Random acyclic networks

Directed acyclic graphs are a fundamental class of networks that includes citation networks, food webs, and family trees, among others. Here we define a random graph model for directed acyclic graphs and give solutions for a number of the model's properties, including connection probabilities and component sizes, as well as a fast algorithm for simulating the model on a computer. We compare the predictions of the model to a real-world network of citations between physics papers and find surprisingly good agreement, suggesting that the structure of the real network may be quite well described by the random graph.Comment: 4 pages, 2 figure

Spin transport and spin dephasing in zinc oxide

The wide bandgap semiconductor ZnO is interesting for spintronic applications because of its small spin-orbit coupling implying a large spin coherence length. Utilizing vertical spin valve devices with ferromagnetic electrodes (TiN/Co/ZnO/Ni/Au), we study the spin-polarized transport across ZnO in all-electrical experiments. The measured magnetoresistance agrees well with the prediction of a two spin channel model with spin-dependent interface resistance. Fitting the data yields spin diffusion lengths of 10.8nm (2K), 10.7nm (10K), and 6.2nm (200K) in ZnO, corresponding to spin lifetimes of 2.6ns (2K), 2.0ns (10K), and 31ps (200K).Comment: 7 pages, 5 figures; supplemental material adde

Stochastic blockmodels and community structure in networks

Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly distort the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.Comment: 11 pages, 3 figure

A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks

This paper presents a novel spectral algorithm with additive clustering designed to identify overlapping communities in networks. The algorithm is based on geometric properties of the spectrum of the expected adjacency matrix in a random graph model that we call stochastic blockmodel with overlap (SBMO). An adaptive version of the algorithm, that does not require the knowledge of the number of hidden communities, is proved to be consistent under the SBMO when the degrees in the graph are (slightly more than) logarithmic. The algorithm is shown to perform well on simulated data and on real-world graphs with known overlapping communities.Comment: Journal of Theoretical Computer Science (TCS), Elsevier, A Para\^itr

Association between muscle dysmorphia psychopathology and binge eating in a large at-risk cohort of men and women.

BACKGROUND Current research on muscle dysmorphia (MD) has focused on restrained eating behaviors and has adopted a primarily male perspective. Despite initial evidence, the role of possible binge eating associated with MD has only been scarcely investigated. To extend the transdiagnostic and cross-gender approaches and address the dearth in research related to MD, this study investigated the association between MD psychopathology and binge eating in men and women. METHODS This study investigated the association between MD psychopathology and binge eating in both men and women. Participants were a sample of 5905 men (n = 422) and women (n = 5483) social media users aged 18-72 years. They completed an online survey that included self-report measures assessing demographics, binge eating, MD psychopathology, and drive for thinness and leanness. Binge eating was assessed using the diagnostic questions of the validated German version of the Eating Disorder Examination-Questionnaire. The Muscle Dysmorphic Disorder Inventory (MDDI) was used to assess MD psychopathology. A total score of > 39 was set as a cutoff to define an "MD at-risk" state for both men and women. Hierarchical logistic regression analysis was used to analyze the association between MD psychopathology and binge eating. RESULTS MD psychopathology was significantly positively associated with binge eating in both men and women. Among the three MDDI subscales, only appearance intolerance was significantly associated with MD, and drive for size and functional impairment were not associated. MD at-risk status yielded a predicted probability of binge eating of 25% for men and 66.9% for women. The increased probability of binge eating associated with MD at-risk status was mainly accounted for by appearance intolerance in men and drive for thinness in women. CONCLUSION MD psychopathology is positively associated with binge eating in both men and women. Binge eating episodes should therefore form part of the clinical assessment of MD

Association between muscle dysmorphia psychopathology and binge eating in a large at-risk cohort of men and women

Background Current research on muscle dysmorphia (MD) has focused on restrained eating behaviors and has adopted a primarily male perspective. Despite initial evidence, the role of possible binge eating associated with MD has only been scarcely investigated. To extend the transdiagnostic and cross-gender approaches and address the dearth in research related to MD, this study investigated the association between MD psychopathology and binge eating in men and women. Methods This study investigated the association between MD psychopathology and binge eating in both men and women. Participants were a sample of 5905 men (n = 422) and women (n = 5483) social media users aged 18–72 years. They completed an online survey that included self-report measures assessing demographics, binge eating, MD psychopathology, and drive for thinness and leanness. Binge eating was assessed using the diagnostic questions of the validated German version of the Eating Disorder Examination-Questionnaire. The Muscle Dysmorphic Disorder Inventory (MDDI) was used to assess MD psychopathology. A total score of > 39 was set as a cutoff to define an “MD at-risk” state for both men and women. Hierarchical logistic regression analysis was used to analyze the association between MD psychopathology and binge eating. Results MD psychopathology was significantly positively associated with binge eating in both men and women. Among the three MDDI subscales, only appearance intolerance was significantly associated with MD, and drive for size and functional impairment were not associated. MD at-risk status yielded a predicted probability of binge eating of 25% for men and 66.9% for women. The increased probability of binge eating associated with MD at-risk status was mainly accounted for by appearance intolerance in men and drive for thinness in women. Conclusion MD psychopathology is positively associated with binge eating in both men and women. Binge eating episodes should therefore form part of the clinical assessment of MD

Training load, sports performance, physical and mental health during the COVID-19 pandemic: A prospective cohort of Swiss elite athletes

Background: The COVID-19 pandemic and associated restrictions have led to abrupt changes in the lives of elite athletes. Objectives: The objective of this prospective cohort study was to examine training load, subjective sports performance, physical and mental health among Swiss elite athletes during a 6-month follow-up period starting with the first Swiss lockdown. Methods: Swiss elite athletes (n = 203) participated in a repeated online survey evaluating health, training, and performance related metrics. After the first assessment during the first lockdown between April and May 2020, there were monthly follow-ups over 6 months. Results: Out of 203 athletes completing the first survey during the first lockdown, 73 athletes (36%) completed all assessments during the entire 6-month follow-up period. Sports performance and training load decreased during the first lockdown and increased again at the beginning of the second lockdown in October 2020, while symptoms of depression and financial fears showed only a transient increase during the first lockdown. Self-reported injuries and illnesses did not change significantly at any timepoint in the study. Stricter COVID-19 restrictions, as measured by the Government Stringency Index (GSI), were associated with reduced subjective sports performance, as well as lower training intensity, increased financial fears, poorer coping with restrictions, and more depressive symptoms, as measured by the 9-item module of the Patient Health Questionnaire-9 (PHQ-9). Conclusion: This study revealed a negative impact of the COVID-19 restrictions on sports performance, training load and mental health among Swiss elite athletes, while the rate of self-reported injuries and illnesses remained unaffected

Interleukin-6 potentiates endurance training adaptation and improves functional capacity in old mice

Interventions to preserve functional capacities at advanced age are becoming increasingly important. So far, exercise provides the only means to counteract age-related decrements in physical performance and muscle function. Unfortunately, the effectiveness of exercise interventions in elderly populations is hampered by reduced acceptance and compliance as well as disuse complications. We therefore studied whether application of interleukin-6 (IL-6), a pleiotropic myokine that is induced by skeletal muscle activity and exerts broad systemic effects in response to exercise, affects physical performance and muscle function alone or in combination with training in aged mice.; Sedentary old male mice (Sed+Saline, n = 15) were compared with animals that received recombinant IL-6 (rIL-6) in an exercise-mimicking pulsatile manner (Sed+IL-6, n = 16), were trained with a moderate-intensity, low-volume endurance exercise regimen (Ex+Saline, n = 13), or were exposed to a combination of these two interventions (Ex+IL-6, n = 16) for 12 weeks. Before and at the end of the intervention, mice underwent a battery of tests to quantify endurance performance, muscle contractility in situ, motor coordination, and gait and metabolic parameters.; Mice exposed to enhanced levels of IL-6 during endurance exercise bouts showed superior improvements in endurance performance (33% more work and 12% greater peak power compared with baseline), fatigue resistance in situ (P = 0.0014 vs. Sed+Saline; P = 0.0199 vs. Sed+IL-6; and P = 0.0342 vs. Ex+Saline), motor coordination (rotarod performance, P = 0.0428), and gait (gait speed, P = 0.0053) following training. Pulsatile rIL-6 treatment in sedentary mice had only marginal effects on glucose tolerance and some gait parameters. No increase in adverse events or mortality related to rIL-6 treatment was observed.; Administration of rIL-6 paired with treadmill running bouts potentiates the adaptive response to a moderate-intensity low-volume endurance exercise regimen in old mice, while being safe and well tolerated

An efficient and principled method for detecting communities in networks

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl

Fast variables determine the epidemic threshold in the pairwise model with an improved closure

Pairwise models are used widely to model epidemic spread on networks. These include the modelling of susceptible-infected-removed (SIR) epidemics on regular networks and extensions to SIS dynamics and contact tracing on more exotic networks exhibiting degree heterogeneity, directed and/or weighted links and clustering. However, extra features of the disease dynamics or of the network lead to an increase in system size and analytical tractability becomes problematic. Various `closures' can be used to keep the system tractable. Focusing on SIR epidemics on regular but clustered networks, we show that even for the most complex closure we can determine the epidemic threshold as an asymptotic expansion in terms of the clustering coefficient.We do this by exploiting the presence of a system of fast variables, specified by the correlation structure of the epidemic, whose steady state determines the epidemic threshold. While we do not find the steady state analytically, we create an elegant asymptotic expansion of it. We validate this new threshold by comparing it to the numerical solution of the full system and find excellent agreement over a wide range of values of the clustering coefficient, transmission rate and average degree of the network. The technique carries over to pairwise models with other closures [1] and we note that the epidemic threshold will be model dependent. This emphasises the importance of model choice when dealing with realistic outbreaks