Methods: This paper introduces the concept of “order parameters” and proposes a method for establishing an order parameter model of gas discharge visualization (GDV) based on the principle of “mastering both permanence and change (MBPC)”. The method involved the following three steps. First, average luminous intensity () and average area () of the GDV images were calculated to construct the phase space, and the score of the health questionnaire was calculated as the health deviation index (H). Second, the k-means++ clustering method was employed to identify subclasses with the same health characteristics based on the data samples, and to statistically determine the symptom-specific frequencies of the subclasses. Third, the distance (d) between each sample and the “ideal health state”, which determined in the phase space of each subclass, was calculated as an order parameter describing the health imbalance, and a linear mapping was established between the d and the H. Further, the health implications of GDV signals were explored by analyzing subclass symptom profiles. We also compare the mean square error (MSE) with classification methods based on age, gender, and body mass index (BMI) indices to verify that the phase space possesses the ability to portray the health status of the human body. Results: This study preliminarily tested the reliability of the order parameter model on data samples provided by 20 participants. Based on the discovered linear law, the current model can use d calculated by measuring the GDV signal to predict H (R2 > 0.77). Combined with the symptom profiles of the subclasses, we explain the classification basis of the phase space based on the pattern identification. Compared with common classification methods based on age, gender, BMI, etc., the MSE of phase space-based classification was reduced by an order of magnitude. Conclusion In this study, the GDV order parameter model based on MBPC can identify subclasses and characterize individual health levels, and explore the TCM health meanings of the GDV signals by using subjective-objective methods, which holds significance for establishing mathematical models from TCM diagnosis principles to interpret human body signals.

Accurate prediction of the translation initiation site (TIS) is an important issue for prokaryotic genome annotation. However, it is still a challenge for the existing methods to predict the TIS in the genomes over a wide variety of GC content. Besides, the existing methods have not yet undergone a comprehensive evaluation, leaving prediction reliability as a largely open problem. A new algorithm MED-StartPlus, a tool that predicts TIS in prokaryotic genomes with a wide variety of GC content was presented. It makes several efforts to model the nucleotide composition bias, the regulatory motifs upstream of the TIS, the sequence patterns around the TIS, and the operon structure. Tests on hundreds of reliable data sets, with TISs confirmed by experiments or having annotated functions, show that the new method achieves a totally high accuracy of TIS prediction. Compared with existing TIS predictors, the method reports a totally higher performance, especially for genomes that are GC-rich or have complex initiation mechanisms. The potential application of the method to improve the TIS annotation deposited in the public database was also proposed.

In the study, a quantum resonant cavity model based on wave-particle duality was proposed for the explanation of the dynamic processes of essence, vigor, and spirit in the human body in traditional Chinese medicine (TCM). It is assumed that there is a macro human order parameter (wave function), and its dynamics are governed by a macro potential field reflecting influences from heaven, earth, and society, and satisfy the generalized Schrodinger equation. This proposed model was applied in the study to interpret basic concepts of human body in TCM, with an aim to unfold the TCM development in the future.

Following the quantum theory-based physical model of the human body, a new interpretation of the traditional Chinese medicine (TCM) principle of “Cunkou reads viscera” is presented. Then, a Gaussian pulse wave model as a solution to the Schrodinger equation is shown to accurately describe 19 different pulse shapes, and to quantitatively capture the degree of Yin-Yang attributes of 13 pulse shapes. Furthermore, the model suggests using pulse depth and strength as leading-order quantity and pulse shape as first-order quantity, to characterize the hierarchical resonance between the human body and the environment. The future pulse informatics will focus on determining an individual’s unique quantum human equilibrium state, and diagnose its health state according to the pulse deviation from its equilibrium state, to truly achieve the high level of TCM: “knowing the normal state and reaching the change”.