Norwood Lemming (rollbadger0)

Clostridiodes (Clostridium) difficile is an anaerobic Gram-positive, spore-forming nosocomial, gastrointestinal pathogen causing C. difficile-associated disease with symptoms ranging from mild cases of antibiotic-associated diarrhea to fatal pseudomembranous colitis. We developed murine monoclonal antibodies (mAbs) specific for a conserved cell surface antigen, lipoteichoic acid (LTA)of C. difficile. The mAbs were characterized in terms of their thermal stability, solubility, and their binding to LTA by surface plasmon resonance and competitive ELISA. Synthetic LTA molecules were prepared in order to better define the minimum epitope required to mimic the natural antigen, and three repeat units of the polymer were required for optimal recognition. One of the murine mAbs was chimerized with human constant region domains and was found to recognize the target antigen identically to the mouse version. These mAbs may be useful as therapeutics (standalone, in conjunction with known antitoxin approaches, or as delivery vehicles for antibody drug conjugates targeting the bacterium), as diagnostic agents, and in infection control applications.The physical chemistry mechanisms behind the oil-brine interface phenomena are not yet fully clarified. The knowledge of the relation between brine composition and concentration for a given oil may lead to the ionic tuning of the injected solution on geochemical and enhanced oil recovery processes. Thus, it is worth examining the parameters influencing the interfacial properties. In this context, we have combined machine learning (ML) techniques with classical molecular dynamics simulations (MD) to predict oil/brine interfacial tensions (IFT) effectively and compared this process to a linear regression (LR) method. To diversify our data set, we have introduced a new atomistic crude oil model (medium) with 36 different types of hydrocarbon molecules. The MD simulations were performed for mono- and multicomponent (toluene, heptane, Heptol, light, and medium) oil systems interfaced with sulfate and chloride brines with varying cations (Na+, K+, Ca2+, and Mg2+) and salinity concentration. Thus, a consistent IFT data set was built for the ML training and LR fitting at room temperature and pressure conditions, over the feature space considering oil density, oil composition, salinity, and ionic concentrations. On the basis of gradient boosted (GB) algorithms, we have observed that the dominant quantities affecting the IFT are related to the oil attributes and the salinity concentration, and no specific ion dominates the IFT changes. When the obtained LR model was validated against MD and experimental data from the literature, the error varied up to 2% and 9%, respectively, showing a robust and consistent transferability. The combination of MD simulations and ML techniques may provide a fast and cost-effective IFT determination over multiple and complex fluid-fluid and fluid-solid interfaces.Nanostructuring and defect engineering are increasingly employed as processing strategies for thermoelectric performance enhancement, and special attention has been paid to nanostructured interfaces and dislocations that can effectively scatter low- and mid-frequency phonons. This work demonstrated that their combination was realized in Fe2O3-dispersed tetrahedrite (Cu12Sb4S13) nanocomposites, leading to significantly reduced thermal conductivities around 0.9 W m-1 K-1 at all temperatures and hence a high ZT value of ∼1.0, which increases by ∼33% compared with that of the matrix. The plausible enhancement mechanisms have been analyzed with an emphasis on the incorporation of magnetic γ-Fe2O3 nanoparticles (NPs) into Cu11.5Ni0.5Sb4S13, leading to various nanostructures (NPs, nanoprecipitates, and nanotwins) and dislocations. A calculated efficiency of ∼9.3% and an average ZT of 0.63 also reveal the potential application of tetrahedrite at medium temperatures. Additionally, the mechanical properties are improved becau