Agenda:

• Joseph Jakes, PhD and Katie Ohno, PhD, Affiliation: Forest Service, Forest Products Laboratory, USDA
  Multiscale X-ray fluorescence microscopy of copper ion distribution in treated wood during exposure to copper-tolerant Fibroporia radiculosa
  
  
  Multiscale X ray fluorescence microscopy (XFM) and SEM–EDAX were employed to characterize copper ion localization, mobilization, and redistribution in copper sulfate–treated Pinus spp. during early colonization by the copper tolerant brown rot fungus Fibroporia radiculosa and the copper sensitive Gloeophyllum trabeum. G. trabeumexhibited strong inhibition by copper and maintained the original Cu spatial profile seen in treated wood controls. In contrast, F. radiculosa initiated copper mobilization within one week. Large field-of-view XFM showed Cu depletion in the upper test block regions and associated Cu accumulation toward central and lower parts of the wood blocks. Sub micrometer resolution XFM (µXFM) revealed removal of Cu from wood cell walls and redeposition onto lumen surfaces, providing the first cellular scale verification of active cell-wall copper mobilization by a copper tolerant decay fungus. SEM–EDAX corroborated the formation of copper rich crystalline deposits on lumen surfaces consistent with copper oxalate formation. Collectively, these multiscale analyses demonstrate that F. radiculosa employs an oxalate driven copper redistribution pathway involving acid-mediated solubilization, Fickian diffusion through moisture plasticized cell walls, and lumen surface precipitation. These insights advance understanding of copper tolerance in brown rot fungi and highlight limitations of copper based wood preservatives.

• Adam Nijhawan, PhD, Argonne National Laboratory
  Integrating X-ray Scattering with Artificial Intelligence for Accurate RNA Structure Prediction
  
  
  RNA molecules play essential roles in biology and therapeutics, often through their three-dimensional structures. However, accurate RNA structure determination remains challenging because structural heterogeneity limits the applicability of high-resolution experimental techniques. Consequently, only ~3,000 RNA-only structures are available in the Protein Data Bank (PDB), compared with more than 200,000 protein structures.
  
  Artificial intelligence has advanced RNA structure prediction, but performance continues to lag behind protein modeling due to limited training data and inadequate representation of conformational heterogeneity. To address these challenges, we are developing a denoising diffusion probabilistic model (DDPM) for RNA structure prediction. We apply diffusion directly in RNA torsional space, providing a compact, physically meaningful representation for generative modeling. We further incorporate global structural information from small-angle X-ray solution scattering (SAXS) and conformational ensembles generated by molecular dynamics (MD) simulations.
  Our results establish torsional space as an effective framework for generative RNA modeling and demonstrate the potential of experimental conditioning to overcome structural data limitations. This hybrid AI–experiment–simulation framework provides a path toward improved RNA structure prediction and applications in RNA therapeutics, drug discovery, and vaccine development.