Breast Cancer Knowledge Among Amish and Mennonite Women.

Breast cancer is the most common cancer diagnosis for women in the USA and ranks second in cancer-related deaths. Disproportionately higher breast cancer rates can be found in rural and Appalachian regions due to several social drivers of health, including poverty, access to healthcare, and lack of culturally sensitive health education. Amish and Mennonite communities, religious groups with distinct cultural practices and beliefs, experience lower mammography screening and higher breast cancer mortality rates (among Amish women). This study focuses on knowledge about breast cancer and causes of cancer among Amish and Mennonite women. A total of 473 women participated in the study at 26 separate women's health clinics throughout Ohio, consisting of 348 Amish and 121 Mennonite women, the largest study conducted on breast cancer knowledge spanning dozens of communities. Statistically significant differences were found in total knowledge scores between Amish and Mennonite women (rpb = .178, n = 466, p = .007), with Amish women having lower scores and stronger beliefs in myths associated with breast cancer cause and symptoms (χ(1) = 7.558, p = .006). Both groups often provided scientifically accurate descriptions of cancer etiology. The majority of participants underestimated breast cancer risk, highlighting the need for culturally appropriate health education programs that consider numeracy and health literacy. By implementing targeted interventions and fostering partnerships with community stakeholders using a multifaceted approach that incorporates cultural sensitivity, community engagement, and collaboration, significant progress can be made towards reducing breast cancer disparities and improving health outcomes.

Meet the authors: Georgios Rizos, Jenna L. Lawson, and Björn W. Schuller.

In their recent publication in Patterns, the authors proposed a methodology based on sample-free Bayesian neural networks and label smoothing to improve both predictive and calibration performance on animal call detection. Such approaches have the potential to foster trust in algorithmic decision making and enhance policy making in applications about conservation using recordings made by on-site passive acoustic monitoring equipment. This interview is a companion to these authors' recent paper, "Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing".

Propagating variational model uncertainty for bioacoustic call label smoothing.

Along with propagating the input toward making a prediction, Bayesian neural networks also propagate uncertainty. This has the potential to guide the training process by rejecting predictions of low confidence, and recent variational Bayesian methods can do so without Monte Carlo sampling of weights. Here, we apply sample-free methods for wildlife call detection on recordings made via passive acoustic monitoring equipment in the animals' natural habitats. We further propose uncertainty-aware label smoothing, where the smoothing probability is dependent on sample-free predictive uncertainty, in order to downweigh data samples that should contribute less to the loss value. We introduce a bioacoustic dataset recorded in Malaysian Borneo, containing overlapping calls from 30 species. On that dataset, our proposed method achieves an absolute percentage improvement of around 1.5 points on area under the receiver operating characteristic (AU-ROC), 13 points in F1, and 19.5 points in expected calibration error (ECE) compared to the point-estimate network baseline averaged across all target classes.

Automated acoustic detection of Geoffroy's spider monkey highlights tipping points of human disturbance.

As more land is altered by human activity and more species become at risk of extinction, it is essential that we understand the requirements for conserving threatened species across human-modified landscapes. Owing to their rarity and often sparse distributions, threatened species can be difficult to study and efficient methods to sample them across wide temporal and spatial scales have been lacking. Passive acoustic monitoring (PAM) is increasingly recognized as an efficient method for collecting data on vocal species; however, the development of automated species detectors required to analyse large amounts of acoustic data is not keeping pace. Here, we collected 35 805 h of acoustic data across 341 sites in a region over 1000 km2 to show that PAM, together with a newly developed automated detector, is able to successfully detect the endangered Geoffroy's spider monkey (Ateles geoffroyi), allowing us to show that Geoffroy's spider monkey was absent below a threshold of 80% forest cover and within 1 km of primary paved roads and occurred equally in old growth and secondary forests. We discuss how this methodology circumvents many of the existing issues in traditional sampling methods and can be highly successful in the study of vocally rare or threatened species. Our results provide tools and knowledge for setting targets and developing conservation strategies for the protection of Geoffroy's spider monkey.