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Interdisciplinarity is often hailed as a necessity for tackling real‐world challenges. We examine the prevalence and impact of interdisciplinarity in the NSF ADVANCE program, which addresses gender equity in STEM.

Through a quantitative analysis of authorship, references, and citations in ADVANCE publications, we compare the interdisciplinarity of knowledge produced within the program to traditional disciplinary knowledge. We use Simpon's Diversity Index to test for differences across disciplines, and we use negative binomial regression to capture the potential influences of interdisciplinarity on the long‐term impact of ADVANCE publications.

ADVANCE publications exhibit higher levels of interdisciplinarity across three dimensions of knowledge integration, and cross‐disciplinary ties within ADVANCE successfully integrate social science knowledge into diverse disciplines. Additionally, the interdisciplinarity of publication references positively influences the impact of ADVANCE work, while the interdisciplinarity of authorship teams does not.

These findings emphasize the significance of interdisciplinarity in problem‐oriented knowledge production, indicating that specific forms of interdisciplinarity can lead to broader impact. By shedding light on the interplay between interdisciplinary approaches, disciplinary structures, and academic recognition, this article contributes to programmatic design to generate impactful problem‐solving knowledge that also adds to the academic community.

 

Structural and functional heterogeneity is a consequence of the weak noncovalent interactions that direct the formation of organic materials from solution precursors. While covalent tethering of solution-phase assemblies provides a compelling strategy to enhance intermolecular order, the effects of this tethering strategy on the formed solid-state materials remain unestablished. This work uses pump–probe microscopy to compare excited-state dynamics in thin films fabricated from tethered perylene bisimide assemblies to those fabricated from noncovalent assemblies. On average, tethered films exhibit faster and more homogeneous excited-state lifetimes, consistent with stronger and more uniform intermolecular coupling. Optical measurements of excited-state diffusion show that the tethered film has ∼75% faster transport than the control film. Kinetic Monte Carlo modeling suggests that the reduction of site energetic disorder is sufficient to quantitatively explain the difference in diffusion coefficients. These results provide strong support that covalent tethering is a promising strategy to enhance the structural and energetic ordering in molecular materials. 

Molecular simulations of biomacromolecules that assemble into multimeric complexes remain a challenge due to computationally inaccessible length and time scales. Low-resolution and implicit-solvent coarse-grained modeling approaches using traditional nonbonded interactions (both pairwise and spherically isotropic) have been able to partially address this gap. However, these models may fail to capture the complex anisotropic interactions present at macromolecular interfaces unless higher-order interaction potentials are incorporated at the expense of the computational cost. In this work, we introduce an alternate and systematic approach to represent directional interactions at protein–protein interfaces by using virtual sites restricted to pairwise interactions. We show that virtual site interaction parameters can be optimized within a relative entropy minimization framework by using only information from known statistics between coarse-grained sites. We compare our virtual site models to traditional coarse-grained models using two case studies of multimeric protein assemblies and find that the virtual site models predict pairwise correlations with higher fidelity and, more importantly, assembly behavior that is morphologically consistent with experiments. Our study underscores the importance of anisotropic interaction representations and paves the way for more accurate yet computationally efficient coarse-grained simulations of macromolecular assembly in future research. 

Female advertisement of reproductive state and receptivity has the potential to play a large role in the mating systems of many taxa, but investigations of this phenomenon are underrepresented in the literature. North American red squirrels (Tamiasciurus hudsonicus) are highly territorial and engage in scramble competition mating, with males converging from spatially disparate territories to engage in mating chases. Given the narrow estrus window exhibited in this species, the ubiquitous use of vocalizations to advertise territory ownership, and the high synchronicity of males arriving from distant territories, we hypothesized that female vocalizations contain cues relating to their estrous state. To test this hypothesis, we examined the spectral and temporal properties of female territorial rattle vocalizations collected from females of known reproductive condition over 3 years. While we found no distinct changes associated with estrus specifically, we did identify significant changes in the spectral characteristics of rattles relating to both female body mass and reproductive state relative to parturition. To the best of our knowledge, this is the first evidence of changes in vocal characteristics associated with late pregnancy in a nonhuman mammal.

 

In the recent decade, we have seen major progress in quantifying the behaviors and the impact of scientists, resulting in a quantitative toolset capable of monitoring and predicting the career patterns of the profession. It is unclear, however, if this toolset applies to other creative domains beyond the sciences. In particular, while performance in the arts has long been difficult to quantify objectively, research suggests that professional networks and prestige of affiliations play a similar role to those observed in science, hence they can reveal patterns underlying successful careers. To test this hypothesis, here we focus on ballet, as it allows us to investigate in a quantitative fashion the interplay of individual performance, institutional prestige, and network effects. We analyze data on competition outcomes from 6363 ballet students affiliated with 1603 schools in the United States, who participated in the Youth America Grand Prix (YAGP) between 2000 and 2021. Through multiple logit models and matching experiments, we provide evidence that schools’ strategic network position bridging between communities captures social prestige and predicts the placement of students into jobs in ballet companies. This work reveals the importance of institutional prestige on career success in ballet and showcases the potential of network science approaches to provide quantitative viewpoints for the professional development of careers beyond science.

 

Airborne Doppler radar provides detailed and targeted observations of winds and precipitation in weather systems over remote or difficult-to-access regions that can help to improve scientific understanding and weather forecasts. Quality control (QC) is necessary to remove nonweather echoes from raw radar data for subsequent analysis. The complex decision-making ability of the machine learning random-forest technique is employed to create a generalized QC method for airborne radar data in convective weather systems. A manually QCed dataset was used to train the model containing data from the Electra Doppler Radar (ELDORA) in mature and developing tropical cyclones, a tornadic supercell, and a bow echo. Successful classification of ∼96% and ∼93% of weather and nonweather radar gates, respectively, in withheld testing data indicate the generalizability of the method. Dual-Doppler analysis from the genesis phase of Hurricane Ophelia (2005) using data not previously seen by the model produced a comparable wind field to that from manual QC. The framework demonstrates a proof of concept that can be applied to newer airborne Doppler radars.

Airborne Doppler radar is an invaluable tool for making detailed measurements of wind and precipitation in weather systems over remote or difficult to access regions, such as hurricanes over the ocean. Using the collected radar data depends strongly on quality control (QC) procedures to classify weather and nonweather radar echoes and to then remove the latter before subsequent analysis or assimilation into numerical weather prediction models. Prior QC techniques require interactive editing and subjective classification by trained researchers and can demand considerable time for even small amounts of data. We present a new machine learning algorithm that is trained on past QC efforts from radar experts, resulting in an accurate, fast technique with far less user input required that can greatly reduce the time required for QC. The new technique is based on the random forest, which is a machine learning model composed of decision trees, to classify weather and nonweather radar echoes. Continued efforts to build on this technique could benefit future weather forecasts by quickly and accurately quality-controlling data from other airborne radars for research or operational meteorology.

 

Proton-switchable access to seven-coordinate ONNO dicarboxamide and NNNN dicarboxamidate rhenium oxo complexes provides a platform for understanding thermodynamics and bonding in pentagonal bipyramidal complexes.