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Infections caused byAcinetobacter baumannii, a Gram‐negative opportunistic pathogen, are difficult to eradicate due to the bacterium's propensity to quickly gain antibiotic resistances and form biofilms, a protective bacterial multicellular community. TheA. baumanniiDNA damage response (DDR) mediates the antibiotic resistance acquisition and regulates RecA in an atypical fashion; both RecA^Lowand RecA^Highcell types are formed in response to DNA damage. The findings of this study demonstrate that the levels of RecA can influence formation and dispersal of biofilms. RecA loss results in surface attachment and prominent biofilms, while elevated RecA leads to diminished attachment and dispersal. These findings suggest that the challenge to treatA. baumanniiinfections may be explained by the induction of the DDR, common during infection, as well as the delicate balance between maintaining biofilms in low RecA cells and promoting mutagenesis and dispersal in high RecA cells. This study underscores the importance of understanding the fundamental biology of bacteria to develop more effective treatments for infections.

 

Abstract A multivariate adiabatic connection (MAC) framework for describing dispersion interactions in a system consisting of N non-overlapping monomers is presented. By constraining the density to the physical ground-state density of the supersystem, the MAC enables a rigorous separation of induction and dispersion effects. The exact dispersion energy is obtained from the zero-temperature fluctuation–dissipation theorem and partitioned into increments corresponding to the interaction energy gained when an additional monomer is added to a K -monomer system. The total dispersion energy of an N -monomer system is independent of any partitioning into subsystems. This statement of dispersion size consistency is shown to be an exact constraint. The resulting additive separability of the dispersion energy results from multiplicative separability of the generalized screening factor defined as the inverse generalized dielectric function. Many-body perturbation theory (MBPT) is found to violate dispersion size-consistency because perturbative approximations to the generalized screening factor are nonseparable; on the other hand, random phase approximation-type methods produce separable generalized screening factors and therefore preserve dispersion size-consistency. This result further explains the previously observed increase in relative errors of MBPT for dispersion interactions as the system size increases. Implications for electronic structure theory and applications to supramolecular materials and condensed matter are discussed. 

Biochemistry curricula present a particular challenge to undergraduate students with abstract concepts which can lead to misconceptions that impede learning. In particular, these students have difficulty understanding enzyme structure and function concepts. Targeted learning activities and three‐dimensional (3D) physical models are proposed to help students challenge these misconceptions and increase conceptual understanding. Here we assessed such pedagogical tools using the Enzyme‐Substrate Interactions Concept Inventory (ESICI) to measure (mis)conceptual changes from Pre‐ to Post‐ time points in a single semester undergraduate biochemistry course. A Control group of students engaged with the active learning activities without the 3D physical models and students in the Intervention group utilized these activities with the 3D physical models. At the Post‐ time point both groups had higher, yet similar ESICI scores of the same magnitude as the highest scoring group from the national sample. Concomitantly, many misconception markers decreased compared to the national sample, although some of these differed between the Control and Intervention groups. Based on this assessment, both pedagogical approaches successfully increased conceptual understanding and targeted many of the misconceptions measured by the ESICI, however, several misconceptions persisted. Surprisingly, the students who used the 3D physical models did not demonstrate a further decrease in the misconception markers. Additionally, psychometric evaluation of the ESICI with our sample recommends the revision of several questions to improve the validity of this assessment. We also offer suggestions to improve instruction and pedagogical tools with further avenues for research on learning.