Despite decades of declining air pollution, urban U.S. areas are still affected by summertime ozone and wintertime particulate matter exceedance events. Volatile organic compounds (VOCs) are known precursors of secondary organic aerosol (SOA) and photochemically produced ozone. Urban VOC emission sources, including on-road transportation emissions, have decreased significantly over the past few decades through successful regulatory measures. These drastic reductions in VOC emissions have led to a change in the distribution of urban emissions and noncombustion sources of VOCs such as those from volatile chemical products (VCPs), which now account for a higher fraction of the urban VOC burden. Given this shift in emission sources, it is essential to quantify the relative contribution of VCP and mobile source emissions to urban pollution. Herein, ground site and mobile laboratory measurements of VOCs were performed. Two ground site locations with different population densities, Boulder, CO, and New York City (NYC), NY, were chosen in order to evaluate the influence of VCPs in cities with varying mixtures of VCPs and mobile source emissions. Positive matrix factorization was used to attribute hundreds of compounds to mobile- and VCP-dominated sources. VCP-dominated emissions contributed to 42 and 78% of anthropogenic VOC emissions for Boulder and NYC, respectively, while mobile source emissions contributed 58 and 22%. Apportioned VOC emissions were compared to those estimated from the Fuel-based Inventory of Vehicle Emissions and VCPs and agreed to within 25% for the bulk comparison and within 30% for more than half of individual compounds. The evaluated inventory was extended to other U.S. cities and it suggests that 50 to 80% of emissions, reactivity, and the SOA-forming potential of urban anthropogenic VOCs are associated with VCP-dominated sources, demonstrating their important role in urban U.S. air quality.Magnesium ions play an essential role in many vital processes. To correctly describe their interactions in molecular dynamics simulations, an accurate parametrization is crucial. Despite the importance and considerable scientific effort, current force fields based on the commonly used 12-6 Lennard-Jones interaction potential fail to reproduce a variety of experimental solution properties. In particular, no parametrization exists so far that simultaneously reproduces the solvation free energy and the distance to the water oxygens in the first hydration shell. Moreover, current Mg2+ force fields significantly underestimate the rate of water exchange leading to unrealistically slow exchange kinetics. In order to make progress in the development of improved models, we systematically optimize the Mg2+ parameters in combination with the TIP3P water model in a much larger parameter space than previously done. The results show that a long-ranged interaction potential and modified Lorentz-Berthelot combination rules allow us to accurately reproduce multiple experimental properties including the solvation free energy, the distances to the oxygens of the first hydration shell, the hydration number, the activity coefficient derivative in MgCl2 solutions, the self-diffusion coefficient, and the binding affinity to the phosphate oxygen of RNA. Matching this broad range of thermodynamic properties, we present two sets of optimal parameters MicroMg yields water exchange on the microsecond timescale in agreement with experiments. NanoMg yields water exchange on the nanosecond timescale facilitating the direct observation of ion-binding events. As shown for the example of the add A-riboswitch, the optimized parameters correctly reproduce the structure of specifically bound ions and permit the de novo prediction of Mg2+-binding sites in biomolecular simulations.The MARTINI model is a widely used coarse-grained force field popular for its capacity to represent a diverse array of complex biomolecules. However, efforts to simulate increasingly realistic models of membranes, involving complex lipid mixtures and multiple proteins, suggest that membrane protein aggregates are overstabilized by the MARTINI v2.2 force field. In this study, we address this shortcoming of the MARTINI model. We determined the free energy of dimerization of four transmembrane protein systems using the nonpolarizable MARTINI model. https://www.selleckchem.com/products/Lapatinib-Ditosylate.html Comparison with experimental FRET-based estimates of the dimerization free energy was used to quantify the significant overstabilization of each protein homodimer studied. To improve the agreement between simulation and experiment, a single uniform scaling factor, α, was used to enhance the protein-lipid Lennard-Jones interaction. A value of α = 1.04-1.045 was found to provide the best fit to the dimerization free energies for the proteins studied while maintaining the specificity of contacts at the dimer interface. To further validate the modified force field, we performed a multiprotein simulation using both MARTINI v2.2 and the reparameterized MARTINI model. While the original MARTINI model predicts oligomerization of protein into a single aggregate, the reparameterized MARTINI model maintains a dynamic equilibrium between monomers and dimers as predicted by experimental studies. The proposed reparameterization is an alternative to the standard MARTINI model for use in simulations of realistic models of a biological membrane containing diverse lipids and proteins.Selective and sensitive detection of nucleic acid biomarkers is of great significance in early-stage diagnosis and targeted therapy. Therefore, the development of diagnostic methods capable of detecting diseases at the molecular level in biological fluids is vital to the emerging revolution in the early diagnosis of diseases. However, the vast majority of the currently available ultrasensitive detection strategies involve either target/signal amplification or involve complex designs. Here, using a p53 tumor suppressor gene whose mutation has been implicated in more than 50% of human cancers, we show a background-free ultrasensitive detection of this gene on a simple platform. The sensor exhibits a relatively static mid-FRET state in the absence of a target that can be attributed to the time-averaged fluorescence intensity of fast transitions among multiple states, but it undergoes continuous dynamic switching between a low- and a high-FRET state in the presence of a target, allowing a high-confidence detection.