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. We present a stochastic model of LINE-1 (L1) transposition in human cancer. This system was chosen because the transposition of L1 elements is well understood, the population dynamics of cancer tumors has been modeled extensively, and the role of L1 elements in cancer progression has garnered interest in recent years. RESULTS: Our model predicts that L1 retrotransposition (RT) can play either advantageous or deleterious roles in tumor progression, depending on the initial lesion size, L1 insertion rate and tumor driver genes. Small changes in the RT rate or set of driver tumor-suppressor genes (TSGs) were observed to alter the dynamics of tumorigenesis. We found high variation in the density of L1 target sites across human protein-coding genes. We also present an analysis, across three cancer types, of the frequency of homozygous TSG disruption in wild-type hosts compared to those with an inherited driver allele. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/atallah-lab/neoplastic-evolution. CONTACT: jlebien@uno.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. Linear and quadratic discriminant analyses both achieved error rates below 1% with three features, determined by tenfold cross validation. The waveform fractal dimension was found to be a highly ranked feature among standard spectral and temporal parameters. The top-ranking features were Higuchi's fractal dimension, spectral centroid, Katz's fractal dimension, and -10 dB duration. Six clustering routines, including four popular network-based algorithms, were also evaluated as unsupervised classification methods using the selected feature set. False positive rates of 0.001 and 0.024 were achieved by Chinese Whispers and spectral clustering, respectively, across 200 randomized trials. However, Chinese Whispers clustering yielded larger false negative rates. Spectral clustering was further tested on clicks from encounters of beaked, sperm, and pilot whales in the Tongue of the Ocean, Bahamas.

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. Moreover, the abnormal flags are rudimentary, merely indicating if a value is within the RI. At the same time, clinical lab data generated in the everyday medical practice contains a wealth of information, that given the correct methodology, can help determine the RIs for each specific segment of the population, including populations that suffer from health disparities. In this work, we develop unsupervised machine learning methods, based on Gaussian mixtures, to determine RIs of analytes related to chronic kidney disease, using millions of routine lab results for the Puerto Rican population. We show that the measures are both gender and age dependent and we find evidence for normal age-related organ function deterioration and failure. We also show that the joint distribution of measures improves the diagnostic value of the lab results.

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. We show that the community composition, and not species richness, of vocalizing vertebrates identified by experts reflects the restoration gradient. Two automated measures - an acoustic index model and a bird community composition derived from an independently developed Convolutional Neural Network - correlated well with restoration (adj-R² = 0.62 and 0.69, respectively). Importantly, both measures reflected composition of non-vocalizing nocturnal insects identified via metabarcoding. We show that such automated monitoring tools, based on new technologies, can effectively monitor the success of forest recovery, using robust and reproducible data.