Search for: All records

Abstract BackgroundPrescribed fire is an essential tool employed by natural resource managers to serve ecological and fuel treatment objectives of fire management. However, limited operational resources, environmental conditions, and competing goals result in a finite number of burn days, which need to be allocated toward maximizing the overall benefits attainable with fire management. Burn prioritization models must balance multiple management objectives at landscape scales, often providing coarse resolution information. We developed a decision-support framework and a burn prioritization model for wetlands and wildland-urban interfaces using high-resolution mapping in Everglades National Park (Florida, USA). The model included criteria relevant to the conservation of plant communities, the protection of endangered faunal species, the ability to safely contain fires and minimize emissions harmful to the public, the protection of cultural, archeological, and recreational resources, and the control of invasive plant species. A geographic information system was used to integrate the multiple factors affecting fire management into a single spatially and temporally explicit management model, which provided a quantitative computations-alternative to decision making that is usually based on qualitative assessments. ResultsOur model outputs were 50-m resolution grid maps showing burn prioritization scores for each pixel. During the 50 years of simulated burn unit prioritization used for model evaluation, the mean burned surface corresponded to 256 ± 160 km2 y−1, which is 12% of the total area within Everglades National Park eligible for prescribed fires. Mean predicted fire return intervals (FRIs) varied among ecosystem types: marshes (9.9 ± 1.7 years), prairies (7.3 ± 1.9 years), and pine rocklands (4.0 ± 0.7 years). Mean predicted FRIs also varied among the critical habitats for species of special concern:Ammodramus maritimus mirabilis(7.4 ± 1.5 years),Anaea troglodyta floridalisandStrymon acis bartramibutterflies (3.9 ± 0.2 years), andEumops floridanus(6.5 ± 2.9 years). While mean predicted fire return intervals accurately fit conservation objectives, baseline fire return intervals, calculated using the last 20 years of data, did not. Fire intensity and patchiness potential indices were estimated to further support fire management. ConclusionsBy performing finer-scale spatial computations, our burn prioritization model can support diverse fire regimes across large wetland landscape such as Everglades National Park. Our model integrates spatial variability in ecosystem types and habitats of endangered species, while satisfying the need to contain fires and protect cultural heritage and infrastructure. Burn prioritization models can allow the achievement of target fire return intervals for higher-priority conservation objectives, while also considering finer-scale fire characteristics, such as patchiness, seasonality, intensity, and severity. Decision-support frameworks and higher-resolution models are needed for managing landscape-scale complexity of fires given rapid environmental changes. 

The Colorado potato beetle (CPB) is the primary defoliator of potatoes and is notorious for its ability to develop resistance to various insecticides. This remarkable adaptability may partly reflect selective pressures imposed due to the beetle’s coevolution with toxic Solanaceous host plants. As the initial interface between the environment and the insect olfactory system, odorant-binding proteins (OBPs) may sequester excess harmful molecules, such as insecticides and plant allelochemicals, in the perireceptor space, mitigating deleterious effects on vulnerable olfactory sensory neuronal dendrites. In this study, we identified an antenna-specific OBP (LdecOBP33) that is significantly upregulated in a pesticide resistant strain compared to a susceptible one. Competitive displacement fluorescence binding assays demonstrated that the LdecOBP33 protein exhibited broad affinity toward a range of plant volatiles and insecticides. Silencing LdecOBP33 decreased the beetle’s resistance to imidacloprid and impaired its ability to locate host plants. Together, these findings provide insight into a key molecular factor involved in the CPB’s response to environmental challenges, suggesting a potential link between insects’ adaptation to xenobiotics and their olfactory processing. 

Abstract Localized tropical rainfall changes commonly occur on 500–1,000 km scales under various climate forcings, but understanding their causality remains challenging. One helpful process‐oriented diagnostic (POD) decomposes the effects of undilute buoyancy and lower free‐tropospheric moisture through a precipitation‐buoyancy relationship, but its applicability at subregional scales is uncertain. We examine month‐to‐month rainfall changes in five South Asian monsoon subregions. The POD accurately characterizes the precipitation‐buoyancy relationship across all subregions and successfully predicts the sign of rainfall changes in four out of five subregions. However, the POD's ability to predict rainfall change magnitudes and identify causal mechanisms varies, providing confident explanations in only two subregions, where lower free‐tropospheric moisture emerges as the dominant driver of change. While these findings demonstrate the POD's utility in specific contexts, they also reveal limitations. We caution against using the POD as a standalone tool at these scales for predicting rainfall changes or decomposing their drivers. 

The measurement of neutralizing immune responses to viral infection is essential, given the heterogeneity of human immunity and the emergence of new virus strains. However, neutralizing antibody (nAb) assays often require high-level biosafety containment, sophisticated instrumentation, and long detection times. Here, as a proof-of-principle, we designed a nanoparticle-supported, rapid, electronic detection (NasRED) assay to assess the neutralizing potency of monoclonal antibodies (mAbs) against SARS-CoV-2. The gold nanoparticles (AuNPs) coated with human angiotensin-converting enzyme 2 (ACE2) protein as nAb potency reporters were mixed with the mAbs to be tested, as well as streptavidin-conjugated multivalent spike (S) protein or their receptor binding domains (RBD). High-affinity and ACE2-competitive nAbs alter the S (or RBD)-to-ACE2 binding level and modulate AuNP cluster formation and precipitation. The amount of free-floating AuNP reporters is quantified by a semiconductor-based readout system that measures the AuNPs' optical extinction, producing nAb signals that can differentiate SARS-CoV-2 variants (Wuhan-Hu-1, Gamma, and Omicron). The modular design nature, short assay time (less than 30 minutes), and portable and inexpensive readout system make this NasRED-nAb assay applicable to measuring vaccine potency, immune responses to infection, and the efficacy of antibody-based therapies. 

Online marketplaces use rating systems to promote the discovery of high-quality products. However, these systems also lead to high variance in producers' economic outcomes: a new producer who sells high-quality items, may unluckily receive a low rating early, severely impacting their future popularity. We investigate the design of rating systems that balance the goals of identifying high-quality products (``efficiency'') and minimizing the variance in outcomes of producers of similar quality (individual ``producer fairness'').We show that there is a trade-off between these two goals: rating systems that promote efficiency are necessarily less individually fair to producers. We introduce prior-weighted rating systems as an approach to managing this trade-off. Informally, the system we propose sets a system-wide prior for the quality of an incoming product; subsequently, the system updates that prior to a posterior for each product's quality based on user-generated ratings over time. We show theoretically that in markets where products accrue reviews at an equal rate, the strength of the rating system's prior determines the operating point on the identified trade-off: the stronger the prior, the more the marketplace discounts early ratings data (increasing individual fairness), but the slower the platform is in learning about true item quality (so efficiency suffers). We further analyze this trade-off in a responsive market where customers make decisions based on historical ratings. Through calibrated simulations in 19 different real-world datasets sourced from large online platforms, we show that the choice of prior strength mediates the same efficiency-consistency trade-off in this setting. Overall, we demonstrate that by tuning the prior as a design choice in a prior-weighted rating system, platforms can be intentional about the balance between efficiency and producer fairness. 

Introductory computer science courses for non-majors (CS0) aim to increase diversity and highlight the relevance of computing across disciplines. To enhance the accessibility and engagement of CS0, researchers have explored contextualized computing, where computing is integrated with another subject, to teach course content. While research has explored various designs for contextualized courses, we know less about how contextualized computing tasks impact students’ learning experiences. Through the lens of metacognition and affect, we conducted a secondary qualitative analysis on daily diary and retrospective interview data from 20 students in a CS0 course that applied coding to different contexts. Our findings demonstrate that students’ feeling of knowing and their perception of the task are two central themes that shape their affect and interest in the course. We conclude with design suggestions for contextualized computing in CS0 to better support students.