Single-Molecule Analysis Reveals the Kinetics and Physiological Relevance of MutL-ssDNA Binding

DNA binding by MutL homologs (MLH/PMS) during mismatch repair (MMR) has been considered based on biochemical and genetic studies. Bulk studies with MutL and its yeast homologs Mlh1-Pms1 have suggested an integral role for a single-stranded DNA (ssDNA) binding activity during MMR. We have developed single-molecule Förster resonance energy transfer (smFRET) and a single-molecule DNA flow-extension assays to examine MutL interaction with ssDNA in real time. The smFRET assay allowed us to observe MutL-ssDNA association and dissociation. We determined that MutL-ssDNA binding required ATP and was the greatest at ionic strength below 25 mM (KD = 29 nM) while it dramatically decreases above 100 mM (KD>2 µM). Single-molecule DNA flow-extension analysis suggests that multiple MutL proteins may bind ssDNA at low ionic strength but this activity does not enhance stability at elevated ionic strengths. These studies are consistent with the conclusion that a stable MutL-ssDNA interaction is unlikely to occur at physiological salt eliminating a number of MMR models. However, the activity may infer some related dynamic DNA transaction process during MMR

Robust design optimization of spin algorithm to reduce spinning time and vibration in washing machine

This study focuses on optimizing the spin algorithm of washing machines to reduce spin time and vibration, two crucial performance metrics often in a trade-off relationship due to structural limitations. We developed a simulator for the spin process that accounts for the uncertainty associated with the clothes’ position in the drum. Using this simulator, we designed a robust optimization algorithm to minimize spin time while maintaining acceptable vibration levels. The variation in the mass and position of laundry within the drum primarily affects vibration performance during the spin process. We formulated the unknown unbalance caused by the laundry as test-implementable unbalance parameters (UBP), represented as random variables. The uncertainties in the laundry’s unbalance for repeated experiments were estimated by the distribution of these UBPs. Experiments were conducted at parametrically designed points to measure vibrations and internal sensor values for control in specific revolutions per minute (RPM)ranges. These data were used to create approximate models. Monte Carlo simulation was then applied with UBP distributions obtained from the clothing experiments to meta-models, and the simulator was equipped with the spin algorithm. This process allowed us to predict the distribution of the maximum tub vibration displacement and spin time. A robust optimization algorithm was subsequently applied to the simulation model to derive the optimal spin algorithm. The proposed methodology yielded substantial improvements: a 25% reduction in the mean value of maximum displacement and a 90% reduction in the mean value of spin time compared to initial values