Exploratory Compatibility Regularity of Traditional Chinese Medicine on Osteoarthritis Treatment: A Data Mining and Random Walk-Based Identification

Osteoarthritis (OA) is a degressive and complex disease which is a growing public health problem on a global scale. On basis of an in-house database consisting of clinical records of 13,083 OA patients, the Traditional Chinese Medicine (TCM) was divided into 4 categories of medicines on the basis of the curative properties of herbs. Due to the lack of depth and internal relationship in the calculation results of TCM compatibility law data mining methods such as statistics and frequency analysis, we use a variety of multidimensional complex network methods that can efficaciously find the compatibility law of TCM, including similarity measure, graphical visualization of network diagram, random walking, and propensity score methods. We summarize common couplet medicines utilized for the treatment of osteoarthritis. The similarity measure method was used to investigate the commonly used drugs for the treatment of osteoarthritis. The method of association rule analysis is used to recognize the compatibility between the components. On basis of the propensity score methods, the evaluation displayed that, compared with single drug, the drug group increased ESR, CRP, C3, C4, IgG, and IgA more efficiently. Concluding, a random walk model was constructed to assess drug efficacy. After applying a random walk model, while revealing the compatibility among different components of TCM, their therapeutic efficacy against OA is analyzed. We obtained four groups of drug combination clusters by similarity measure and 11 pairs of highly connected drugs by association rules, which are cardinal drug combinations in the prescription for the treatment of OA. We also found that different traditional drug pairs were associated with different laboratory indexes, and drug combinations could better optimize laboratory indexes. This study presented that the TCM constituents complement one another. Besides, the therapeutic effects resulting from a variety of combinations of these constituents are quite different.

Application of Random Walk Model in Mobile Communication Data Optimization

With the continuous development of society and economy, the development and application of mobile communications have become more and more popular, and more people have begun to use mobile devices for office, study and life. The quality of mobile communications network signals directly affects people’s experience of use. The scope of actual application has gradually increased with the renewal of the technology itself. Based on the random walk model, this paper constructs a mobile communication data optimization plan. Through data storage, transmission delay, and information total control, the optimization form of wireless network mobile communication data transmission performance is studied. Practice shows that this scheme can effectively improve communication quality and transmission efficiency.

Zebrafish airinemes optimize their shape between ballistic and diffusive search

In addition to diffusive signals, cells in tissue also communicate via long, thin cellular protrusions, such as airinemes in zebrafish. Before establishing communication, cellular protrusions must find their target cell. Here we demonstrate that the shape of airinemes in zebrafish are consistent with a finite persistent random walk model. The probability of contacting the target cell is maximized for a balance between ballistic search (straight) and diffusive search (highly curved, random). We find that the curvature of airinemes in zebrafish, extracted from live cell microscopy, is approximately the same value as the optimum in the simple persistent random walk model. We also explore the ability of the target cell to infer direction of the airineme's source, finding that there is a theoretical trade-off between search optimality and directional information. This provides a framework to characterize the shape, and performance objectives, of non-canonical cellular protrusions in general.

STATISTICAL PROPERTIES OF RANKINGS IN SPORTS AND GAMES

Any collection can be ranked. Sports and games are common examples of ranked systems: players and teams are constantly ranked using different methods. The statistical properties of rankings have been studied for almost a century in a variety of fields. More recently, data availability has allowed us to study rank dynamics: how elements of a ranking change in time. Here, we study the rank distributions and rank dynamics of 12 datasets from different sports and games. To study rank dynamics, we consider measures that we have defined previously: rank diversity, change probability, rank entropy, and rank complexity. We also introduce a new measure that we call “system closure” that reflects how many elements enter or leave the rankings in time. We use a random walk model to reproduce the observed rank dynamics, showing that a simple mechanism can generate similar statistical properties as the ones observed in the datasets. Our results show that while rank distributions vary considerably for different rankings, rank dynamics have similar behaviors, independently of the nature and competitiveness of the sport or game and its ranking method. Our results also suggest that our measures of rank dynamics are general and applicable for complex systems of different natures.