Indirect reference interval estimation using a convolutional neural network with application to cancer antigen 125.

Indirect methods for reference interval (RI) estimation, which use data acquired from routine pathology testing, have the potential to accelerate the establishment of RIs to account for variables such as gender and age to improve clinical assessments. However, they require more sophisticated methods of analysis due to the potential influence of pathological patients in raw clinical datasets. In this paper we develop a novel convolutional neural network (CNN) model trained on synthetic data to identify underlying healthy distributions within pathological admixtures.

Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.

Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics and metabarcoding to measure forest recovery post-agriculture in a global biodiversity hotspot in Ecuador.

Unsupervised machine learning method for indirect estimation of reference intervals for chronic kidney disease in the Puerto Rican population.

Reference intervals (RIs) for clinical laboratory values are extremely important for diagnostics and treatment of patients. However, the determination of these ranges is costly and time-consuming. As a result, often different unverified RIs are used in practice for the same analyte and the same range is used for all patients despite evidence that the values are gender, age, and ethnicity dependent.

An in silico model of LINE-1-mediated neoplastic evolution.

Motivation: Recent research has uncovered roles for transposable elements (TEs) in multiple evolutionary processes, ranging from somatic evolution in cancer to putatively adaptive germline evolution across species. Most models of TE population dynamics, however, have not incorporated actual genome sequence data. The effect of site integration preferences of specific TEs on evolutionary outcomes and the effects of different selection regimes on TE dynamics in a specific genome are unknown.

Species-level classification of beaked whale echolocation signals detected in the northern Gulf of Mexico.

This study presents and evaluates several methods for automated species-level classification of echolocation clicks from three beaked whale species recorded in the northern Gulf of Mexico. The species included are Cuvier's and Gervais' beaked whales, as well as an unknown species denoted Beaked Whale Gulf. An optimal feature set for discriminating the three click types while also separating detected clicks from unidentified delphinids was determined using supervised step-wise discriminant analysis.