The use of multilayer network analysis in animal behaviour

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as \emph{multilayer network analysis}, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour as connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population, and evolutionary levels of organisation. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.Comment: Thoroughly revised; title changed slightl

Quantifying the latency benefits of near-edge and in-network FPGA acceleration

Transmitting data to cloud datacenters in distributed IoT applications introduces significant communication latency, but is often the only feasible solution when source nodes are computationally limited. To address latency concerns, cloudlets, in-network computing, and more capable edge nodes are all being explored as a way of moving processing capability towards the edge of the network. Hardware acceleration using Field Programmable Gate Arrays (FPGAs) is also seeing increased interest due to reduced computation latency and improved efficiency. This paper evaluates the the implications of these offloading approaches using a case study neural network based image classification application, quantifying both the computation and communication latency resulting from different platform choices. We consider communication latency including the ingestion of packets for processing on the target platform, showing that this varies significantly with the choice of platform. We demonstrate that emerging in-network accelerator approaches offer much improved and predictable performance as well as better scaling to support multiple data sources

From photons to big-data applications: terminating terabits

Computer architectures have entered a watershed as the quantity of network data generated by user applications exceeds the data-processing capacity of any individual computer end-system. It will become impossible to scale existing computer systems while a gap grows between the quantity of networked data and the capacity for per system data processing. Despite this, the growth in demand in both task variety and task complexity continues unabated. Networked computer systems provide a fertile environment in which new applications develop. As networked computer systems become akin to infrastructure, any limitation upon the growth in capacity and capabilities becomes an important constraint of concern to all computer users. Considering a networked computer system capable of processing terabits per second, as a benchmark for scalability, we critique the state of the art in commodity computing, and propose a wholesale reconsideration in the design of computer architectures and their attendant ecosystem. Our proposal seeks to reduce costs, save power and increase performance in a multi-scale approach that has potential application from nanoscale to data-centre-scale computers.This work was supported by the UK Engineering and Physical Sciences Research Council Internet Project EP/H040536/1. This work was supported by the Defense Advanced Research Projects Agency and the Air Force Research Laboratory, under contract FA8750-11-C-0249

Association of adverse childhood experiences with the diagnosis and severity of obstructive sleep apnea

BACKGROUND: Obstructive Sleep Apnea (OSA) is a very common disease characterized by brief episodes of airway collapse and low oxygen during sleep. Many years of research on OSA have shown that social determinants of health, such as race, income, zip code, and parental education level lead to varied levels of disease prevalence and severity among different populations. It has been shown very clearly that low-income African-American children have the highest prevalence and most severe outcomes of OSA. However, there is one very important component of health that has not been evaluated in conjunction with OSA: childhood trauma. To assess for childhood trauma, physicians often use the Adverse Childhood Experiences (ACE) QUESTIONNAIRE. This series of questions has been used in numerous medical specialties, including primary care, oncology, pulmonology, general surgery, and obstetrics/gynecology, for decades to evaluate patients for childhood trauma before the age of 18. This survey not only allows clinicians to provide trauma-informed care to their patients, but also serves as a reminder to address both the social and physical aspects of disease when treating a patient, as trauma has been shown to have detrimental effects on long-term health. Over two decades of research have shown that an increased number of ACEs before the age of 18 leads to worse outcomes in a wide variety of diseases, many of which are some of the leading causes of morbidity and mortality in the United States. However, no studies to date have used the ACE questionnaire to evaluate disease and surgical outcomes in the field of otolaryngology (ENT), one of the medical specialties in which patients with OSA are treated and followed. Given the prevalence of OSA nationwide among adults and children, it is crucial to begin examining whether childhood trauma leads to worse disease outcomes in OSA. OBJECTIVE: To conduct a comprehensive literature review of research done on both pediatric OSA and the effects of childhood trauma on disease outcomes for some of most common causes of morbidity and mortality in the United States. An additional objective is to show, using data from the Kids Inpatient Database, associations between various demographic factors and diagnosis with OSA requiring an adenotonsillectomy. METHODS: For the literature review, a PubMed search was conducted on both aforementioned topics using various keywords and MeSH terms to narrow down the search. Reference lists of relevant papers were also used to find other relevant papers. For the empirical data, the Kids’ Inpatient Database, a national database created by the Healthcare Cost and Utilization Project and Agency for Healthcare Research and Quality, was used. There have been many version of this database, but the most recent version from 2016 was used for this study. Due to the COVID-19 pandemic-related restrictions, we were not able to administer the ACE Questionnaire to patients with obstructive sleep apnea as originally planned. RESULTS: The literature review on pediatric OSA showed that the main risk factors for developing severe OSA are adenotonsillar hypertrophy, African-American race, low socioeconomic status/low income, and obesity. Other important risk factors include preterm birth, male sex, and craniofacial anomalies. The literature review on childhood trauma showed that adults over the age of 18 who endorsed more adverse experiences on the ACE questionnaire consistently had worse disease outcomes in chronic illnesses such as ischemic heart disease, chronic obstructive pulmonary disease, and some cancers. The results from the Kids Inpatient Database showed that Black children had the highest odds of developing OSA and requiring a tonsillectomy. Additionally, the Kids Inpatient Database shed light on the odds of having a tonsillectomy based on income quartile, showing that children of higher income quartiles are more likely to receive surgery. CONCLUSIONS: Many years of research have shown both that social determinants of health such as income, race, and socioeconomic status are associated with severity of obstructive sleep apnea, and that increased childhood trauma is often associated with worse disease outcomes in a wide variety of chronic illnesses. However, a very important next step in research on these topics is to combine them to assess whether increased childhood trauma also correlates with increased severity of OSA