describes the tools and techniques our research group and our collaborators have developed over the years to help RNA structural biologists both evaluate and achieve better accuracy. Expert analysis of large, high-resolution, quality-conscious RNA datasets provides the fundamental information that enables automated methods for robust and efficient error diagnosis in validating RNA structures at all resolutions. The even more crucial goal of correcting the diagnosed outliers has steadily developed toward highly effective, computationally based techniques. Automation enables solving complex issues in large RNA structures, but cannot circumvent the need for thoughtful examination of local details, and so we also provide some guidance for interpreting and acting on the results of current structure validation for RNA.