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The bacterial wilt pathogenRalstonia pseudosolanacearum (Rps)colonizes plant xylem vessels and blocks the flow of xylem sap by its biofilm (comprising of bacterial cells and extracellular material), resulting in devastating wilt disease across many economically important host plants including tomatoes. The technical challenges of imaging the xylem environment, along with the use of artificial cell culture plates and media in existingin vitrosystems, limit the understanding ofRpsbiofilm formation and its infection dynamics. In this study, we designed and built a microfluidic system that mimicked the physical and chemical conditions of the tomato xylem vessels, and allowed us to dissectRpsresponses to different xylem-like conditions. The system, incorporating functional surface coatings of carboxymethyl cellulose-dopamine, provided a bioactive environment that significantly enhancedRpsattachment and biofilm formation in the presence of tomato xylem sap. Using computational approaches, we confirmed thatRpsexperienced linear increasing drag forces in xylem-mimicking channels at higher flow rates. Consistently, attachment and biofilm assays conducted in our microfluidic system revealed that both seeding time and flow rates were critical for bacterial adhesion to surface and biofilm formation inside the channels. These findings provided insights into theRpsattachment and biofilm formation processes, contributing to a better understanding of plant-pathogen interactions during wilt disease development. 

Abstract Wildfires emit large amounts of black carbon and light-absorbing organic carbon, known as brown carbon, into the atmosphere. These particles perturb Earth’s radiation budget through absorption of incoming shortwave radiation. It is generally thought that brown carbon loses its absorptivity after emission in the atmosphere due to sunlight-driven photochemical bleaching. Consequently, the atmospheric warming effect exerted by brown carbon remains highly variable and poorly represented in climate models compared with that of the relatively nonreactive black carbon. Given that wildfires are predicted to increase globally in the coming decades, it is increasingly important to quantify these radiative impacts. Here we present measurements of ensemble-scale and particle-scale shortwave absorption in smoke plumes from wildfires in the western United States. We find that a type of dark brown carbon contributes three-quarters of the short visible light absorption and half of the long visible light absorption. This strongly absorbing organic aerosol species is water insoluble, resists daytime photobleaching and increases in absorptivity with night-time atmospheric processing. Our findings suggest that parameterizations of brown carbon in climate models need to be revised to improve the estimation of smoke aerosol radiative forcing and associated warming. 

The authors showcase the potential of symbolic regression as an analytic method for use in materials research. First, the authors briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science. The authors then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson–Mehl–Avrami–Kolmogorov form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO 3 . Finally, the authors propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine learning-based regression models for learning from data.