We propose a new method inspired from statistical mechanics for extracting geometric information from undirected binary networks and generating random networks that conform to this geometry. In this method an undirected binary network is perceived as a thermodynamic system with a collection of permuted adjacency matrices as its states. The task of extracting information from the network is then reformulated as a discrete combinatorial optimization problem of searching for its ground state. To solve this problem, we apply multiple ensembles of temperature regulated Markov chains to establish an ultrametric geometry on the network. This geometry is equipped with a tree hierarchy that captures the multiscale community structure of the network. We translate this geometry into a Parisi adjacency matrix, which has a relative low energy level and is in the vicinity of the ground state. The Parisi adjacency matrix is then further optimized by making block permutations subject to the ultrametric geometry. The optimal matrix corresponds to the macrostate of the original network. An ensemble of random networks is then generated such that each of these networks conforms to this macrostate; the corresponding algorithm also provides an estimate of the size of this ensemble. By repeating this procedure at different scales of the ultrametric geometry of the network, it is possible to compute its evolution entropy, i.e. to estimate the evolution of its complexity as we move from a coarse to a ne description of its geometric structure. We demonstrate the performance of this method on simulated as well as real data networks.

Background. Chinese medicine (CM) has been used to relieve symptoms relevant to uterine fibroids. Objective. This study investigated the association between the use of CM and the incidence of uterine surgery in women with uterine fibroids. Subjects and Methods. This retrospective cohort study extracted records for 16,690 subjects diagnosed with a uterine fibroid between 2000 and 2003 from the National Health Insurance reimbursement database. The risk factors for surgery were examined via Cox proportional hazard analysis, and the difference in incidence of surgery between CM users and nonusers was compared using incidence rate ratios (IRRs) derived from Poisson's models. Results. After an average follow-up period of 4.5 years, the cumulative incidence of uterine surgery was significantly lower in CM users than CM nonusers (P < 0.0001). Compared to CM nonusers, CM users were more unlikely to undergo uterine surgery (adjusted hazard ratio = 0.18, 95% confidence interval (CI) = 0.17, 0.19). The incidence of surgery in CM users was dramatically different from that for CM nonusers (IRR = 0.17, 95% CI = 0.16, 0.18). Conclusion. The risk of uterine surgery among fibroid patients who used CM was significantly decreased, implying an effective treatment of fibroid-related symptoms provided by CM.

Background

Chinese medicine is commonly used and covered by health insurance to treat symptoms of uterine fibroids in Taiwan. This retrospective cohort study compared the consumption of conventional western medicine and medical cost between Chinese medicine (CM) users and nonusers among patients with uterine fibroids.

Methods

We extracted 44,122 patients diagnosed with uterine fibrosis between 1996 and 2010 from the National Health Insurance reimbursement database, which is a population-based database released by a government-run health insurance system. Multivariate linear regression models were used to find association between using Chinese medicine and the consumption of conventional medicine, and between using Chinese medicine and medical cost.

Results

The total fibroid-related conventional western medicine consumed by CM users was less than that by nonusers (β = -10.49, P < 0.0001). Three categories of conventional medicines, including antianemics (-3.50 days/year/patient, P < 0.0001), hemostatics (- 1.89 days/year/patient, P < 0.0001), and hormone-related agents (-3.13 days/year/patient, P < 0.0001), were used less in patients who were CM users. Moreover, although using CM increased 16.9 USD per patient in CM users annually (P < 0.0001), the total annual medical cost for treating fibroid was 5610 USD less in CM users than in nonusers (P < 0.0001).

Conclusions

Our results suggested that CM reduced the consumption of conventional medicine, and might be a potential therapeutic substitute for conventional western medicines to treat uterine fibroids with low cost.

The Pap test diagnosed cervical dysplasia, which could recover to normal or progress to cervical cancer (CC), is an early stage of cell abnormality before CC. This case-control study analyzed the differences in the risk to develop CC between Chinese medicine (CM) users and nonusers among women who had ever been diagnosed as having cervical dysplasia. A total of 750 CC patients with a cervical dysplasia history were collected between 1998 and 2011 from National Health Insurance Research Database, and controls were women with cervical dysplasia history but did not develop CC. Adjusted odds ratio (aOR) for developing CC was assessed using multivariable logistic regression after adjusting for age, urbanization of residence, and occupation. The proportion of using CM among CC patients was lower than that among CC nonpatients, with an aOR of 0.8. By analyzing the relationship between CC development and the frequency of CM usage, the trend test revealed a significant decreasing trend for developing CC among high-frequency CM users. Moreover, the most frequently used single herb high-frequency was Rheum palmatum (Da-Huang). The usage of CM might be an effective complementary method to prevent uterine cervix from progressing to CC after cervical dysplasia has occurred.

Data generated from a system of interest typically consists of measurements on many covariate features and possibly multiple response features across all subjects in a designated ensemble. Such data is naturally represented by one response-matrix against one covariate-matrix. A matrix lattice is an advantageous platform for simultaneously accommodating heterogeneous data types: continuous, discrete and categorical, and exploring hidden dependency among/between features and subjects. After each feature being individually renormalized with respect to its own histogram, the categorical version of mutual conditional entropy is evaluated for all pairs of response and covariate features according to the combinatorial information theory. Then, by applying Data Could Geometry (DCG) algorithmic computations on such a mutual conditional entropy matrix, multiple synergistic feature-groups are partitioned. Distinct synergistic feature-groups embrace distinct structures of dependency. The explicit details of dependency among members of synergistic features are seen through mutliscale compositions of blocks computed by a computing paradigm called Data Mechanics. We then propose a categorical pattern matching approach to establish a directed associative linkage: from the patterned response dependency to serial structured covariate dependency. The graphic display of such a directed associative linkage is termed an information flow and the degrees of association are evaluated via tree-to-tree mutual conditional entropy. This new universal way of discovering system knowledge is illustrated through five data sets. In each case, the emergent visible heterogeneity is an organization of discovered knowledge.

Natural plants consist of a hierarchical architecture featuring an intricate network of highly interconnected struts and channels that not only ensure extraordinary structural stability, but also allow efficient transport of nutrients and electrolytes throughout the entire plants. Here we show that a hyperaccumulation effect can allow efficient enrichment of selected metal ions (for example, Sn2+, Mn2+) in the halophytic plants, which can then be converted into three-dimensional carbon/metal oxide (3DC/MOx) nanocomposites with both the composition and structure hierarchy. The nanocomposites retain the 3D hierarchical porous network structure, with ultrafine MOx nanoparticles uniformly distributed in multi-layers of carbon derived from the cell wall, cytomembrane and tonoplast. It can simultaneously ensure efficient electron and ion transport and help withstand the mechanical stress during the repeated electrochemical cycles, enabling the active material to combine high specific capacities typical of batteries and the cycling stability of supercapacitors.

C–C coupling is a critical step of CO2 fixation in constructing the carbon skeleton of value-added multicarbon products. The Wood–Ljungdahl pathway is an efficient natural process through which microbes transform CO2 into methyl and carbonyl groups and subsequently couple them together. This asymmetric coupling mechanism remains largely unexplored in inorganic CO2 electroreduction. Here we experimentally validate the asymmetric coupling pathway through isotope-labelled co-reduction experiments on a Cu surface where 13CH3I and 12CO are co-fed externally as the methyl and the carbonyl source, respectively. Isotope-labelled multicarbon oxygenates were detected, which confirms an electrocatalytic asymmetric coupling on the Cu surface. We further employed tandem Cu–Ag nanoparticle systems in which *CHx and *CO intermediates can be generated to achieve asymmetric C–C coupling for a practical CO2 electroreduction. We found that the production of multicarbon oxygenates is correlated with the generation rate of two intermediate indicators, CH4 and CO. By aligning their rates, the oxygenates generation rate can be maximized. [Figure not available: see fulltext.]

The dynamic response of surface ligands on nanoparticles (NPs) to external stimuli critically determines the functionality of NP–ligand systems. For example, in electrocatalysis the collective dissociation of ligands on NP surfaces can lead to the creation of an NP/ordered-ligand interlayer, a microenvironment that is highly active and selective for CO2-to-CO conversion. However, the lack of in situ characterization techniques with high spatial resolution hampers a comprehensive molecular-level understanding of the mechanism of interlayer formation. Here we utilize in situ infrared nanospectroscopy and surface-enhanced Raman spectroscopy, unveiling an electrochemical bias-induced consecutive bond cleavage mechanism of surface ligands leading to formation of the NP/ordered-ligand interlayer. This real-time molecular insight could influence the design of confined localized fields in multiple catalytic systems. Moreover, the demonstrated capability of capturing nanometre-resolved, dynamic molecular-scale events holds promise for the advancement of using controlled local molecular behaviour to achieve desired functionalities across multiple research domains in nanoscience. (Figure presented.).