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Precise and accurate charge measurements on microdroplets are essential for understanding the role of charge in modulating microdroplet chemistry, including reaction kinetics, ion distribution, and interfacial dynamics. Despite the availability of various charge measurement techniques, existing contactless techniques either lack the sensitivity to accurately detect charges with ∼1 fC precision or lack the ability to measure charge on micron-sized particles, leaving a significant gap in the field. Here, a new technique is presented to directly measure the net charge of microdroplets exiting a quadrupole electrodynamic trap (QET) using induced charge detection. With this method, the charge droplets induce on a cylindrical electrode (Qinduced) is detected using a homebuilt charge sensitive pre-amplifier (CSP). The long time constant of the CSP (1.02 ± 0.01 s−1) facilitates accurate measurement of Qinduced on slow-moving microdroplets that interact with the detection electrode for up to 100s of ms. The new charge detection method is validated by comparing Qinduced with the charge of droplets measured using a Faraday cup (QFaraday cup) for roughly 2900 droplets with different net charges, sizes, and velocities. Regardless of droplet properties, Qinduced closely correlates with QFaraday cup with absolute differences averaging <5 fC (i.e., 1% accuracy). While the charge detection system is coupled to a QET, it could easily be adapted for other droplet-based measurements (e.g., droplet train experiments). Ultimately, the induced charge detection system presented here will support future studies exploring how charge influences the physical and chemical processing of microdroplets, such as understanding how charge can drive accelerated chemistry in microdroplets. 

Background Wolbachia bacteria of arthropods are at the forefront of basic and translational research on multipartite host-symbiont-pathogen interactions. These vertically transmitted microbes are the most widespread endosymbionts on the planet due to factors including host reproductive manipulation and fitness benefits. Importantly, some strains of Wolbachia can inhibit viral pathogenesis within and between arthropod hosts. Mosquitoes carrying the wMel Wolbachia strain of Drosophila melanogaster have a greatly reduced capacity to spread viruses like dengue and Zika to humans. While significant research efforts have focused on viruses, relatively little attention has been given to Wolbachia-fungal interactions despite the ubiquity of fungal entomopathogens in nature. Results Here, we demonstrate that Wolbachia increase the longevity of their Drosophila melanogaster hosts when challenged with a spectrum of yeast and filamentous fungal pathogens. We find that this pattern can vary based on host genotype, sex, and fungal species. Further, Wolbachia correlates with higher fertility and reduced pathogen titers during initial fungal infection, indicating a significant fitness benefit. Finally, RNA sequencing results show altered expression of many immune and stress response genes in the context of Wolbachia and fungal infection, suggesting host immunity may be involved in the mechanism. Conclusions This study demonstrates Wolbachia’s protective role in diverse fungal pathogen interactions and determines that the phenotype is broad, but with several variables that influence both the presence and strength of the phenotype. It also is a critical step forward to understanding how symbionts can protect their hosts from a variety of pathogens. 

In response to COVID-19, the CDC issued hygiene, protective equipment, and physical distancing guidelines to reduce virus transmission. Adherence was crucial for public health, particularly in the earliest stage of COVID-19, before effective treatments emerged. Still, there was wide variation in willingness and/or ability to follow the recommendations. One group that might be expected to flout rules and take risks under normal circumstances is adolescents. This developmental stage predisposes one to push boundaries and seek the company of peers. Adolescents with a history of lawbreaking might be even more inclined to disregard public health guidelines due to experiential and dispositional factors. We employed a longitudinal study launched prior to the pandemic to identify which pre-pandemic factors predict adolescents’ adherence to—or disregard for—public health guidelines during a crisis. The sample (N = 75, 30% justice-involved) came from predominantly minoritized communities in a southwestern U.S. city. Data were collected in three waves over one year. Analyses tested whether adherence varied by time period, local infection trajectories, justice involvement, pre-pandemic mental health, risk-taking, and rule orientation. Results revealed that adherence declined over time and was generally lower among justice-involved adolescents. In addition, justice-involved adolescents with higher depressive symptoms displayed lower adherence, whereas those reporting higher anxiety symptoms displayed higher adherence. Understanding these factors is crucial for developing strategies to promote adherence to public health guidelines among adolescents during public health emergencies. 

Open source software (OSS) underpins modern software infrastructure, yet many projects struggle with long- term sustainability. We introduce OSSPREY, an AI-powered platform that can predict the sustainability of any GitHub- hosted project. OSSPREY collects longitudinal socio-technical data, such as: commits, issues, and contributor interactions, and uses a transformer-based model to generate month-by-month sustainability forecasts. When project downturns are detected, it recommends evidence-based interventions drawn from published software engineering studies. OSSPREY integrates scraping, forecasting, and actionable guidance into an interactive dash- board, enabling maintainers to monitor project health, anticipate decline, and respond with targeted strategies. By connecting real- time project data with research-backed insights, OSSPREY offers a practical tool for sustaining OSS projects at scale. The codebase is linked to the project website at: https: //oss-prey.github.io/OSSPREY-Website/ The screencast is available at: https://www.youtube.com/ watch?v=N7a0v4hPylU 

Sixth-order boundary value problems (BVPs) arise in thin-film flows with a surface that has elastic bending resistance. We consider the case in which the elastic interface is clamped at the lateral walls of a closed trough and thus encloses a finite amount of fluid. For a slender film undergoing infinitesimal deformations, the displacement of the elastic surface from its initial equilibrium position obeys a sixth-order (in space) initial boundary value problem (IBVP). To solve this IBVP, we construct a set of odd and even eigenfunctions that intrinsically satisfy the boundary conditions (BCs) of the original IBVP. These eigenfunctions are the solutions of a non-self-adjoint sixth-order eigenvalue problem (EVP). To use the eigenfunctions for series expansions, we also construct and solve the adjoint EVP, leading to another set of even and odd eigenfunctions, which are orthogonal to the original set (biorthogonal). The eigenvalues of the adjoint EVP are the same as those of the original EVP, and we find accurate asymptotic formulas for them. Next, employing the biorthogonal sets of eigenfunctions, a Petrov–Galerkin spectral method for sixth-order problems is proposed, which can also handle lower-order terms in the IBVP. The proposed method is tested on two model sixth-order BVPs, which admit exact solutions. We explicitly derive all the necessary formulas for expanding the quantities that appear in the model problems into the set(s) of eigenfunctions. For both model problems, we find that the approximate Petrov–Galerkin spectral solution is in excellent agreement with the exact solution. The convergence rate of the spectral series is rapid, exceeding the expected sixth-order algebraic rate. 

We present a protocol for measuring naturalistic and normalized decision-mak- ing in humans across four contexts (approach-avoid, moral, social, and probabi- listic) using a web application. We describe steps for session setup, eye tracker calibration, and heart rate monitoring. In each session, a participant encounters a story, rates rewards and costs relevant within that context, and then evaluates various cost-reward pairings in context. For complete details on the use and execution of this protocol, please refer to Rakocevic et al.1