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Recently, Zhang et al. (Chin. Phys. B26 024208 (2017)) investigated the band gap structures and semi-Dirac point of two-dimensional function photonic crystals, and the equations for the plane wave expansion method were induced to obtain the band structures. That report shows the band diagrams with the effects of function coefficient k and medium column ra under TE and TM waves. The proposed results look correct at first glance, but the authors made some mistakes in their report. Thus, the calculated results in their paper are incorrect. According to our calculations, the errors in their report are corrected, and the correct band structures also are presented in this paper. [ABSTRACT FROM AUTHOR]

In the real world there are situations where the network dynamics are transient (e.g., various spreading processes) and the final nodal states represent the available data. Can the network topology be reconstructed based on data that are not time series? Assuming that an ensemble of the final nodal states resulting from statistically independent initial triggers (signals) of the spreading dynamics is available, we develop a maximum likelihood estimation-based framework to accurately infer the interaction topology. For dynamical processes that result in a binary final state, the framework enables network reconstruction based solely on the final nodal states. Additional information, such as the first arrival time of each signal at each node, can improve the reconstruction accuracy. For processes with a uniform final state, the first arrival times can be exploited to reconstruct the network. We derive a mathematical theory for our framework and validate its performance and robustness using various combinations of spreading dynamics and real-world network topologies. [ABSTRACT FROM AUTHOR]

Studies on how to model the interplay between diseases and behavioral responses (so-called coupled disease-behavior interaction) have attracted increasing attention. Owing to the lack of obvious clinical evidence of diseases, or the incomplete information related to the disease, the risks of infection cannot be perceived and may lead to inappropriate behavioral responses. Therefore, how to quantitatively analyze the impacts of asymptomatic infection on the interplay between diseases and behavioral responses is of particular importance. In this letter, under the complex network framework, we study the coupled disease-behavior interaction model by dividing infectious individuals into two states: U-state (without evident clinical symptoms, labelled as U) and I-state (with evident clinical symptoms, labelled as I). A susceptible individual can be infected by U- or I-nodes, however, since the U-nodes cannot be easily observed, susceptible individuals take behavioral responses only when they contact I-nodes. The mechanism is considered in the improved Susceptible-Infected-Susceptible (SIS) model and the improved Susceptible-Infected-Recovered (SIR) model, respectively. Then, one of the most concerned problems in spreading dynamics: the epidemic thresholds for the two models are given by two methods. The analytic results quantitatively describe the influence of different factors, such as asymptomatic infection, the awareness rate, the network structure, and so forth, on the epidemic thresholds. Moreover, because of the irreversible process of the SIR model, the suppression effect of the improved SIR model is weaker than the improved SIS model. [ABSTRACT FROM AUTHOR]

How to identify influential nodes in social networks is of theoretical significance, which relates to how to prevent epidemic spreading or cascading failure, how to accelerate information diffusion, and so on. In this letter, we make an attempt to find effective multiple spreaders in complex networks by generalizing the idea of the coloring problem in graph theory to complex networks. In our method, each node in a network is colored by one kind of color and nodes with the same color are sorted into an independent set. Then, for a given centrality descriptor, the nodes with the highest centrality in an independent set are chosen as multiple spreaders. Comparing this approach with the traditional method, in which nodes with the highest centrality from the entire network perspective are chosen, we find that our method is more effective in accelerating the spreading process and maximizing the spreading coverage than the traditional method, no matter in network models or in real social networks. Moreover, the low computational complexity of the coloring algorithm guarantees the potential applications of our method. [ABSTRACT FROM AUTHOR]

A key ingredient in social contagion dynamics is reinforcement, as adopting a certain social behavior requires verification of its credibility and legitimacy. Memory of nonredundant information plays an important role in reinforcement, which so far has eluded theoretical analysis. We first propose a general social contagion model with reinforcement derived from nonredundant information memory. Then, we develop a unified edge-based compartmental theory to analyze this model, and a remarkable agreement with numerics is obtained on some specific models. We use a spreading threshold model as a specific example to understand the memory effect, in which each individual adopts a social behavior only when the cumulative pieces of information that the individual received from his or her neighbors exceeds an adoption threshold. Through analysis and numerical simulations, we find that the memory characteristic markedly affects the dynamics as quantified by the final adoption size. Strikingly, we uncover a transition phenomenon in which the dependence of the final adoption size on some key parameters, such as the transmission probability, can change from being discontinuous to being continuous. The transition can be triggered by proper parameters and structural perturbations to the system, such as decreasing individuals' adoption threshold, increasing initial seed size, or enhancing the network heterogeneity. [ABSTRACT FROM AUTHOR]

Basic medical laboratory courses (BMLCs) play an important role in medical educational courses helping the student acquire three important skills of surgical operating, collaborative learning, and problem solving. The outcome-based student assessment (OBSA) is a learning evaluation method that establishes specific evaluation points based on performance of students in three aspects: surgical operating, collaborative learning, and problem solving in the BMLC curriculum practices. The purpose of the present randomized controlled trial study is to explore the efficiency of OBSA program in BMLCs. The 233 students attending BMLCs were randomly divided into 2 groups, 118 in the OBSA group and 115 in the control group. We conducted multiple-choice examination questions (MCQs) test and two questionnaires with the method of two-sample t test for statistics. The results of MCQs in total eight BMLC blocks showed that the academic performance of the OBSA group was significantly better than that of the control group (P < 0.05). In addition, the average scores of direct observation of procedural skills (DOPS) and mini-experimental evaluation exercise in OBSA group were significantly higher than those in control group (P < 0.05). The majority of the medical students preferred the OBSA and considered OBSA could effectively improve their surgical operating skills (83.9%), collaborative learning skills (92.1%), and problem-solving skills (91.1%). From the above, OBSA is an effective evaluation method for the implementation of the BMLC curriculum. [ABSTRACT FROM AUTHOR]

It has been recognized that many complex dynamical systems in the real world require a description in terms of multiplex networks, where a set of common, mutually connected nodes belong to distinct network layers and play a different role in each layer. In spite of recent progress toward data based inference of single-layer networks, to reconstruct complex systems with a multiplex structure remains largely open. In this paper, we articulate a mean-field based maximum likelihood estimation framework to address this problem. In a concrete manner, we reconstruct a class of prototypical duplex network systems hosting two categories of spreading dynamics, and we show that the structures of both layers can be simultaneously reconstructed from time series data. In addition to validating the framework using empirical and synthetic duplex networks, we carry out a detailed analysis to elucidate the impacts of network and dynamics parameters on the reconstruction accuracy and the robustness. [ABSTRACT FROM AUTHOR]

The RAS family of proto-oncogenes are among the most commonly mutated genes in human cancers and predict poor clinical outcome. Several mechanisms underlying oncogenic RAS transformation are well documented, including constitutive signaling through the RAF-MEK-ERK proproliferative pathway as well as the PI3K-AKT prosurvival pathway. Notably, control of redox balance has also been proposed to contribute to RAS transformation. However, how homeostasis between reactive oxygen species (ROS) and antioxidants, which have opposing effects in the cell, ultimately influence RAS-mediated transformation and tumor progression is still a matter of debate and the mechanisms involved have not been fully elucidated. Here, we show that oncogenic KRAS protects fibroblasts from oxidative stress by enhancing intracellular GSH levels. Using a whole transcriptome approach, we discovered that this is attributable to transcriptional up-regulation of xCT, the gene encoding the cystine/glutamate antiporter. This is in line with the function of xCT, which mediates the uptake of cystine, a precursor for GSH biosynthesis. Moreover, our results reveal that the ETS-1 transcription factor downstream of the RAS-RAF-MEK-ERK signaling cascade directly transactivates the xCT promoter in synergy with the ATF4 endoplasmic reticulum stress-associated transcription factor. Strikingly, xCT was found to be essential for oncogenic KRAS-mediated transformation in vitro and in vivo by mitigating oxidative stress, as knockdown of xCT strongly impaired growth of tumor xenografts established from KRAS-transformed cells. Overall, this study uncovers a mechanism by which oncogenic RAS preserves intracellular redox balance and identifies an unexpected role for xCT in supporting RAS-induced transformation and tumorigenicity. [ABSTRACT FROM AUTHOR]

Controlled self-assembly of the inorganic-organic hybrid compounds based on polyoxometalates (POMs) is hampered by the wide range of oxo and hydroxo units on the peripheries of POMs and many chemical parameters. In this work, aiming to understand the pH value influence on the POM/Ag/trz assembly (trz = 1,2,3-triazole), two new hybrid compounds based on POMs combining with metal-organic frameworks (MOFs), H[Ag27(trz)16(H2O)4][(AsW12O40)2] (1), and [Ag15(trz)8][AsW12O40] (2), were isolated under different pH value. The structural analysis reveals that the [Ag7(trz)4]3+ subunits and Keggin-type arsenotungstate are assembled into a complicated three-dimensional framework containing two distinct types of channels in compound 1. And the [Ag5(trz)4]+ subunits and Keggin-type arsenotungstate are assembled into a three-dimensional framework containing the single channel under the aid of Ag-Ag bonds in compound 2. In addition, compounds 1 and 2 were employed as anode materials of lithium-ion batteries (LIBs) and exhibited the promising electrochemical performances. [ABSTRACT FROM AUTHOR]

Background: The phenomenon that malignant cells can acquire stemness under specific stimuli, encompassed under the concept of cancer cell plasticity, has been well-described in epithelial malignancies. To our knowledge, cancer cell plasticity has not yet been described in hematopoietic cancers. To illustrate and study cancer cell plasticity in hematopoietic cancers, we employed an in-vitro experimental model of ALK-positive anaplastic large-cell lymphoma (ALK+ALCL) that is based on the phenotypic and functional dichotomy of these cells, with cells responsive to a Sox2 reporter (i.e. RR cells) being significantly more stem-like than those unresponsive to the reporter (i.e. RU cells).Methods: H2O2 was employed to trigger oxidative stress. GFP expression and luciferase activity, readouts of the Sox2 reporter activity, were quantified by using flow cytometry and luciferase activity assay, respectively. Doxorubicin-resistance and clonogenicity were assessed by using the MTS, methylcellulose colony formation and limiting dilution assays. Western blotting and quantitative PCR were used to assess the expression of various members of the Wnt/β-catenin pathway. Pull-down studies using a Sox2 binding consensus sequence were used to assess Sox2-DNA binding. Quercetin and 10074-G5 were used to inhibit β-catenin and MYC, respectively. siRNA was used to downregulate Sox2.Results: Under H2O2-induced oxidative stress, a substantial fraction of RU cells was found to convert to RR cells, as evidenced by their acquisition of GFP expression and luciferase activity. Compared to the native RU cells, converted RR cells had significantly higher levels of doxorubicin-resistance, clonogenicity and sphere formation. Converted RR cells were characterized by an upregulation of the Wnt/β-catenin/MYC/Sox2 signaling axis, previously found to be the key regulator of the RU/RR dichotomy in ALK+ALCL. Furthermore, Sox2 was found to bind to DNA efficiently in converted RR cells but not RU cells, and this finding correlated with significant elevations of several Sox2 downstream targets such as WNT2B and BCL9. Lastly, inhibition of β-catenin, MYC or Sox2 in RU cells significantly abrogated the H2O2-induced RU/RR conversion.Conclusions: We have demonstrated that cancer cell plasticity exists in ALK+ALCL, a type of hematopoietic cancer. In this cancer type, the Wnt/β-catenin/MYC/Sox2 axis is an important regulator of cancer cell plasticity. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effects of triptergium glycosides (TG) on expressions of monocyte chemoattractant protein-1 (MCP-1) and connective tissue growth factor (CTGF) in early diabetic nephropathy (DN) in rats, and explore its mechanism of renal protection. Methods: Thirty-two rats were divided into 4 groups: normal control (NC), DN, and DN-treated with TG (5 and 10 mg/kg/d) groups. After 8 weeks, body weight, blood glucose (BG), albumin (ALB), blood urea nitrogen (BUN), creatinine (SCr) and 24 h urinary total protein (UTP) of rats were determined. Additionally, expressions of CTGF, MCP-1 and ED-1 were detected by immunohistochemistry assay, while mRNA and protein expressions of CTGF and MCP-1 in kidney tissues were evaluated using reverse transcription-polymerase chain reaction (RT-PCR) and western blot technique. Results: BG, ALB, SCr, BUN and UTP in DN group were significantly increased (p < 0.01), compared with NC group. Compared with DN group, ALB (28.90 and 31.49 vs 23.13 g/L) and UTP (21.87 and 18.91 vs 37.19 mg/24 h) were significantly changed in TG groups (p < 0.05). ED-1 positive cells were significantly increased in DN group (p < 0.01), compared with NC group, whereas treatment with TG significantly reversed the increase (1.67 and 1.41 vs 2.73 in glomeruli, 9.86 and 9.49 VS 13.18 in glomerular interstitial, p < 0.01). Proteins and mRNA expressions of CTGF and MCP-1 significantly increased (p < 0.01) in DN group, compared with NC group, while their expressions in TG groups were reversed. Conclusion: TG ameliorates renal injury in diabetic rats via decreasing MCP-1 and CTGF expressions and reducing macrophage activation. [ABSTRACT FROM AUTHOR]

The spread of disease on complex networks has attracted wide attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight-based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained. Even in complex networks with the strong heterogeneous degree and weight distributions, this approach is used. We then propose an edge-weight-based removal strategy with different biases and find that such a strategy can effectively control the spread of epidemic when the highly weighted edges are preferentially removed, especially when the weight distribution of a network is extremely heterogenous. The theoretical results from the suggested method can accurately predict the above removal effectiveness. [ABSTRACT FROM AUTHOR]

An efficient algorithm that can properly identify the targets to immunize or quarantine for preventing an epidemic in a population without knowing the global structural information is of obvious importance. Typically, a population is characterized by its community structure and the heterogeneity in the weak ties among nodes bridging over communities. We propose and study an effective algorithm that searches for bridge hubs, which are bridge nodes with a larger number of weak ties, as immunizing targets based on the idea of referencing to an expanding friendship circle as a self-avoiding walk proceeds. Applying the algorithm to simulated networks and empirical networks constructed from social network data of five US universities, we show that the algorithm is more effective than other existing local algorithms for a given immunization coverage, with a reduced final epidemic ratio, lower peak prevalence and fewer nodes that need to be visited before identifying the target nodes. The effectiveness stems from the breaking up of community networks by successful searches on target nodes with more weak ties. The effectiveness remains robust even when errors exist in the structure of the networks.

We theoretically analyze a two-dimensional periodic structure consisting of period nonlinear split-ring resonators (SRRs) with varactor diode. The diode is loaded into the slit of the SRRs. Then, we demonstrate nonlinear phenomena of left-handed nonlinear SRRs. This paper introduces nonlinear SRRs based on left-handed (LH) media and simulates self-tuning mechanisms, bistable effects in the microwave frequency range. In addition, with the increase of input power, the nonlinear response of the SRRs becomes multi-valued, paving a way for creating bistable tunable metamaterials. [ABSTRACT FROM AUTHOR]

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems. [ABSTRACT FROM AUTHOR]

Time-varying community structures exist widely in real-world networks. However, previous studies on the dynamics of spreading seldom took this characteristic into account, especially those on social contagions. To study the effects of time-varying community structures on social contagions, we propose a non-Markovian social contagion model on time-varying community networks based on the activity-driven network model. A mean-field theory is developed to analyze the proposed model. Through theoretical analyses and numerical simulations, two hierarchical features of the behavior adoption processes are found. That is, when community strength is relatively large, the behavior can easily spread in one of the communities, while in the other community the spreading only occurs at higher behavioral information transmission rates. Meanwhile, in spatial-temporal evolution processes, hierarchical orders are observed for the behavior adoption. Moreover, under different information transmission rates, three distinctive patterns are demonstrated in the change of the whole network's final adoption proportion along with the growing community strength. Within a suitable range of transmission rate, an optimal community strength can be found that can maximize the final adoption proportion. Finally, compared with the average activity potential, the promoting or inhibiting of social contagions is much more influenced by the number of edges generated by active nodes. [ABSTRACT FROM AUTHOR]