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G-quadruplexes (G4s) are well known non-canonical DNA secondary structures that can form in human cells. Most of the tools available to investigate G4-biology rely on small molecule ligands that stabilise these structures. However, the development of probes that disrupt G4s is equally important to study their biology. In this study, we investigated the disruption of G4s using Locked Nucleic Acids (LNA) as invader probes. We demonstrated that strategic positioning of LNA-modifications within short oligonucleotides (10 nts.) can significantly accelerate the rate of G4-disruption. Single-molecule experiments revealed that short LNA-probes can promote disruption of G4s with mechanical stability sufficient to stall polymerases. We corroborated this using a single-step extension assay, revealing that short LNA-probes can relieve replication dependent polymerase-stalling at G4 sites. We further demonstrated the potential of such LNA-based probes to study G4-biology in cells. By using a dual-luciferase assay, we found that short LNA probes can enhance the expression of c-KIT to levels similar to those observed when the c-KIT promoter is mutated to prevent the formation of the c-KIT1 G4. Collectively, our data suggest a potential use of rationally designed LNA-modified oligonucleotides as an accessible chemical-biology tool for disrupting individual G4s and interrogating their biological functions in cells.

 

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.

 

We report the synthesis and characterization of as-grown SrFexMn1-xO2.5 epitaxial films, which were also subjected to post-growth oxidation and topotactic fluorination to obtain SrFexMn1-xO3 and SrFexMn1-xO(2.5-d)Fg films. We show how both the B-site cation and anion composition influence the structural, electronic, and optical properties of this family of perovskite materials. The Fe substitution of Mn in SrMnO2.5 gradually expands the c-axis parameter, as indicated by X-ray diffraction. With increasing x, the F content incorporated under identical fluorination conditions increases, reaching its maximum in SrFeO(2.5-d)Fg. In the compounds with mixed B-site occupation, the Fe 2p photoemission peaks are shifted upon fluorination while the Mn 2p peaks are not, suggesting inductive effects lead to asymmetric responses in how F alters the Mn and Fe bonds. Electronic transport measurements reveal all compounds are insulators, with the exception of SrFeO3, and demonstrate that fluorination increases resistivity for all values of x. Optical absorption spectra in the SrFexMn1-xO2.5 and SrFexMn1-xO3 films evolve systematically as a function of x, consistent with a physical scenario in which optical changes with Fe substitution arise from a linear combination of Mn and Fe 3d bands within the electronic structure. In contrast, the F incorporation induces non-linear changes to the optical response, suggesting a more complex impact on the electronic structure in materials with concurrent B-site and anion site substitution.