Subsampling for Chain-Referral Methods

International audienceWe study chain-referral methods for sampling in social networks. These methods rely on subjects of the study recruiting other participants among their set of connections. This approach gives us the possibility to perform sampling when the other methods, that imply the knowledge of the whole network or its global characteristics, fail. Chain-referral methods can be implemented with random walks or crawling in the case of online social networks. However, the estimations made on the collected samples can have high variance, especially with small sample size. The other drawback is the potential bias due to the way the samples are collected. We suggest and analyze a sub-sampling technique, where some users are requested only to recruit other users but do not participate to the study. Assuming that the referral has lower cost than actual participation, this technique takes advantage of exploring a larger variety of population, thus decreasing significantly the variance of the estimator. We test the method on real social networks and on synthetic ones. As by-product, we propose a Gibbs like method for generating synthetic networks with desired properties

A Comparison of Techniques for Sampling Web Pages

As the World Wide Web is growing rapidly, it is getting increasingly challenging to gather representative information about it. Instead of crawling the web exhaustively one has to resort to other techniques like sampling to determine the properties of the web. A uniform random sample of the web would be useful to determine the percentage of web pages in a specific language, on a topic or in a top level domain. Unfortunately, no approach has been shown to sample the web pages in an unbiased way. Three promising web sampling algorithms are based on random walks. They each have been evaluated individually, but making a comparison on different data sets is not possible. We directly compare these algorithms in this paper. We performed three random walks on the web under the same conditions and analyzed their outcomes in detail. We discuss the strengths and the weaknesses of each algorithm and propose improvements based on experimental results

Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study

Background: The early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic. Objective: This retrospective observational study of emergency physician Twitter use details how the health care crisis has influenced emergency physician discourse online and how this discourse may have use as a harbinger of ensuing surge. Methods: Followers of the three main emergency physician professional organizations were identified using Twitter\u27s application programming interface. They and their followers were included in the study if they identified explicitly as US-based emergency physicians. Statuses, or tweets, were obtained between January 4, 2020, when the new disease was first reported, and December 14, 2020, when vaccination first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and Sentiment Reasoner (VADER) tool as well as topic modeling using latent Dirichlet allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new COVID-19 cases and inpatient bed utilization. Results: A total of 3463 emergency physicians produced 334,747 unique English-language tweets during the study period. Out of 3463 participants, 910 (26.3%) stated that they were in training, and 466 of 902 (51.7%) participants who provided their gender identified as men. Overall tweet volume went from a pre-March 2020 mean of 481.9 (SD 72.7) daily tweets to a mean of 1065.5 (SD 257.3) daily tweets thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and 4 days in November, discourse was dominated by the health care system (45,570/334,747, 13.6%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to moderately correlate with COVID-19 hospital bed utilization (Pearson r=0.41), as was the occurrence of covid, coronavirus, or pandemic in tweet texts (r=0.47). Momentum in COVID-19 tweets, as demonstrated by a sustained crossing of 7- and 28-day moving averages, was found to have occurred on an average of 45.0 (SD 12.7) days before peak COVID-19 hospital bed utilization across the country and in the four most contributory states. Conclusions: COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge

The evolutionary history of <i>Shigella flexneri</i> serotype 6 in Asia

Shigella flexneri serotype 6 is an understudied cause of diarrhoeal diseases in developing countries, and has been proposed as one of the major targets for vaccine development against shigellosis. Despite being named as S. flexneri, Shigella flexneri serotype 6 is phylogenetically distinct from other S. flexneri serotypes and more closely related to S. boydii. This unique phylogenetic relationship and its low sampling frequency have hampered genomic research on this pathogen. Herein, by utilizing whole genome sequencing (WGS) and analyses of Shigella flexneri serotype 6 collected from epidemiological studies (1987-2013) in four Asian countries, we revealed its population structure and evolutionary history in the region. Phylogenetic analyses supported the delineation of Asian Shigella flexneri serotype 6 into two phylogenetic groups (PG-1 and -2). Notably, temporal phylogenetic approaches showed that extant Asian S. flexneri serotype 6 could be traced back to an inferred common ancestor arising in the 18th century. The dominant lineage PG-1 likely emerged in the 1970s, which coincided with the times to most recent common ancestors (tMRCAs) inferred from other major Southeast Asian S. flexneri serotypes. Similar to other S. flexneri serotypes in the same period in Asia, genomic analyses showed that resistance to first-generation antimicrobials was widespread, while resistance to more recent first-line antimicrobials was rare. These data also showed a number of gene inactivation and gene loss events, particularly on genes related to metabolism and synthesis of cellular appendages, emphasizing the continuing role of reductive evolution in the adaptation of the pathogen to an intracellular lifestyle. Together, our findings reveal insights into the genomic evolution of the understudied Shigella flexneri serotype 6, providing a new piece in the puzzle of Shigella epidemiology and evolution.</p

RELATING RSV GENETIC DIVERSITY TO GLOBAL TRANSMISSION DYNAMICS

Many studies of Respiratory Syncytial Virus (RSV) have relied on analyses of the Major Surface Glycoprotein G gene (G gene). Global transmission patterns have not been well studied due to lack of systematic global surveillance efforts. This study used phylogenetic analysis of full genome data, categorized by geo-region, to determine the sources of RSV A and B infection in Chile and Houston, Texas. Additionally, disease severity studies have generally focused on outcomes associated with a single genotype. In this study we developed a statistical phylogenetic approach to explore the relationship between tree topology and disease severity. Disease severity data included if the patient was given oxygen, if they were hospitalized, and if they were admitted to an intensive care unit. Global data was downloaded from GenBank, separated into RSV A and RSV B, aligned, and manually optimized. The United States and Canada region was overrepresented in the publicly available data, so subsampling was conducted to reduce selection bias. Starting trees were generated from the subsampled datasets using RAxML. Geographic traits and trait state transition rates were jointly estimated in a Bayesian statistical framework using BEAST. The global transmission network was estimated using the Bayesian stochastic search variable selection and a constant population with a HKY genetic substitution model. Trait associations were calculated using BaTS. For RSV A, the time to most recent common ancestor (tMRCA) was 1963.40 (95% BCI: 1946.15, 1969.60). For RSV B, the tMRCA was 1963.80 (95% BCI: 1959.50, 1967.33). Europe and Central Asia was a key source of RSV A and B transmissions for both Chile and Houston. In addition, the Middle East and North Africa and Latin America and the Caribbean were sources of RSV transmission into Houston. For the RSV A clinical data, there were significant associations between disease severity and tree topology when analyzing all three traits together (AI 3.13 p\u3c0.01, PS 22.39 p\u3c0.01) and for oxygen (AI 0.98 p\u3c0.01, PS 9.32 p\u3c0.01) and hospitalization independently (AI 1.92 p\u3c0.01, PS 11.78 p\u3c0.01). No significant association was found between tree topology and ICU admission. No significant associations were found in the RSV B clinical data, which may be due to the small sample size and homogeneous outcomes in this group. Improved surveillance systems are needed to gain a better understanding of global transmission patterns to complement studies done of local transmission patterns, as global introductions play an important role in local outbreaks. Identifying genetic mutations that lead to more severe outcomes may help researchers target vaccine development

Comparison of fecal microbiota of horses suffering from atypical myopathy and healthy co-grazers

Equine atypical myopathy (AM) is caused by hypoglycin A (HGA) and methylenecyclopropylglycine (MCPG) intoxication resulting from the ingestion of seeds or seedlings of some Acer tree species. Interestingly, not all horses pasturing in the same toxic environment develop signs of the disease. In other species, it has been shown that the intestinal microbiota has an impact on digestion, metabolism, immune stimulation and protection from disease. The objective of this study was to characterize and compare fecal microbiota of horses suffering from AM and healthy co-grazers. Furthermore, potential differences in fecal microbiota regarding the outcome of diseased animals were assessed. This prospective observational study included 59 horses with AM (29 survivors and 30 non-survivors) referred to three Belgian equine hospitals and 26 clinically healthy co-grazers simultaneously sharing contaminated pastures during spring and autumn outbreak periods. Fresh fecal samples (rectal or within 30min of defecation) were obtained from all horses and bacterial taxonomy profiling obtained by 16S amplicon sequencing was used to identify differentially distributed bacterial taxa between AM-affected horses and healthy co-grazers. Fecal microbial diversity and evenness were significantly (p &lt; 0.001) higher in AM-affected horses as compared with their non-affected co-grazers. The relative abundance of families Ruminococcaceae, Christensenellaceae and Akkermansiaceae were higher (p ≤ 0.001) whereas those of the Lachnospiraceae (p = 0.0053), Bacteroidales (p &lt; 0.0001) and Clostridiales (p = 0.0402) were lower in horses with AM, especially in those with a poor prognosis. While significant shifts were observed, it is still unclear whether they result from the disease or might be involved in the onset of disease pathogenesis

On Mixing in Pairwise Markov Random Fields with Application to Social Networks

International audienceWe consider pairwise Markov random fields which have a number of important applications in statistical physics, image processing and machine learning such as Ising model and labeling problem to name a couple. Our own motivation comes from the need to produce synthetic models for social networks with attributes. First, we give conditions for rapid mixing of the associated Glauber dynamics and consider interesting particular cases. Then, for pairwise Markov random fields with submodular energy functions we construct monotone perfect simulation