Epidemic spreading and digital contact tracing: Effects of heterogeneous mixing and quarantine failures.

Contact tracing via digital tracking applications installed on mobile phones is an important tool for controlling epidemic spreading. Its effectivity can be quantified by modifying the standard methodology for analyzing percolation and connectivity of contact networks. We apply this framework to networks with varying degree distributions, numbers of application users, and probabilities of quarantine failures. Further, we study structured populations with homophily and heterophily and the possibility of degree-targeted application distribution. Our results are based on a combination of explicit simulations and mean-field analysis. They indicate that there can be major differences in the epidemic size and epidemic probabilities which are equivalent in the normal susceptible-infectious-recovered (SIR) processes. Further, degree heterogeneity is seen to be especially important for the epidemic threshold but not as much for the epidemic size. The probability that tracing leads to quarantines is not as important as the application adoption rate. Finally, both strong homophily and especially heterophily with regard to application adoption can be detrimental. Overall, epidemic dynamics are very sensitive to all of the parameter values we tested out, which makes the problem of estimating the effect of digital contact tracing an inherently multidimensional problem.

Emergence of power laws in noncritical neuronal systems.

Experimental and computational studies provide compelling evidence that neuronal systems are characterized by power-law distributions of neuronal avalanche sizes. This fact is interpreted as an indication that these systems are operating near criticality, and, in turn, typical properties of critical dynamical processes, such as optimal information transmission and stability, are attributed to neuronal systems. The purpose of this Rapid Communication is to show that the presence of power-law distributions for the size of neuronal avalanches is not a sufficient condition for the system to operate near criticality. Specifically, we consider a simplistic model of neuronal dynamics on networks and show that the degree distribution of the underlying neuronal network may trigger power-law distributions for neuronal avalanches even when the system is not in its critical regime. To certify and explain our findings we develop an analytical approach based on percolation theory and branching processes techniques.

Open facet joint denervation as an adjunct in patients undergoing posterior lumbar decompression for spinal stenosis-a single blinded randomized controlled trial.

BACKGROUND: Facet radiofrequency denervation is a prevalent procedure used to try and relieve back pain. Despite the increasing use of this treatment, its effectiveness has been questioned. In consideration of the conflicting reports in the literature, we sought to conduct a trial to study the short-term effect of facet denervation in patients undergoing lumbar laminectomy(s) to determine the short-term effect of adding facet denervation to patients undergoing lumbar laminectomy(s) where the anatomy was exposed, allowing an open technique to be used for the denervation. METHODS: Sixty patients with a diagnosis of degenerative lumbar spinal stenosis who complained of neurogenic claudication and back pain for at least 3 months were randomized to undergo a lumbar laminectomy(s) either with or without facet joint denervation. Pain and self-reported function using a 10 cm visual analogue scale (VAS) and the Roland-Morris Disability Questionnaire (RMDQ) were measured before surgery and at patients' 6-, 12- and 24-week follow-up clinic visit. Various parametric and non-parametric tests including the Chi-square, independent samples t-tests, the Mann Whitney U, Wilcoxon sign ranks, one-way ANOVA with a Bonferroni post hoc test were used to analyze the data. The RMDQ scores were analyzed between groups and within groups over time. RESULTS: No differences in pain or functional ability were seen between groups. Both groups significantly improved in both pain and function from baseline to follow up times. CONCLUSIONS: Our findings do not support the addition of facet denervation for short-term treatment of back pain in patients undergoing lumbar laminectomy(s) for spinal stenosis within our study design.

Characterizing the Analogy Between Hyperbolic Embedding and Community Structure of Complex Networks.

We show that the community structure of a network can be used as a coarse version of its embedding in a hidden space with hyperbolic geometry. The finding emerges from a systematic analysis of several real-world and synthetic networks. We take advantage of the analogy for reinterpreting results originally obtained through network hyperbolic embedding in terms of community structure only. First, we show that the robustness of a multiplex network can be controlled by tuning the correlation between the community structures across different layers. Second, we deploy an efficient greedy protocol for network navigability that makes use of routing tables based on community structure.

Optimal percolation on multiplex networks.

Optimal percolation is the problem of finding the minimal set of nodes whose removal from a network fragments the system into non-extensive disconnected clusters. The solution to this problem is important for strategies of immunization in disease spreading, and influence maximization in opinion dynamics. Optimal percolation has received considerable attention in the context of isolated networks. However, its generalization to multiplex networks has not yet been considered. Here we show that approximating the solution of the optimal percolation problem on a multiplex network with solutions valid for single-layer networks extracted from the multiplex may have serious consequences in the characterization of the true robustness of the system. We reach this conclusion by extending many of the methods for finding approximate solutions of the optimal percolation problem from single-layer to multiplex networks, and performing a systematic analysis on synthetic and real-world multiplex networks.

Successful Nonoperative Treatment of a Lumbar Spine Extension Injury with Disruption of all Three Bony Columns in a Patient with Ankylosing Spondylitis - A Case Report.

STUDY DESIGN: A case report. BACKGROUND: Patients with ankylosing spondylitis have altered spinal biomechanics putting them at increased risk of spinal fractures that are unstable. As a result there is an increasing trend to treat these fractures with surgical stabilization. We hypothesize that the fracture pattern is also an important factor in patients with this disease and that those with an extension injury in the lumbar spine can be treated with brace immobilization. OBJECTIVE: Report on the non-operative management of an elderly patient, with ankylosing spondylitis, who sustained an extension injury of all three bony columns of the lumbar spine. METHODS: A case report of a 70-year-old man who fell from a standing height, sustaining a three-column fracture at L1-2, who did not want surgical stabilization. RESULTS: External brace immobilization was used and the patient was closely monitored. At his final 13 month follow-up, the patient had no clinical evidence of spinal instability or neurologic compromise and radiologically we could see callous formation anteriorly and laterally between the L1 and L2 vertebral bodies.These bridged the trebeculae across the middle and posterior columns at L1 and L2 on the lateral view, and there was no change in the sagittal or coronal alignment" to "There was mature bridging bone across the middle and posterior columns at L1 and L2 on the lateral view, and there was no change in the sagittal or coronal alignment. CONCLUSION: This case supports our hypothesis that the fracture pattern is an important factor in patients with ankylosing spondylitis and adds to the body of knowledge in the scientific literature concerning non-operative treatment of fractures in patients with ankylosed spines. Further study is required to determine whether ours is an isolated case or whether this applies to a wider population of ankylosing spondylitis patients.

Emergence of coexisting percolating clusters in networks.

It is commonly assumed in percolation theories that at most one percolating cluster can exist in a network. We show that several coexisting percolating clusters (CPCs) can emerge in networks due to limited mixing, i.e., a finite and sufficiently small number of interlinks between network modules. We develop an approach called modular message passing (MMP) to describe and verify these observations. We demonstrate that the appearance of CPCs is an important source of inaccuracy in previously introduced percolation theories, such as the message passing (MP) approach, which is a state-of-the-art theory based on the belief propagation method. Moreover, we show that the MMP theory improves significantly over the predictions of MP for percolation on synthetic networks with limited mixing and also on several real-world networks. These findings have important implications for understanding the robustness of networks and in quantifying epidemic outbreaks in the susceptible-infected-recovered (SIR) model of disease spread.

Network cloning unfolds the effect of clustering on dynamical processes.

We introduce network L-cloning, a technique for creating ensembles of random networks from any given real-world or artificial network. Each member of the ensemble is an L-cloned network constructed from L copies of the original network. The degree distribution of an L-cloned network and, more importantly, the degree-degree correlation between and beyond nearest neighbors are identical to those of the original network. The density of triangles in an L-cloned network, and hence its clustering coefficient, is reduced by a factor of L compared to those of the original network. Furthermore, the density of loops of any fixed length approaches zero for sufficiently large values of L. Other variants of L-cloning allow us to keep intact the short loops of certain lengths. As an application, we employ these network cloning methods to investigate the effect of short loops on dynamical processes running on networks and to inspect the accuracy of corresponding tree-based theories. We demonstrate that dynamics on L-cloned networks (with sufficiently large L) are accurately described by the so-called adjacency tree-based theories, examples of which include the message passing technique, some pair approximation methods, and the belief propagation algorithm used respectively to study bond percolation, SI epidemics, and the Ising model.