McCarthy Johannesen (sailorhead27)

Sensitivity analysis indicates that small changes to water-channel interactions can alter the free energy barrier for ion permeation. These results, illustrating polarization effects present in the complex electrostatic environment of the gA channel, have broad implications for revising proposed mechanisms of ion permeation and selectivity in a variety of ion channels.Protein design has received much attention in the last decades. With an additional disulfide bond to enhance the protein stability, human A15C neuroglobin (Ngb) is an ideal protein scaffold for heme enzyme design. In this study, we rationally converted A15C Ngb into a multifunctional peroxidase by replacing the heme axial His64 with an Asp residue, where Asp64 and the native Lys67 at the heme distal site were proposed to act as an acid-base catalytic couple for H2O2 activation. Kinetic studies showed that the catalytic efficiency of A15C/H64D Ngb was much higher (∼50-80-fold) than that of native dehaloperoxidase, which even exceeds (∼3-fold) that of the most efficient native horseradish peroxidase. Moreover, the dye-decolorizing peroxidase activity was also comparable to that of some native enzymes. Electron paramagnetic resonance, molecular docking, and isothermal titration calorimetry studies provided valuable information for the substrate-protein interactions. Therefore, this study presents the rational design of an efficient multifunctional peroxidase based on Ngb with potential applications such as in bioremediation for environmental sustainability.Land-use regression (LUR) models are frequently applied to estimate spatial patterns of air pollution. Traditional LUR often relies on fixed-site measurements and GIS-derived variables with limited spatial resolution. We present an approach that leverages Google Street View (GSV) imagery to predict street-level particulate air pollution (i.e., black carbon [BC] and particle number [PN] concentrations). We developed empirical models based on mobile monitoring data and features extracted from ∼52 500 GSV images using a deep learning model. We tested theory- and data-driven feature selection methods as well as models using images within varying buffer sizes (50-2000 m). Compared to LUR models with traditional variables, our models achieved similar model performance using the street-level predictors while also identifying additional potential hotspots. Adjusted R2 (10-fold CV R2) with integrated feature selection was 0.57-0.64 (0.50-0.57) and 0.65-0.73 (0.61-0.66) for BC and PN models, respectively. Models using only features near the measurement locations (i.e., GSV images within 250 m) explained ∼50% of air pollution variability, indicating PN and BC are strongly affected by the street-level built environment. Our results suggest that GSV imagery, processed with computer vision techniques, is a promising data source to develop LUR models with high spatial resolution and consistent predictor variables across administrative boundaries.Although several molecular-based studies have demonstrated the involvement of ammonia-oxidizing archaea (AOA) in ammonia oxidation in wastewater treatment plants (WWTPs), factors affecting the persistence and growth of AOA in these engineered systems have not been resolved. Here, we show a seasonal prevalence of AOA in a full-scale WWTP (Shatin, Hong Kong SAR) over a 6-year period of observation, even outnumbering ammonia-oxidizing bacteria in the seasonal peaks in 3 years, which may be due to the high bioavailable copper concentrations. Comparative analysis of three metagenome-assembled genomes of group I.1a AOA obtained from the activated sludge and 16S rRNA gene sequences recovered from marine sediments suggested that the seawater used for toilet flushing was the primary source of the WWTP AOA. A rare AOA population in the estuarine source water became transiently abundant in the WWTP with a metagenome-based relative abundance of up to 1.3% over three seasons of observation. Correla