Animals use varied acoustic signals that play critical roles in their lives. Understanding the function of these signals may inform about key life-history processes relevant for conservation. In the case of fin whales (Balaenoptera physalus), that produce different call types associated with different behaviours, several hypotheses have emerged regarding call function, but the topic still remains in its infancy. Here, we investigate the potential function of two fin whale vocalizations, the song-forming 20-Hz call and the 40-Hz call, by examining their production in relation to season, year and prey biomass. Our results showed that the production of 20-Hz calls was strongly influenced by season, with a clear peak during the breeding months, and secondarily by year, likely due to changes in whale abundance. These results support the reproductive function of the 20-Hz song used as an acoustic display. Conversely, season and year had no effect on variation in 40-Hz calling rates, but prey biomass did. This is the first study linking 40-Hz call activity to prey biomass, supporting the previously suggested food-associated function of this call. Understanding the functions of animal signals can help identifying functional habitats and predict the negative effects of human activities with important implications for conservation.

Background: To construct a model based on magnetic resonance imaging (MRI) features and histological and clinical variables for the prediction of pathology-detected extracapsular extension (pECE) in patients with prostate cancer (PCa). Methods: We performed a prospective 3 T MRI study comparing the clinical and MRI data on pECE obtained from patients treated using robotic-assisted radical prostatectomy (RARP) at our institution. The covariates under consideration were prostate-specific antigen (PSA) levels, the patient’s age, prostate volume, and MRI interpretative features for predicting pECE based on the Prostate Imaging–Reporting and Data System (PI-RADS) version 2.0 (v2), as well as tumor capsular contact length (TCCL), length of the index lesion, and prostate biopsy Gleason score (GS). Univariable and multivariable logistic regression models were applied to explore the statistical associations and construct the model. We also recruited an additional set of participants—which included 59 patients from external institutions—to validate the model. Results: The study participants included 185 patients who had undergone RARP at our institution, 26% of whom were pECE+ (i.e., pECE positive). Significant predictors of pECE+ were TCCL, capsular disruption, measurable ECE on MRI, and a GS of ≥7(4 + 3) on a prostate biopsy. The strongest predictor of pECE+ is measurable ECE on MRI, and in its absence, a combination of TCCL and prostate biopsy GS was significantly effective for detecting the patient’s risk of being pECE+. Our predictive model showed a satisfactory performance at distinguishing between patients with pECE+ and patients with pECE−, with an area under the ROC curve (AUC) of 0.90 (86.0–95.8%), high sensitivity (86%), and moderate specificity (70%). Conclusions: Our predictive model, based on consistent MRI features (i.e., measurable ECE and TCCL) and a prostate biopsy GS, has satisfactory performance and sufficiently high sensitivity for predicting pECE+. Hence, the model could be a valuable tool for surgeons planning preoperative nerve sparing, as it would reduce positive surgical margins.