A benchmark for computational analysis of animal behavior, using animal-borne tags.

Background: Animal-borne sensors ('bio-loggers') can record a suite of kinematic and environmental data, which are used to elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers, but there exists no common framework for comparing the different machine learning techniques in this domain. This makes it difficult to, for example, identify patterns in what works well for machine learning-based analysis of bio-logger data.

Hidden rivals: the negative impacts of dolphinfish on seabird foraging behaviour.

Marine predators often aggregate at the air-sea boundary layer to pursue shared prey. In such scenarios, seabirds are likely to benefit from underwater predators herding fish schools into tight clusters thereby enhancing seabirds' prey detectability and capture potential. However, this coexistence can lead to competition, affecting not only immediate foraging strategies but also their distribution and interspecies dynamics.

Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector.

Rare behaviors displayed by wild animals can generate new hypotheses; however, observing such behaviors may be challenging. While recent technological advancements, such as bio-loggers, may assist in documenting rare behaviors, the limited running time of battery-powered bio-loggers is insufficient to record rare behaviors when employing high-cost sensors (e.g.

Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.

Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks.

Machine learning enables improved runtime and precision for bio-loggers on seabirds.

Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge.

Deep learning-assisted comparative analysis of animal trajectories with DeepHL.

A comparative analysis of animal behavior (e.g., male vs.

Changes in the microbial community during the acclimation process of anaerobic digestion for treatment of synthetic lipid-rich wastewater.

Changes in the microbial community were investigated during the acclimation process of anaerobic digestion while treating synthetic lipid-rich wastewater, which comprised of glucose, acetic acid, lactic acid, and soybean oil. The oil content in the synthetic wastewater was increased successively from 0% to 25% and finally to 50% of the total carbon content, to clarify the effect of substrate type change from easily degradable organic materials to lipid. The oil decomposition-associated methane production rate increased as the microorganisms acclimated to the oil and eventually levelled off around 0.

Characterizing the microbial community involved in anaerobic digestion of lipid-rich wastewater to produce methane gas.

This study attempted to characterize the microbial community and its role in anaerobic digestion of lipid. Reactors were fed semi-continuously with three related substrates, oil and its degradation intermediates (glycerol and long chain fatty acids (LCFAs)), with a stepwise increase in organic loading rate for 90 days. Microbial community analysis using next-generation sequencing (NGS) with the MiSeq Illumina platform revealed that Anaerolineaceae was the most dominant group of bacteria in all experiments, whereas Clostridium, Desulfovibrio, Rikenellaceae, and Treponema were observed characteristically in glycerol degradation and Leptospirales, Synergistaceae, Thermobaculaceae and Syntrophaceae were seen with high abundance in LCFA and oil mineralization.

STEFTR: A Hybrid Versatile Method for State Estimation and Feature Extraction From the Trajectory of Animal Behavior.

Animal behavior is the final and integrated output of brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient prior knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed.