From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

Sampling of temporal networks: methods and biases

Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data

A systematic review of the use of an expertise-based randomised controlled trial design

Acknowledgements JAC held a Medical Research Council UK methodology (G1002292) fellowship, which supported this research. The Health Services Research Unit, Institute of Applied Health Sciences (University of Aberdeen), is core-funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. Views express are those of the authors and do not necessarily reflect the views of the funders.Peer reviewedPublisher PD

Probing empirical contact networks by simulation of spreading dynamics

Disease, opinions, ideas, gossip, etc. all spread on social networks. How these networks are connected (the network structure) influences the dynamics of the spreading processes. By investigating these relationships one gains understanding both of the spreading itself and the structure and function of the contact network. In this chapter, we will summarize the recent literature using simulation of spreading processes on top of empirical contact data. We will mostly focus on disease simulations on temporal proximity networks -- networks recording who is close to whom, at what time -- but also cover other types of networks and spreading processes. We analyze 29 empirical networks to illustrate the methods

Digital medical history implementation to triage orthopaedic patients during COVID-19: findings from a rapid cycle, semi-randomised A/B testing quality improvement project

Introduction: The COVID-19 pandemic severely impacted musculoskeletal care. To better triage the notable backlog of patients, we assessed whether a digital medical history (DMH), a summary of health information and concerns completed by the patient prior to a clinic visit, could be routinely collected and utilised.Methods: We analysed 640 patients using a rapid cycle, semi-randomised A/B testing approach. Four rapid cycles of different randomised interventions were conducted across five unique patient groups. Descriptive statistics were used to report DMH completion rates by cycle/patient group and intervention. Multivariable logistic regression was used to determine whether age or anatomic injury location was associated DMH completion.Ethical Approval: N/A (Quality Improvement Project)Results: Across all patients, the DMH completion rate was 48% (307/640). Phone calls were time consuming and resource intensive without an increased completion rate. The highest rate of DMH completion was among patients who were referred and called the clinic themselves (78% of patients [63 out of 81 patients]). Across all patients, increasing age (odds ratio [OR]: 0.985 (95% CI: 0.976-0.995), p = 0.002), patients with back concerns (OR: 0.395 (95% CI: 0.234-0.666), p = 0.001), and patients with non-specific/other musculoskeletal concerns (OR: 0.331 (95% CI: 0.176-0.623), p = 0.001) were associated with decreased odds of DMH completion.Discussion and Conclusion: DMHs can be valuable in helping triage orthopaedic patients in resource-strapped settings, times of crisis, or as we transition towards value-based health care delivery. However, further work is needed to continue to increase the completion rate about 50%.Orthopaedics, Trauma Surgery and Rehabilitatio

Effects of dapagliflozin on volume status and systemic haemodynamics in patients with chronic kidney disease without diabetes:Results from DAPASALT and DIAMOND

Aims To assess the effect of sodium-glucose cotransporter-2 inhibitor dapagliflozin on natriuresis, blood pressure (BP) and volume status in patients with chronic kidney disease (CKD) without diabetes. Materials and methods We performed a mechanistic open-label study (DAPASALT) to evaluate the effects of dapagliflozin on 24-hour sodium excretion, 24-hour BP, extracellular volume, and markers of volume status during a standardized sodium diet (150 mmol/d) in six patients with CKD. In parallel, in a placebo-controlled double-blind crossover trial (DIAMOND), we determined the effects of 6 weeks of dapagliflozin on markers of volume status in 53 patients with CKD. Results In DAPASALT (mean age 65 years, mean estimated glomerular filtration rate [eGFR] 39.4 mL/min/1.73 m(2), median urine albumin:creatinine ratio [UACR] 111 mg/g), dapagliflozin did not change 24-hour sodium and volume excretion during 2 weeks of treatment. Dapagliflozin was associated with a modest increase in 24-hour glucose excretion on Day 4, which persisted at Day 14 and reversed to baseline after discontinuation. Mean 24-hour systolic BP decreased by -9.3 (95% confidence interval [CI] -19.1, 0.4) mmHg after 4 days and was sustained at Day 14 and at wash-out. Renin, angiotensin II, urinary aldosterone and copeptin levels increased from baseline. In DIAMOND (mean age 51 years, mean eGFR 59.0 mL/min/1.73 m(2), median UACR 608 mg/g), compared to placebo, dapagliflozin increased plasma renin (38.5 [95% CI 7.4, 78.8]%), aldosterone (19.1 [95% CI -5.9, 50.8]%), and copeptin levels (7.3 [95% CI 0.1, 14.5] pmol/L). Conclusions During a standardized sodium diet, dapagliflozin decreased BP but did not increase 24-hour sodium and volume excretion. The lack of increased natriuresis and diuresis may be attributed to activation of intra-renal compensatory mechanisms to prevent excessive water loss

Random walk centrality for temporal networks

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included

LEARN 2 MOVE 7-12 years: a randomized controlled trial on the effects of a physical activity stimulation program in children with cerebral palsy

<p>Abstract</p> <p>Background</p> <p>Regular participation in physical activities is important for all children to stay fit and healthy. Children with cerebral palsy have reduced levels of physical activity, compared to typically developing children. The aim of the LEARN 2 MOVE 7-12 study is to improve physical activity by means of a physical activity stimulation program, consisting of a lifestyle intervention and a fitness training program.</p> <p>Methods/Design</p> <p>This study will be a 6-month single-blinded randomized controlled trial with a 6-month follow up. Fifty children with spastic cerebral palsy, aged 7 to 12 years, with Gross Motor Function Classification System levels I-III, will be recruited in pediatric physiotherapy practices and special schools for children with disabilities. The children will be randomly assigned to either the intervention group or control group. The children in the control group will continue with their regular pediatric physiotherapy, and the children in the intervention group will participate in a 6-month physical activity stimulation program. The physical activity stimulation program consists of a 6-month lifestyle intervention, in combination with a 4-month fitness training program. The lifestyle intervention includes counseling the child and the parents to adopt an active lifestyle through Motivational Interviewing, and home-based physiotherapy to practise mobility-related activities in the daily situation. Data will be collected just before the start of the intervention (T0), after the 4-month fitness training program (T4), after the 6-month lifestyle intervention (T6), and after six months of follow-up (T12). Primary outcomes are physical activity, measured with the StepWatch Activity Monitor and with self-reports. Secondary outcomes are fitness, capacity of mobility, social participation and health-related quality of life. A random coefficient analysis will be performed to determine differences in treatment effect between the control group and the intervention group, with primary outcomes and secondary outcomes as the dependent variables.</p> <p>Discussion</p> <p>This is the first study that investigates the effect of a combined lifestyle intervention and fitness training on physical activity. Temporary effects of the fitness training are expected to be maintained by changes to an active lifestyle in daily life and in the home situation.</p> <p>Trial registration</p> <p>This study is registered in the Dutch Trial Register as NTR2099.</p

Diffusion on networked systems is a question of time or structure

Network science investigates the architecture of complex systems to understand their functional and dynamical properties. Structural patterns such as communities shape diffusive processes on networks. However, these results hold under the strong assumption that networks are static entities where temporal aspects can be neglected. Here we propose a generalized formalism for linear dynamics on complex networks, able to incorporate statistical properties of the timings at which events occur. We show that the diffusion dynamics is affected by the network community structure and by the temporal properties of waiting times between events. We identify the main mechanism—network structure, burstiness or fat tails of waiting times—determining the relaxation times of stochastic processes on temporal networks, in the absence of temporal–structure correlations. We identify situations when fine-scale structure can be discarded from the description of the dynamics or, conversely, when a fully detailed model is required due to temporal heterogeneities