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Altered DNA dynamics at lesion sites are implicated in how DNA repair proteins pause and identify damage within genomic DNA. We examined DNA dynamics in the context of damage recognition by Rad4 (yeast ortholog of XPC), which recognizes diverse lesions from environmental mutagens and initiates nucleotide excision repair. Previous studies with a cytosine-analog FRET pair placed on either side of 3 base-pair (bp) mismatched sites – recognized specifically by Rad4 in vitro – unveiled severely deformed DNA even without Rad4 (Chakraborty et al. (2018) Nucleic Acid Res. 46: 1240-1255). Here, using laser T-jump, we revealed the timescales of these spontaneous deformations. 3-bp AT-rich nonspecific sites, whether matched or mismatched, exhibited conformational dynamics primarily within the T-jump observation window (~20 µs – <100 ms), albeit with some amplitude in unresolved (<20 µs) kinetics. The amplitudes of the “missing” fast kinetics increased dramatically for mismatched specific sites, which were further distinguished by additional “missing” amplitude in slow (>100 ms) kinetics at elevated temperatures. We posit that the rapid (µs-ms) fluctuations help stall a diffusing protein at AT-rich/damaged sites and that the >100-ms kinetics reflect a propensity for specific DNA to adopt unwound/bent conformations that may resemble Rad4-bound structures. These studies provide compelling evidence for unusual DNA dynamics and deformability that likely govern how Rad4 senses DNA damage. 

Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification of stochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary. The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization, causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo (MC) simulations. In this work, we present a machine learning (ML) approach, as a data-driven surrogate to MD simulations, which only needs a few MD simulations. This efficient yet high-fidelity ML approach enables MC simulations for full-scale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate. Unlike conventional ML models, the proposed hybrid polynomial correlated function expansion here, being a Bayesian ML approach, is data efficient. Further, it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations, which in principle addresses the issue of repeatability in stochastic simulations with low variance. Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus, yield strength and ultimate strength, illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency.

 

The total energy transfer from the solar wind to the magnetosphere is governed by the reconnection rate at the magnetosphere edges as the Z‐component of interplanetary magnetic field (IMFB_z) turns southward. The geomagnetic storm on 21–22 January 2005 is considered to be anomalous as the SYM‐H index that signifies the strength of ring current, decreases and had a sustained trough value of −101 nT lasting more than 6 hr under northward IMFB_zconditions. In this work, the standard WINDMI model is utilized to estimate the growth and decay of magnetospheric currents by using several solar wind‐magnetosphere coupling functions. However, it is found that the WINDMI model driven by any of these coupling functions is not fully able to explain the decrease of SYM‐H under northward IMFB_z. A dense plasma sheet along with signatures of a highly stretched magnetosphere was observed during this storm. The SYM‐H variations during the entire duration of the storm were only reproduced after modifying the WINDMI model to account for the effects of the dense plasma sheet. The limitations of directly driven models relying purely on the solar wind parameters and not accounting for the state of the magnetosphere are highlighted by this work.

 

Solar eclipses present a rare glimpse into the impact of ionospheric electrodynamics on the magnetosphere independent of other well studied seasonal influences. Despite decades of study, we still do not have a complete description of the conditions for geomagnetic substorm onset. We present herein a mutual information based study of previously published substorm onsets and the past two decades of eclipses which indicates the likelihood of co‐occurrence is greater than random chance. A plausible interpretation for this relation suggests that the abrupt fluctuations in ionospheric conductivity during an eclipse may influence the magnetospheric preconditions of substorm initiation. While the mechanism remains unclear, this study presents strong evidence of a link between substorm onset and solar eclipses.

 

High spin states in 104 Ag were populated via heavy-ion ( 32 S) induced fusion evaporation reaction at a beam energy of 110 MeV. The de-exciting γ-rays were detected by 18 Compton suppressed HPGe clover detectors, placed in different (θ, φ) angles. Spin of several excited states were assigned firmly from the present angular correlation measurement.