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

OBJECTIVE:Generalised spike and wave discharges (SWDs) are pathognomonic EEG signatures for diagnosing absence seizures in patients with Genetic Generalized Epilepsy (GGE). The Genetic Absence Epilepsy Rats from Strasbourg (GAERS) is one of the best-validated animal models of GGE with absence seizures. METHODS:We developed an SWDs detector for both GAERS rodents and GGE patients with absence seizures using a neural network method. We included 192 24-hour EEG sessions recorded from 18 GAERS rats, and 24-hour scalp-EEG data collected from 11 GGE patients. RESULTS:The SWDs detection performance on GAERS showed a sensitivity of 98.01% and a false positive (FP) rate of 0.96/hour. The performance on GGE patients showed 100% sensitivity in five patients, while the remaining patients obtained over 98.9% sensitivity. Moderate FP rates were seen in our patients with 2.21/hour average FP. The detector trained within our patient cohort was validated in an independent dataset, TUH EEG Seizure Corpus (TUSZ), that showed 100% sensitivity in 11 of 12 patients and 0.56/hour averaged FP. CONCLUSIONS:We developed a robust SWDs detector that showed high sensitivity and specificity for both GAERS rats and GGE patients. SIGNIFICANCE:This detector can assist researchers and neurologists with the time-efficient and accurate quantification of SWDs.

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.

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.

This paper proposes a method to address misreadings and consequent inadequacy of radio-frequency identification data for social insect monitoring. Six-month worth field experiment data were collected to demonstrate the application of the method. The data are transformed into a linear combination of the Gaussian model and curve-fitted using an evolutionary algorithm. This results show that the proposed method allows us to improve the quality of data that infer honey bee behavior at the colony level.

We present an end-to-end visual analytics framework that aims to facilitate prediction and decision making about honey bee health based on micro sensing data. The framework is particularly tailored to cope with heterogeneous data from micro sensors and environmental sensors that are deployed to collect information about bees and their environment. The framework design allows for a wide range of end users, including scientists, bee keepers, and decision makers, to effectively explore the bee data through interactive visual interfaces. User centred design is deployed throughout the development to meet the various requirements of the users. A large scale study is being planned to evaluate and further refine the framework based on user experiences.

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.

Spatial data is typically inferred between reference points using interpolation techniques and communicated to end users through visualisation. It is not well understood yet how different interpolation techniques perform visually and what visualisation attributes impact on the visual communication of spatial maps. In this paper, we present a study to address these issues. We performed a dedicated experiment in which observers judged visual similarity between interpolated maps and reference maps. We could clearly identify the superior interpolation techniques amongst a set of techniques under consideration. We further found a significant effect for the colour map used for visualisation. No interaction, however, was found between the colour maps and specific interpolation technique comparisons. Response times were recorded as a proxy for judging difficulty and were found to be significantly larger for comparisons amongst the best and worst interpolation techniques.

The impact of perceptual relevance information on content aware image retargeting is investigated. We integrated fixation density maps and region-of-interest maps into a contemporary image retargeting algorithm to test the hypothesis that the latter result in superior performance given their object level representation. We performed an experimental study with human participants to evaluate the performance gains relative to benchmark conditions. The experiment revealed that the kind of perceptual relevance information, image content, and retargeting ratio all have a strong impact on the overall performance. Recorded response times provided further insight into the difficulty that people experienced when performing the assessment task. These findings are instrumental for the image retargeting research community to further improve their algorithms by augmenting content awareness with perceptual relevance.

As obesity is increasing in many countries, helping people manage their weight has become an important issue. Medical research has shown that the family context may be important to promote lifestyle changes. Our work aims at designing a collaborative environment to engage a family in support of an individual needing to manage his or her weight. This paper presents the first step in our iterative design process which aimed at collecting information about the needs of overweight and obese people, and about the type of environment they would find useful for them and their family.

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.

We are looking at new effective ways to engage families in weight management. Going beyond targeting individuals out of their family context with a single intervention, we investigate ways of delivering tailored health information as an information service, using the family as a major contextual factor. In particular, we explore interaction modes and processes of engagement with families that would be most effective to provide an information service that users will perceive as a real benefit to them.