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Barrier membranes have been used for the treatment of alveolar bone loss caused by periodontal diseases or trauma. However, an optimal barrier membrane must satisfy multiple requirements simultaneously, which are challenging to combine into a single material. We herein report the design of a bioinspired membrane consisting of three functional layers. The primary layer is composed of clay nanosheets and chitin, which form a nacre-inspired laminated structure. A calcium phosphate mineral layer is deposited on the inner surface of the nacre-inspired layer, while a poly(lactic acid) layer is coated on the outer surface. The composite membrane integrates good mechanical strength and deformability because of the nacre-inspired structure, facilitating operations during the implant surgery. The mineral layer induces the osteogenic differentiation of bone marrow mesenchymal stem cells and increases the stiffness of the membrane, which is an important factor for the regeneration process. The poly(lactic acid) layer can prevent unwanted mineralization on the outer surface of the membrane in oral environments. Cell experiments reveal that the membrane exhibits good biocompatibility and anti-infiltration capability toward connective tissue/epithelium cells. Furthermore, in vitro analyses show that the membrane does not degrade too fast, allowing enough time for bone regeneration. In vivo experiments prove that the membrane can effectively induce better bone regeneration and higher trabecular bone density in alveolar bone defects. This study demonstrates the potential of this bioinspired triple-layered membrane with hierarchical structures as a promising barrier material for periodontal guided tissue regeneration. [ABSTRACT FROM AUTHOR]

A specific functional electric component is connected to any branch of the neural circuit, which can enhance the corresponding biological function as a smart sensor, whereas the temperature has a significant effect on the firing rhythm of neurons by regulating channel conductance and excitability. So for this paper, a controllable thermosensitive neuron model is established by paralleling two thermistors in the FitzHugh–Nagumo neural circuit. Especially, the thermistors are controlled by two switches, where three working modes can be selected, for which bifurcation diagrams, time response diagrams and spectral entropy (SE) complexity are used to analyze the dynamic behaviors in different operating modes. It is found that any modulation of thermistor can regulate the branch current and output voltage completely under the three working modes. More importantly, the neuronal activities show different mode transitions from periodic to bursts or chaos. The superior dynamic characteristics and rich firing behavior make the new model more suitable for images encryption. For this purpose, we consider switching the three thermosensitive neuron models continuously to obtain more complex chaotic sequences. Next, based on the feature of high pixel correlation among color image components, a 3D projection scrambling algorithm is designed, and then employed in combination with the universal gravitational diffusion method to propose a color image encryption scheme. In addition, the designed algorithm is executed by encrypting and decrypting some color images. The experimental results show that the algorithm cannot only encrypt the color image effectively, but also has no limitation on the size of the test image. It is worth mentioning that the designed encrypted algorithm has satisfactory security performance and considerable robustness against noise attacks and shearing attacks. The exploration of this paper about the thermosensitive neuron model and its application may provide some theoretical guidance and experimental basis in the field of secure communication. [ABSTRACT FROM AUTHOR]

Fidelity mechanics is formalized as a framework for investigating critical phenomena in quantum many-body systems. Fidelity temperature is introduced for quantifying quantum fluctuations, which, together with fidelity entropy and fidelity internal energy, constitute three basic state functions in fidelity mechanics, thus enabling us to formulate analogues of the four thermodynamic laws and Landauer's principle at zero temperature. Fidelity flows, which are irreversible, are defined and may be interpreted as an alternative form of renormalization group flows. Thus, fidelity mechanics offers a means to characterize both stable and unstable fixed points: divergent fidelity temperature for unstable fixed points and zero-fidelity temperature and (locally) maximal fidelity entropy for stable fixed points. In addition, fidelity entropy behaves differently at an unstable fixed point for topological phase transitions and at a stable fixed point for topological quantum states of matter. A detailed analysis of fidelity mechanical-state functions is presented for six fundamental models—the quantum spin- 1 / 2 XY model, the transverse-field quantum Ising model in a longitudinal field, the quantum spin- 1 / 2 XYZ model, the quantum spin- 1 / 2 XXZ model in a magnetic field, the quantum spin-1 XYZ model, and the spin- 1 / 2 Kitaev model on a honeycomb lattice for illustrative purposes. We also present an argument to justify why the thermodynamic, psychological/computational, and cosmological arrows of time should align with each other, with the psychological/computational arrow of time being singled out as a master arrow of time. [ABSTRACT FROM AUTHOR]

Neurons are connected through synapses, but it is not reasonable to equate the connection mode of synapses to an edge in the network due to synaptic plasticity whereas the magnetic field coupling can be considered to handle it. Therefore, in this paper, the interaction between HR neurons is realized by magnetic field coupling based on induction coil, and time delay is introduced to represent the lag in information transfer. First, the coexisting firing activities and synchronization behaviors in dual neuronal networks are numerically calculated, respectively, depending on the external stimulation current, coupling strength, time delays, and initial conditions. When the time delays are given, it is interesting to note that the infinite number of firing modes including chaotic firing, periodical firing, and quiescent state is induced by initial conditions. Due to the initial values, the types of synchronization consisting of complete synchronization, delayed synchronization, and asynchronization are then revealed under the framework of extreme multistability. In particular, the state of complete synchronization exhibits only quiescent state and period-1 firing when the time delay is not equal to 0. Furthermore, the linear augmentation method is conceived to control extreme multistability. It can be found that the attractors with different positions and topological structures can be controlled to the point attractors with the same shape but with different positions when the coupling strength of linear system and nonlinear system is increased. That is, the heterogeneous multistability can be successfully controlled to the homogeneous multistability, and the coupled neurons can also be achieved synchronization after control. These conclusions in this paper could be helpful in providing new insights for studying neurodynamics and applying neural circuits. [ABSTRACT FROM AUTHOR]

Bond strength of steel reinforcement embedded in ultra‐high‐performance fiber‐reinforced concrete (UHPFRC) plays a vital role in the post‐cracking behavior of reinforced UHPFRC members in flexure or tension. This paper presents an analytical study on the bond strength of steel reinforcement in UHPFRC. Three different bond‐slip models, namely the modified fib model, Marchand model and the proposed model are compared in the analytical method and explicit equations are also proposed. Comparisons with test data suggest that the proposed model and explicit equations can evaluate the bond‐slip behavior of reinforcement with good accuracy. Marchand model shows similar predictions of bond‐slip behavior to the proposed model at different load levels, whereas the modified fib model is accurate only when the reinforcement nearly yields in tension. The embedment length required for embedded reinforcement to develop its yield and ultimate strengths can also be calculated from the analytical method and explicit equations. [ABSTRACT FROM AUTHOR]

In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control. Author summary: Recently structural network control methods which exploit structural controllability of complex networks have become a powerful analysis tool for identifying driver nodes (e.g., driver genes or drug targets) in biological networked systems. In fact, the key point to apply structural network control on biological networked systems is how to characterize the state transition network (or interaction network) of these biological systems. The state transition network is a graph in which nodes denote the system variable and edges denote the significant interactions to trigger the state transition of system from one attractor (e.g., healthy state of individual patients) to another attractor (e.g., disease state of individual patients). The state transition network characterizes the state transition of networked system between any two attractors. Coincidentally, recently single sample network construction methods have been proposed to construct sample specific interaction network for characterizing the state transition of sample-specific biological systems. Therefore it is time to conduct a study to assess the performance of these state-of-the-art methods on the real-world biological systems, including sample-specific network construction methods and structural network control methods. Here we comprehensively provided a performance evaluation of 16 analysis workflows based on the combination of four sample-specific network reconstruction methods and four representative structural network control methods on a variety of real-world biological datasets. By this study, we have shed light on the relative behavior and performance of different analysis frameworks for evaluating and recommending these methods in particular biological application scenarios. In addition, we would like to point a few challenges for current methods and suggest several development directions of new SSC analysis workflows when more complex practice conditions of control principles are faced. [ABSTRACT FROM AUTHOR]

High‐throughput sequencing methods have facilitated the identification of novel selenoproteins, which exert a vital role in the development and progression of tumor diseases. Recently, Selenoprotein M (SELM) is upregulated in several types of cancer. However, the biological roles of SELM in renal cell carcinoma (RCC) remain unclear. In this paper, quantitative reverse transcription PCR (qRT‐PCR) and Western blot were used to measure relative levels of SELM in a cohort of RCC tissues with matched normal tissues as well as human RCC cell lines. SELM expression was found to be upregulated in RCC. High level of SELM was related to poor prognosis of RCC. Furthermore, silence of SELM could inhibit the in vitro proliferative, migratory, and invasive capacities of RCC. In addition, downregulated SELM could impede in vivo tumorigenesis of RCC. SELM could activate the PI3K/Akt/mTOR pathway and mediate expressions of matrix metallopeptidase 2 and 9 (MMP2, MMP9). In conclusion, our study reveals the oncogenic function of SELM in RCC, and SELM may be a therapeutic and prognostic target for RCC. [ABSTRACT FROM AUTHOR]

Motivation: It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective. Results: SCS integrates mutation data and expression data into a reference molecular network for each patient to obtain the driver mutation profiles in a personalized-sample manner. This is the first such a computational framework, to bridge the personalized driver mutation discovery problem and the structural network controllability problem. The key idea of SCS is to detect those mutated genes which can achieve the transition from the normal state to the disease state based on each individual omics data from network controllability perspective. We widely validate the driver mutation profiles of our SCS from three aspects: (i) the improved precision for the predicted driver genes in the population compared with other driver-focus methods; (ii) the effectiveness for discovering the personalized driver genes and (iii) the application to the risk assessment through the integration of the driver mutation signature and expression data, respectively, across the five distinct benchmarks from The Cancer Genome Atlas. In conclusion, our SCS makes efficient and robust personalized driver mutation profiles predictions, opening new avenues in personalized medicine and targeted cancer therapy. [ABSTRACT FROM AUTHOR]

Background: The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). Results: Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng. Conclusions: In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks. [ABSTRACT FROM AUTHOR]

Objective This mixed treatment comparison is used to compare the adverse effects of eleven different drugs used to treat Parkinson's disease ( PD). The drugs that we compare include the following: ropinirole, rasagiline, rotigotine, entacapone, apomorphine, pramipexole, sumanirole, bromocriptine, piribedil, pergolide, and levodopa. Methods PubMed, EMBASE, and Cochrane Library were searched from the inception to December 2015. Our analysis combines the evidences of direct comparison and indirect comparison between various literatures. We evaluated the merging odds ratios ( OR) value and surface under the cumulative ranking curves ( SUCRA) of each of the drugs and used this as a mode of comparison. Results Twenty-four randomized controlled trials ( RCTs) were included in this study. Our results demonstrated that the incidence of adverse reactions of ropinirole, rotigotine, entacapone, and sumanirole were obviously higher in terms of nausea compared to the placebo. Ropinirole produced the highest incidence rates of dyskinesia side effects, whereas pramipexole was significantly higher in terms of patients' hallucination. In addition, the SUCRA values of all the drugs showed that the incidence of adverse reaction of pergolide was relatively high (nausea: 83.5%; hallucination: 79.8%); for dyskinesia and somnolence, the incidence of ropinirole was higher (dyskinesia: 80.5%; somnolence: 69.4%); the incidence of adverse reaction of piribedil was higher on PD in terms of dizziness (67.0%); and the incidence of bromocriptine was relatively high in terms of constipation (62.3%). Conclusions This mixed treatment comparison showed that the drugs ropinirole, bromocriptine, and piribedil produced the highest incidence rates of nausea, dyskinesia, hallucination, dizziness, constipation, and somnolence symptoms. Thus, we conclude that as these three drugs produced the most frequent symptoms, they are not recommended for the treatment of patients with Parkinson's disease. [ABSTRACT FROM AUTHOR]

As an important resource of functional food, the seeds of tree peony are rich in phenolic compounds, which are associated with antioxidant activity. However, so far there has not been systematic study on phenolic compositions and antioxidant activity of the seeds from wild tree peony species. The aim of this study was to determine the phenolic content, antioxidant compounds and antioxidant activity of seeds from nine tree peony species native to China. Among the seed samples, Paeonia rockii had the highest total flavonoid content, strongest DPPH and ABTS radical scavenging activities, and strongest cupric reducing capacity; P . decomposita subsp. rotundiloba had the highest total phenolic and flavanol contents, as well as the strongest hydroxyl radical scavenging activity. Sixteen individual phenolic compounds were quantitatively measured, with (+)-catechin being the most abundant component. The content of the phenolic compounds luteolin, paeonol, and the total flavonoid content were significantly correlated with four antioxidant activities. Hierarchical cluster analysis showed that P . rockii and P . decomposita subsp. rotundiloba could be clustered in a group having a high phenolic content and strong antioxidant activity. These results suggest P . rockii and P . decomposita subsp. rotundiloba are the most promising candidates as useful sources of natural antioxidants. [ABSTRACT FROM AUTHOR]

In a closed-loop supply chain (CLSC), the right alliance can help manufacturers better manufacture green products and make more profits. Choosing the most suitable alliance partner is also critical for manufacturers. In regard to product greenness and recycling competition, this paper considers the CLSC comprised of a dominant manufacturer, a retailer, and a third-party recycler. Based on the Stackelberg game and equilibrium analysis, we discuss the optimal supply chain decision-making under four different models. Then, in order to ensure supply chain (SC) members' enthusiasm to participate in the alliance, we design a profit distribution method to distribute the total profit to SC members. The results show that manufacturer's optimal alliance decision is related to the degree of recycling competition. When less than the threshold, C alliance(the manufacturer make an alliance with the retailer and the third-party recycler at the same time) is optimal, otherwise, MR alliance(the manufacturer and the retailer make an alliance) is more beneficial for the manufacturer. [ABSTRACT FROM AUTHOR]

We propose a method to construct universal order parameters for quantum phase transitions in many-body lattice systems. The method exploits the H-orthogonality of a few near-degenerate lowest states of the Hamiltonian describing a given finite-size system, which makes it possible to perform finite-size scaling and take full advantage of currently available numerical algorithms. An explicit connection is established between the fidelity per site between two H-orthogonal states and the energy gap between the ground state and low-lying excited states in the finite-size system. The physical information encoded in this gap arising from finite-size fluctuations clarifies the origin of the universal order parameter. We demonstrate the procedure for the one-dimensional quantum formulation of the q-state Potts model, for q = 2, 3, 4 and 5, as prototypical examples, using finite-size data obtained from the density matrix renormalization group algorithm. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are non-coding RNA molecules that bind to mRNAs to regulate gene expression. Since changes in miRNA expression levels have been found in a variety of autoimmune illnesses, miRNAs are important in autoimmune diseases. MiRNAs serve not only as pathogenic factors and biomarkers for autoimmune diseases but also as important targets for disease therapeutics. Although miRNA-based treatments are still in the research stage, in-depth investigations into the biological functions of miRNAs have significantly enhanced our understanding of their mechanisms in autoimmune diseases. The purpose of this review is to summarize the biological functions of miRNAs, their roles in rheumatoid arthritis and systemic lupus erythematosus, therapeutic strategies, and challenges., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Root-knot nematodes (RKNs) are highly specialized parasites that cause significant yield losses worldwide. In this study, we isolated Bacillus pumilus strain S1-10 from the rhizosphere soil of Zingiber officinale Rosc. plants and evaluated its fumigant activity against Meloidogyne incognita . S1-10 exhibited a strong repellent effect on second-stage juveniles (J2s) of M. incognita , and in vitro assays indicated that S1-10 volatile organic compounds (VOCs) suppressed J2 activity and egg hatching. Under greenhouse conditions, 71 and 79% reductions of nematodes and eggs were detected on plants treated with S-10 VOCs compared with controls. Ten VOCs were identified through gas chromatography and mass spectrometry (GC-MS), of which 2-(methylamino)-ethanol (2-ME) had strong fumigant activity against J2s of M. incognita , with an LC 50 value of 1.5 mM at 12 h. These results indicate that S1-10 represents a potential novel biocontrol agent for RKNs., Competing Interests: The author(s) declare no conflict of interest.

Micheliolide (MCL) derivatives with etherification or esterification of the hydroxyl group at the C4 position were synthesized and evaluated for their activities against different acute myelogenous leukemia (AML) cell lines. These derivatives demonstrated comparable activities against AML cell lines HL-60 and doxorubicin resistant cell line HL-60/A. As to multi-drug resistant AML progenitor cells KG-1a, MCL and some of its derivatives maintained significant activities, and only 1.1-2.7 fold activity reductions were observed when compared with the activities against HL-60, while doxorubicin showed 20-fold activity reduction. Our study demonstrated that the C4 hydroxyl group of MCL might not only be a suitable position for structural modifications, but also be a starting point for the design of appropriate molecular probes to explore the specific targets in the progenitor cell line KG-1a.

For any translation-invariant quantum lattice system with a symmetry group G, we propose a practical and universal construction of order parameters which identify quantum phase transitions with symmetry-breaking order. They are defined in terms of the fidelity between a ground state and its symmetry-transformed counterpart, and are computed through tensor network representations of the ground-state wave function. To illustrate our scheme, we consider three quantum systems on an infinite lattice in one spatial dimension, namely, the quantum Ising model in a transverse magnetic field, the quantum spin-1/2XYX model in an external magnetic field, and the quantum spin-1 XXZ model with single-ion anisotropy. All these models have symmetry group Z(2) and exhibit broken-symmetry phases. We also discuss the role of the order parameters in identifying factorized states.