Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis.

Aerobic vaginitis (AV) is a complex vaginal dysbiosis that is thought to be caused by the micro-ecological change of the vaginal microbiota. While most studies have focused on how changes in the abundance of individual microbes are associated with the emergence of AV, we still do not have a complete mechanistic atlas of the microbe-AV link. Network modeling is central to understanding the structure and function of any microbial community assembly. By encapsulating the abundance of microbes as nodes and ecological interactions among microbes as edges, microbial networks can reveal how each microbe functions and how one microbe cooperate or compete with other microbes to mediate the dynamics of microbial communities. However, existing approaches can only estimate either the strength of microbe-microbe link or the direction of this link, failing to capture full topological characteristics of a network, especially from high-dimensional microbial data. We combine allometry scaling law and evolutionary game theory to derive a functional graph theory that can characterize bidirectional, signed, and weighted interaction networks from any data domain. We apply our theory to characterize the causal interdependence between microbial interactions and AV. From functional networks arising from different functional modules, we find that, as the only favorable genus from Firmicutes among all identified genera, the role of Lactobacillus in maintaining vaginal microbial symbiosis is enabled by upregulation from other microbes, rather than through any intrinsic capacity. Among Lactobacillus species, the proportion of L. crispatus to L. iners is positively associated with more healthy acid vaginal ecosystems. In a less healthy alkaline ecosystem, L. crispatus establishes a contradictory relationship with other microbes, leading to population decrease relative to L. iners. We identify topological changes of vaginal microbiota networks when the menstrual cycle of women changes from the follicular to luteal phases. Our network tool provides a mechanistic approach to disentangle the internal workings of the microbiota assembly and predict its causal relationships with human diseases including AV.

Preference prediction analysis based on graph model for environmental governance conflict.

A mathematical model for decision maker's preference prediction in environmental governance conflict is established based on the graph model for conflict resolution. The rapid economic development in many countries, over the past decades, has caused serious environmental pollution. Sewage companies are the main source of contamination since they are always wavering on the issue of environmental governance because of their profit-seeking nature. Environmental management departments cannot grasp the offending company preferences accurately. The problem of how to obtain decision maker's preference in environmental governance conflict is studied in this paper. The mathematical model established in this paper can obtain a preference set of one decision maker on the promise that the ideal conflict outcome and preference of the other decision makers are known. Then, preference value distribution information entropy is introduced to mine the preference information contained in the preference set, which means that it is possible to get the preference information of conflict opponent at their own ideal conflict outcome. All of these preference sets provide the possibility to choose the appropriate coping strategies and lead the conflict to the direction that some decision makers want. Finally, the effectiveness and superiority of the preference prediction analysis method is verified through a case study of "Chromium Pollution in Qujing County" which took place in China. The preference prediction analysis method in this paper can provide decision making support for the decision makers in environmental governance from strategic level.

FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits.

We describe a statistical protocol of how to reconstruct and dissect functional omnigenic multilayer interactome networks that mediate complex dynamic traits in a genome-wide association study (GWAS). This protocol, named FunGraph, can analyze how each locus affects phenotypic variation through its own direct effect and a complete set of indirect effects due to regulation by other loci co-existing in large-scale networks. FunGraph is applicable to any GWAS aimed to characterize the genetic architecture of dynamic phenotypic traits. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).

[Simulation Analysis of the Impact of COVID-19 Pandemic Control on Air Quality in Henan Province based on Machine Learning Algorithm].

To reduce the risks of COVID-19 on society and the health of the general public, necessary prevention and control measures were implemented throughout China in 2020. Consequently, air quality was greatly improved due to lower emissions. However, the improvement of air quality could also be closely related to meteorological conditions. During quarantine (January 27 to February, 2020), reductions were observed in the concentration of all air pollutants in Henan Province (PM2.5, PM10, SO2, CO, and NO2 decreased by 36.89%, 34.18%, 19.43%, 29.85%, and 58.51%, respectively) relative to measurements taken from January 1 to 26, 2020. The only exception was for the concentration of O3, which increased by 69.64%. This study evaluates the importance of meteorological conditions in air pollution, through simulation with a long-and-short-term memory network (LSTM) and a machine learning algorithm. Results show that meteorological conditions play a crucial role in air pollutant formation. Given favorable meteorological factors, the concentrations of pollutants could be reduced by 15%-30%, while the reduction due to anthropogenic emission control ranges from 6%-40%. During the epidemic, meteorological conditions and human emissions accounted for 34.84% and 34.81% of the increase in O3 concentration, respectively. The results show that primary pollutant concentrations are more sensitive to the intensity of anthropogenic emissions. However, secondary pollutants are more dependent on meteorological factors. Furthermore, a nonlinear relationship has been identified between O3 concentration and to emission intensity. Further investigation into the causes of the rise in O3 concentration is necessary to gain a greater understanding and better control of particulate matter and O3 pollution.

The Genomic Physics of COVID-19 Pathogenesis and Spread.

Coronavirus disease (COVID-19) spreads mainly through close contact of infected persons, but the molecular mechanisms underlying its pathogenesis and transmission remain unknown. Here, we propose a statistical physics model to coalesce all molecular entities into a cohesive network in which the roadmap of how each entity mediates the disease can be characterized. We argue that the process of how a transmitter transforms the virus into a recipient constitutes a triad unit that propagates COVID-19 along reticulate paths. Intrinsically, person-to-person transmissibility may be mediated by how genes interact transversely across transmitter, recipient, and viral genomes. We integrate quantitative genetic theory into hypergraph theory to code the main effects of the three genomes as nodes, pairwise cross-genome epistasis as edges, and high-order cross-genome epistasis as hyperedges in a series of mobile hypergraphs. Charting a genome-wide atlas of horizontally epistatic hypergraphs can facilitate the systematic characterization of the community genetic mechanisms underlying COVID-19 spread. This atlas can typically help design effective containment and mitigation strategies and screen and triage those more susceptible persons and those asymptomatic carriers who are incubation virus transmitters.

Evaluation of Harmonic Contributions for Multi Harmonic Sources System Based on Mixed Entropy Screening and an Improved Independent Component Analysis Method.

Evaluating the harmonic contributions of each nonlinear customer is important for harmonic mitigation in a power system with diverse and complex harmonic sources. The existing evaluation methods have two shortcomings: (1) the calculation accuracy is easily affected by background harmonics fluctuation; and (2) they rely on Global Positioning System (GPS) measurements, which is not economic when widely applied. In this paper, based on the properties of asynchronous measurements, we propose a model for evaluating harmonic contributions without GPS technology. In addition, based on the Gaussianity of the measured harmonic data, a mixed entropy screening mechanism is proposed to assess the fluctuation degree of the background harmonics for each data segment. Only the segments with relatively stable background harmonics are chosen for calculation, which reduces the impacts of the background harmonics in a certain degree. Additionally, complex independent component analysis, as a potential method to this field, is improved in this paper. During the calculation process, the sparseness of the mixed matrix in this method is used to reduce the optimization dimension and enhance the evaluation accuracy. The validity and the effectiveness of the proposed methods are verified through simulations and field case studies.

[Evolution and Evaluation of O3 and VOCs in Zhengzhou During the National Traditional Games of Ethnic Minorities Period].

During the National Traditional Games of Ethnic Minorities (NTGEM) 2019, air quality in Zhengzhou was analyzed to evaluate the impact of pollution prevention and control measures on Zhengzhou. Ground-observed meteorological and pollutant data as well as the chemical compositions of volatile organic compounds (VOCs) were investigated. The results showed that the six parameters of pollutants in the safeguard period in 2019 indicated a downward trend as compared with that during the same time in 2018, and the average concentrations of PM2.5 and PM10 were decreased by 16.2% and 25.1%, respectively. However, the average concentration of O3 was only reduced by 3.7%. The daily proportions of primary pollutants of O3 increased to 90% during the NTGEM, and the ozone pollution was severe in this period. Meanwhile, the concentration of total volatile organic compounds (TVOCs) in the safeguard period was 26.21×10-9, which was significantly lower than that during the historical period. Six emission sources of the VOCs were identified using PMF model, including vehicle exhaust (28%), LPG evaporation (21%), combustion source (16%), industrial emissions (15%), solvent utilization (15%), and biogenic VOCs (5%). During the NTGEM period, the control of combustion sources and industrial sources was evident.

[Heavy Metal Pollution Characteristics and Ecological and Health Risk Assessment of Atmospheric PM2.5 in a Living Area of Zhengzhou City].

To study the pollution characteristics of atmospheric heavy metal elements in a living area of Zhengzhou City, assess the potential ecological risks, and determine risks to resident health in this city, the Wuhan Tianhong TH-16A Airborne Particles Intelligent Sampler was used to collect atmospheric PM2.5 in Zhengzhou City. The mass concentrations of 17 metal elements were analyzed by ambient air determination of inorganic elements by ambient particle matter wavelength dispersive X-ray fluorescence spectrometry. The source of heavy metals was analyzed by the enrichment factor method and principal component analysis. The ecological risk index method and the US Environmental Protection Agency's health risk assessment method were used to evaluate the potential ecological risks and residents' health risks from Cr, Cd, Cu, Zn, Ni, Pb, As, and other elements. The results showed that metals with higher enrichment factor values were Cd, Sb, Pb, and As, and Cd had the highest enrichment factor value. The sources of metal elements in a living area of Zhengzhou City were mainly crust/burning coal, fuel, garbage burning, metallurgical dust, and vehicle emission. The single factor potential ecological hazard index values of Cd, Pb, Zn, As, Cu, Ni, and Cr were 70420.2, 255.3, 204.6, 71.5, 36.9, 24.0, and 5.1, respectively. Cd, As, and Cr in a living area of Zhengzhou City posed a cancer risk, and Cd was the most harmful. Mn had a non-carcinogenic risk.