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Migratory locusts (Locusta migratoria) emit two key odorants during aggregation: 4-vinylanisole (4VA), which serves as an aggregation pheromone attracting conspecifics to form swarms, and phenylacetonitrile (PAN), which acts as an aposematic signal and a precursor of a defense toxin, deterring conspecifics from cannibalism and protecting against predators. However, how locusts reconcile these two conflicting olfactory signals while aggregating is not yet understood. Our study addresses this by examining the release dynamics of the two signals, their behavioral effects, and the neural mechanisms underlying their perception. 4VA is released earlier and at lower locust densities than PAN, with PAN’s release increasing as aggregation progresses. Although PAN’s emission levels eventually exceed those of 4VA, locusts consistently exhibit a preference for the emitted blend, regardless of variations in proportions and concentrations. Notably, increasing amounts of 4VA added to PAN can counteract PAN’s repellent effects, but this is not the case when PAN is added to 4VA. Mechanistically, we found that antennal neurons responsive to 4VA suppress the activity of neurons responsive to PAN. In the antennal lobe, it is the conduction velocities of projection neurons, rather than other neural properties, that are responsible for the observed behavioral pattern, leading to an overall attractive response. Collectively, our findings imply that insects are capable of harmonizing the effects of two distinct pheromones to optimize both social cohesion and chemical defense. 

For large observational studies lacking a control group (unlike randomized controlled trials, RCT), propensity scores (PS) are often the method of choice to account for pre-treatment confounding in baseline characteristics, and thereby avoid substantial bias in treatment estimation. A vast majority of PS techniques focus on average treatment effect estimation, without any clear consensus on how to account for confounders, especially in a multiple treatment setting. Furthermore, for time-to event outcomes, the analytical framework is further complicated in presence of high censoring rates (sometimes, due to non-susceptibility of study units to a disease), imbalance between treatment groups, and clustered nature of the data (where, survival outcomes appear in groups). Motivated by a right-censored kidney transplantation dataset derived from the United Network of Organ Sharing (UNOS), we investigate and compare two recent promising PS procedures, (a) the generalized boosted model (GBM), and (b) the covariate-balancing propensity score (CBPS), in an attempt to decouple the causal effects of treatments (here, study subgroups, such as hepatitis C virus (HCV) positive/negative donors, and positive/negative recipients) on time to death of kidney recipients due to kidney failure, post transplantation. For estimation, we employ a 2-step procedure which addresses various complexities observed in the UNOS database within a unified paradigm. First, to adjust for the large number of confounders on the multiple sub-groups, we fit multinomial PS models via procedures (a) and (b). In the next stage, the estimated PS is incorporated into the likelihood of a semi-parametric cure rate Cox proportional hazard frailty model via inverse probability of treatment weighting, adjusted for multi-center clustering and excess censoring, Our data analysis reveals a more informative and superior performance of the full model in terms of treatment effect estimation, over sub-models that relaxes the various features of the event time dataset.