Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys.

Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: "adult caribou", "calf caribou", and "ghost caribou" (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96-0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers' annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities.

Searching the Human Genome for Snail and Slug With DNA@Home.

DNA@Home is a volunteer computing project that aims to use Gibbs Sampling for the identification and location of DNA control signals on full genome-scale datasets. A fault tolerant and asynchronous implementation of Gibbs sampling using the Berkeley Open Infrastructure for Network Computing (BOINC) was used to identify the location of binding sites of the SNAI1 (Snail) and SNAI2 (Slug) transcription factors across the human genome. Genes regulated by Slug but not Snail, and genes regulated by Snail but not Slug provided two datasets with known motifs. These datasets contained up to 994 DNA sequences which to our knowledge is largest scale use of Gibbs sampling for discovery of binding sites. 1000 parallel sampling walks were used to search for the presence of 1, 2 or 3 possible motifs using small, medium, and full size sets of these sequences. These runs were performed over a period of two months using over 1500 volunteered computing hosts and generated over 2.2 Terabytes of sampling data. High performance computing resources were used for post processing. This paper presents intra and inter walk analyses used to determine walk convergence. The results were validated against current biological knowledge of the Snail and Slug promoter regions and present avenues for further biological study.