Animals continuously evaluate sensory information to decide on their next action. Different sensory cues, however, often demand opposing behavioral responses. How does the brain process conflicting sensory information during decision making? Here, we show that flies use neural substrates attributed to odor learning and memory, including the mushroom body (MB), for immediate sensory integration and modulation of innate behavior. Drosophila melanogaster must integrate contradictory sensory information during feeding on fermenting fruit that releases both food odor and the innately aversive odor CO2. Here, using this framework, we examine the neural basis for this integration. We have identified a local circuit consisting of specific glutamatergic output and PAM dopaminergic input neurons with overlapping innervation in the MB-β'2 lobe region, which integrates food odor and suppresses innate avoidance. Activation of food odor-responsive dopaminergic neurons reduces innate avoidance mediated by CO2-responsive MB output neurons. We hypothesize that the MB, in addition to its long recognized role in learning and memory, serves as the insect's brain center for immediate sensory integration during instantaneous decision making.

For U.S. AYA without identified risk factors, a one-time routine HIV screen at age 25, after the peak of incidence, would optimize clinical outcomes and be cost-effective compared with current U.S. screening practices. Focusing screening on AYA ages 18 or younger is a less efficient use of a one-time screen among AYA than screening at a later age.

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Background and objectiveSimulation models can project effects of tobacco use and cessation and inform tobacco control policies. Most existing tobacco models do not explicitly include relapse, a key component of the natural history of tobacco use. Our objective was to develop, calibrate and validate a novel individual-level microsimulation model that would explicitly include smoking relapse and project cigarette smoking behaviours and associated mortality risks.MethodsWe developed the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) model, in which individuals transition monthly between tobacco use states (current/former/never) depending on rates of initiation, cessation and relapse. Simulated individuals face tobacco use-stratified mortality risks. For US women and men, we conducted cross-validation with a Cancer Intervention and Surveillance Modeling Network (CISNET) model. We then incorporated smoking relapse and calibrated cessation rates to reflect the difference between a transient quit attempt and sustained abstinence. We performed external validation with the National Health Interview Survey (NHIS) and the linked National Death Index. Comparisons were based on root-mean-square error (RMSE).ResultsIn cross-validation, STOP-generated projections of current/former/never smoking prevalence fit CISNET-projected data well (coefficient of variation (CV)-RMSE≤15%). After incorporating smoking relapse, multiplying the CISNET-reported cessation rates for women/men by 7.75/7.25, to reflect the ratio of quit attempts to sustained abstinence, resulted in the best approximation to CISNET-reported smoking prevalence (CV-RMSE 2%/3%). In external validation using these new multipliers, STOP-generated cumulative mortality curves for 20-year-old current smokers and never smokers each had CV-RMSE ≤1% compared with NHIS. In simulating those surveyed by NHIS in 1997, the STOP-projected prevalence of current/former/never smokers annually (1998–2009) was similar to that reported by NHIS (CV-RMSE 12%).ConclusionsThe STOP model, with relapse included, performed well when validated to US smoking prevalence and mortality. STOP provides a flexible framework for policy-relevant analysis of tobacco and nicotine product use.