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Abstract Mathematical models highlighted the importance of pathogen‐mediated invasion, with the replacement of red squirrels by squirrelpox virus (SQPV) carrying grey squirrels in the UK, a well‐known example.In this study, we combine new epidemiological models, with a range of infection characteristics, with recent longitudinal field and experimental studies on the SQPV dynamics in red and grey squirrel populations to better infer the mechanistic basis of the disease interaction.A key finding is that a model with either partial immunity or waning immunity and reinfection, where individuals become seropositive on the second exposure to infection, that up to now has been shown in experimental data only, can capture the key aspects of the field study observations.By fitting to SQPV epidemic observations in isolated red squirrel populations, we can infer that SQPV transmission between red squirrels is significantly (4×) higher than the transmission between grey squirrels and as a result our model shows that disease‐mediated replacement of red squirrels by greys is considerably more rapid than replacement in the absence of SQPV.Our findings recover the key results of the previous model studies, which highlights the value of simple strategic models that are appropriate when there are limited data, but also emphasise the likely complexity of immune interactions in wildlife disease and how models can help infer disease processes from field data. 

Abstract Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay Area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision-making processes. Observation of precipitation in the complex Bay Area landscape of California’s coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow, and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels—especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and worldwide. 

Describing and measuring instructional quality of mathematics lessons is a common goal amongst mathematics education researchers. Such work takes several forms such as classifying and coding instructional moves and student activity or providing high-level rubric-based scores in relation to categories. In this work, we share an innovative mixed methods approach to analyzing lesson data that includes both a time-based classification of instruction and an overall scoring component. Using the Math Habits framework, our project team analyzed a set of 97 fourth-eighth grade mathematics lessons including overall scores. From this qualitative analysis, we developed quantitative models to predict overall scores and better understand the ways that individual codes do or do not contribute to overall lesson score characterizations. 

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mismodeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling. Published by the American Physical Society2025 

Daily oscillations in photosynthetically active radiation strongly influence the timing of metabolic processes in picocyanobacteria, but it is less clear how the light-dark cycle affects the activities of their consumers. We investigated the relationship between marine picocyanobacteria and nanoplanktonic consumers throughout the diel cycle to determine whether heterotrophic and mixotrophic protists (algae with phagotrophic ability) display significant periodicity in grazing pressure. Carbon biomass of Prochlorococcus and Synechococcus was estimated continuously from abundances and cell size measurements made by flow cytometry. Picocyanobacterial dynamics were then compared to nanoplankton abundances and ingestion of fluorescently labeled bacteria measured every 4 h during a 4 d survey in the North Pacific Subtropical Gyre. Grazing of the labeled bacteria by heterotrophic nanoplankton was significantly greater at night than during the day. The grazing activity of mixotrophic nanoplankton showed no diel periodicity, suggesting that they may feed continuously, albeit at lower rates than heterotrophic nanoplankton, to alleviate nutrient limitation in this oligotrophic environment. Diel changes in Prochlorococcus biomass indicated that they could support substantial growth of nanoplankton if those grazers are the main source of picocyanobacterial mortality, and that grazers may contribute to temporally stable abundances of picocyanobacteria. 

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low-energy ( $<3\mathrm{MeV}$) blips around fiberglass mechanical support struts along the time projection chamber edges with energy spectrum features consistent with the Compton edge of 2.614 MeV ${}^{208}\mathrm{Tl}$decay $\gamma$rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE’s data to few percent precision and to measure the specific activity of ${}^{208}\mathrm{Tl}$in the fiberglass composing these struts, $(11.7\pm 0.2\left(\mathrm{stat}\right)\pm 3.1\left(\mathrm{syst}\right))\mathrm{Bq}/\mathrm{kg}$. Cosmogenically produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated cosmic-ray simulations, suggesting either incorrect modeling of incident cosmic fluxes or particle transport modeling issues in eant4. Published by the American Physical Society2025 

We present a measurement of neutral pion production in charged-current interactions using data recorded with the MicroBooNE detector exposed to Fermilab’s booster neutrino beam. The signal comprises one muon, one neutral pion, any number of nucleons, and no charged pions. Studying neutral pion production in the MicroBooNE detector provides an opportunity to better understand neutrino-argon interactions, and is crucial for future accelerator-based neutrino oscillation experiments. Using a dataset corresponding to $6.86\times {10}^{20}$protons on target, we present single-differential cross sections in muon and neutral pion momenta, scattering angles with respect to the beam for the outgoing muon and neutral pion, as well as the opening angle between the muon and neutral pion. Data extracted cross sections are compared to generator predictions. We report good agreement between the data and the models for scattering angles, except for an over-prediction by generators at muon forward angles. Similarly, the agreement between data and the models as a function of momentum is good, except for an underprediction by generators in the medium momentum ranges, 200–400 MeV for muons and 100–200 MeV for pions. Published by the American Physical Society2024 

We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses. Published by the American Physical Society2024