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Adeno-associated viruses (AAVs) are a leading vector for gene therapy, yet their clinical utility is limited by the lack of robust quality control methods to distinguish between empty (AAVempty), partially loaded (AAVpartial), and fully DNA loaded (AAVfull) capsids. Current analytical techniques provide partial insights but remain limited in sensitivity, throughput, or resolution. Here we present a multimodal plasmonic nanopore sensor that integrates optical trapping with electrical resistive-pulse sensing to characterize AAV9 capsids at the single-particle level in tens of μL sample volumes and fM range concentrations. As a model system, we employed AAV9 capsids not loaded with DNA, capsids loaded with a self-complementary 4.7 kbp DNA (AAVscDNA), and ones loaded with single-stranded 4.7 kbp DNA (AAVssDNA). Ground-truth validation was performed with analytical ultracentrifugation (AUC). Nanosensor data were acquired concurrently for optical step changes (occurring at AAV trapping and un-trapping) both in transmittance and reflectance geometries, and electrical nanopore resistive pulse signatures, making for a total of five data dimensions. The acquired data was then filtered and clustered by Gaussian mixture models (GMMs), accompanied by spectral clustering stability analysis, to successfully separate between AAV species based on their DNA load status (AAVempty, AAVpartial, AAVfull) and DNA load type (AAVscDNA versus AAVssDNA). The motivation for quantifying the AAVempty and AAVpartial population fractions is that they reduce treatment efficacy and increase immunogenicity. Likewise, the motivation to identify AAVscDNA population fractions is that these have much higher transfection rates. Importantly, the results showed that the nanosensor could differentiate between AAVscDNA and AAVssDNA despite their identical masses. In contrast, AUC could not differentiate between AAVscDNA and AAVssDNA. An equimolar mixture of AAVscDNA, AAVssDNA and AAVempty was also measured with the sensor, and the results showed the expected population fractions, supporting the capacity of the method to differentiate AAV load status in heterogeneous solutions. In addition, less common optical and electrical signal signatures were identified in the acquired data, which were attributed to debris, rapid entry re-entry to the optical trap, or weak optical trap exits, representing critical artifacts to recognize for correct interpretation of the data. Together, these findings establish plasmonic nanopore sensing as a promising platform for quantifying AAV DNA loading status and genome type with the potential to extend ultra-sensitive single-particle characterization beyond the capabilities of existing methods. 

Leadership is evolving dynamically from an individual endeavor to shared efforts. This paper aims to advance our understanding of shared leadership in scientific teams. We define three kinds of leaders, junior (10–15), mid (15–20), and senior (20+) based on career age. By considering the combinations of any two leaders, we distinguish shared leadership as “heterogeneous” when leaders are in different age cohorts and “homogeneous” when leaders are in the same age cohort. Drawing on 1,845,351 CS, 254,039 Sociology, and 193,338 Business teams with two leaders in the OpenAlex dataset, we identify that heterogeneous shared leadership brings higher citation impact for teams than homogeneous shared leadership. Specifically, when junior leaders are paired with senior leaders, it significantly increases team citation ranking by 1–2 %, in comparison with two leaders of similar age. We explore the patterns between homogeneous leaders and heterogeneous leaders from team scale, expertise composition, and knowledge recency perspectives. Compared with homogeneous leaders, heterogeneous leaders are more impactful in large teams, have more diverse expertise, and trace both the newest and oldest references. 

IntroductionDisagreements between people on different sides of popular issues in STEM are often rooted in differences in “mental models,” which include both rational and emotional cognitive associations about the issue; especially given these issues are systemic in nature. MethodsIn the research described here, we employ the fuzzy cognitive mapping software MentalModeler (developed by one of the authors)¹as a tool for articulating implicit and explicit assumptions about one’s knowledge of both the environmental and social science and values underpinning complex system related issues. More specifically, we test the assumption that this pedagogical approach will foster certain aspects of perspective taking that can be traced with cognitive development and systems thinking as students not only articulate their own understanding of an issue, but also articulate the view of others. Results and discussionResults are discussed with respect to systems thinking that is developed through this type of modeling. 

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Systems thinking (ST) skills are often the foundation of sustainability science curricula. Though ST skill sets are used as a basic approach to reasoning about complex environmental problems, there are gaps in our understanding regarding the best ways to promote and assess ST learning in classrooms. Since ST learning provides Science, Technology, Engineering, and Mathematics (STEM) students’ important skills and awareness to participate in environmental problem-solving, addressing these gaps is an important STEM learning contribution. We have created guidelines for teaching and measuring ST skills derived from a hybrid of a literature review and through case study data collection. Our approach is based on semi-quantitative cognitive mapping techniques meant to support deep reasoning about the complexities of social–ecological issues. We begin by arguing that ST should be evaluated on a continuum of understanding rather than a binary of correct/incorrect or present/absent. We then suggest four fundamental dimensions of teaching and evaluating ST which include: (1) system structure, (2) system function, (3) identification of leverage points for change, and (4) trade-off analysis. Finally, we use a case study to show how these ideas can be assessed through cognitive maps to help students develop deep system understanding and the capacity to propose innovative solutions to sustainability problems.