Graph networks can model the data observed across different levels of biological systems that span from the population graph (with patients as network nodes) to the molecular graphs that involve omics data. Graph-based approaches have shed light on decoding biological processes modulated by complex interactions. This paper systematically reviews the graph-based analysis methods, including Graph Signal Processing (GSP), Graph Neural Network (GNN), and graph topology inference methods, and their applications to biological data. This work focuses on the algorithms of the graph-based approaches and the constructions of the graph-based frameworks that are adapted to the broad range of biological data. We cover the Graph Fourier Transform and the graph filter developed in GSP, which provides tools to investigate biological networks in the graph domain that can potentially benefit from the underlying graph structure. We also review the node, graph, and interaction oriented GNN architecture with inductive and transductive learning manners for various biological objectives. As the key component of graph analysis, we provide a review of the graph topology inference methods that incorporate assumptions for specific biological objectives. Finally, we discuss the biological application of graph analysis methods within the exhaustive literature collection, potentially providing insights for future research in the biological sciences.

This paper introduces both a hardware and a software system designed to allow low-cost electronic monitoring of social insects using RFID tags. Data formats for individual insect identification and their associated experiment are proposed to facilitate data sharing from experiments conducted with this system. The antennas’ configuration and their duty cycle ensure a high degree of detection rates. Other advantages and limitations of this system are discussed in detail in the paper.

Males in Hymenopteran societies are understudied in many aspects and it is assumed that they only have a reproductive function. We studied the time budget of male honey bees, drones, using multiple methods. Changes in the activities of animals provide important information on biological clocks and their health. Yet, in nature, these changes are subtle and often unobservable without the development and use of modern technology. During the spring and summer mating season, drones emerge from the hive, perform orientation flights, and search for drone congregation areas for mating. This search may lead drones to return to their colony, drift to other colonies (vectoring diseases and parasites), or simply get lost to predation. In a low percentage of cases, the search is successful, and drones mate and die. Our objective was to describe the activity of Apis mellifera drones during the mating season in Northwestern Argentina using three methods: direct observation, video recording, and radio frequency identification (RFID). The use of RFID tagging allows the tracking of a bee for 24 h but does not reveal the detailed activity of drones. We quantified the average number of drones’ departure and arrival flights and the time outside the hive. All three methods confirmed that drones were mostly active in the afternoon. We found no differences in results between those obtained by direct observation and by video recording. RFID technology enabled us to discover previously unknown drone behavior such as activity at dawn and during the morning. We also discovered that drones may stay inside the hive for many days, even after initiation of search flights (up to four days). Likewise, we observed drones to leave the hive for several days to return later (up to three days). The three methods were complementary and should be considered for the study of bee drone activity, which may be associated with the diverse factors influencing hive health.

Significant consumption and cost savings can be made by better managing energy usage within small commercial properties and individual dwellings. By combining Web services and off-the-shelf home automation equipment, it is now possible to build a cost-effective infrastructure to support the delivery of energy management services to small consumers. In this paper we treat residential energy management as a resource management problem in a distributed computing system. Energy consumers are able to delegate the energy management of smart-meter connected appliances to these energy service providers and specify their energy consumption preference through access policies. We give an optimal scenario in terms of energy cost and efficiency in this service model. We also design an algorithm that makes use of aggregated user information to achieve near optimal energy use among residential electricity users.

This paper proposes a method to address misreadings and consequent inadequacy of Radio- Frequency Identification (RFID) data for social insect monitoring. Six months worth of field experiment data were collected to demonstrate the application of the method. The data is transformed into a linear combination of Gaussian model and curve-fitted using evolutionary algorithm. The results show that the proposed method allows us to improve the quality of data that infer honey bee behaviour at the colony level.

Recent trends in computing environments indicate that the future infrastructure for visual analytics will be distributed and collaborative. Collaborative frameworks create value for scientists, analysts, industrial partners, domain experts, and other end-users to meet, communicate, interact with others, and coordinate their activities in a globally shared network. This paper focuses on collaborative framework design for immersive analytics facilitating the integration of multimodal immersive interfaces. The framework design takes into account visualisation and interaction techniques for multiple users and especially decision support tools for scientific visual analytics experts. An overview of several important aspects of collaborative platforms for immersive analytics is presented and different modules of our proposed platform (including data management, analytics, visualisation, querying, and user interface design) will be detailed to highlight their importance in a full visual analytics pipeline.