Network Inference Using the Hub Model and Variants

Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko (2019) propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. The set of members which can serve as a hub is called the hub set. The hub model belongs to the family of Bernoulli mixture models. Identifiability of Bernoulli mixture model parameters is a notoriously difficult problem. This paper proves identifiability of the hub model parameters and estimation consistency under mild conditions. Furthermore, this paper generalizes the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. We refer to this additional component as the null component. The new model bridges the gap between the hub model and the degenerate case of the mixture model -- the Bernoulli product. Identifiability and consistency are also proved for the new model. In addition, a penalized likelihood approach is proposed to estimate the hub set when it is unknown.Comment: arXiv admin note: substantial text overlap with arXiv:2004.0970

Quality analysis of Polygala tenuifolia root by ultrahigh performance liquid chromatography–tandem mass spectrometry and gas chromatography–mass spectrometry

Polygala tenuifolia root is used as a functional food due to its attractive health benefits. In this study, ultrahigh-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) and gas chromatography–mass spectrometry (GC-MS) were utilized to characterize the bioactive compounds in P. tenuifolia root. The UPLC-MS/MS information revealed 36 bioactive compounds, including oligosaccharide esters, polygalasaponins, and polygalaxanthones. GC-MS identified 34 volatile compounds with fatty acids as the main chemicals. The leading compound judged by UPLC-MS/MS was tenuifoliside A, and oleic acid was the leading volatile from GC-MS profiles. All samples tested showed similar bioactive compound compositions, but the level of each compound varied. Principal component analysis revealed the principal bioactive compounds with significant level variations between samples. These principal chemicals could be used for quality judgment of P. tenuifolia root, instead of measuring the levels of all compositional compounds

A bibliometric and visualized analysis of research on air pollution and cardiovascular diseases

A large number of studies have shown that air pollution has a great impact on cardiovascular diseases (CVD). However, there are few bibliometric studies or visual analyses in this field. The objective of this study was to research trends and hotspots of air pollution and CVD. We used CiteSpace and VOSviewer software to retrieve relevant studies from the Web of Science Core Collection (WoSCC) over the past decade. Amount to 4284 documents on air pollution and CVD were included in this study. The past decade saw an upward trend in the number of studies. The analysis of national publications showed that the United States had the highest academic contribution in this field. Peking University, the University of Washington and Harvard University were the main institutions studying the effect of air pollution on CVD. The cooperation among institutions with high publications was very close. Cluster analysis of the keywords listed four categories as follow: (1) oxidative stress and the cardiovascular effects of air pollution; (2) the cardiovascular effects of pollution exposure sources; (3) the relationship between environmental stressors and CVD; (4) personal-level interventions. This study puts forward a comprehensive summary of the trends and development of air pollution and CVD, confirms the research frontier and hotspot direction and could give a meaningful reference for researchers in this field