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Molecular fingerprints are significant cheminformatics tools to map molecules into vectorial space according to their characteristics in diverse functional groups, atom sequences, and other topological structures. In this paper, we set out to investigate a novel molecular fingerprint \emph{Anonymous-FP} that possesses abundant perception about the underlying interactions shaped in small, medium, and large molecular scale links. In detail, the possible inherent atom chains are sampled from each molecule and are extended in a certain anonymous pattern. After that, the molecular fingerprint \emph{Anonymous-FP} is encoded in virtue of the Natural Language Processing technique \emph{PV-DBOW}. \emph{Anonymous-FP} is studied on molecular property identification and has shown valuable advantages such as rich information content, high experimental performance, and full structural significance. During the experimental verification, the scale of the atom chain or its anonymous manner matters significantly to the overall representation ability of \emph{Anonymous-FP}. Generally, the typical scale $r = 8$ enhances the performance on a series of real-world molecules, and specifically, the accuracy could level up to above $93\%$ on all NCI datasets., 11 pages

Autonomous individuals establish a structural complex system through pairwise connections and interactions. Notably, the evolution reflects the dynamic nature of each complex system since it recodes a series of temporal changes from the past, the present into the future. Different systems follow distinct evolutionary trajectories, which can serve as distinguishing traits for system classification. However, modeling a complex system's evolution is challenging for the graph model because the graph is typically a snapshot of the static status of a system, and thereby hard to manifest the long-term evolutionary traits of a system entirely. To address this challenge, we suggest utilizing a heat-driven method to generate temporal graph augmentation. This approach incorporates the physics-based heat kernel and DropNode technique to transform each static graph into a sequence of temporal ones. This approach effectively describes the evolutional behaviours of the system, including the retention or disappearance of elements at each time point based on the distributed heat on each node. Additionally, we propose a dynamic time-wrapping distance GDTW to quantitatively measure the distance between pairwise evolutionary systems through optimal matching. The resulting approach, called the Evolution Kernel method, has been successfully applied to classification problems in real-world structural graph datasets. The results yield significant improvements in supervised classification accuracy over a series of baseline methods.

Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual's activity rate and the possibility of group interaction, we propose a probabilistic activity driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems. Surprisingly, the power-law exponents and the clustering coefficients of the aggregated PAD network could be tuned in a wide range by altering a set of model parameters. We further provide an approximation algorithm to select the proper parameters that can generate networks with given structural properties, the effectiveness of which is verified by fitting various real-world networks. Lastly, we explore the co-evolution of PAD model and higher-order contagion dynamics, and analytically derive the critical conditions for phase transition and bistable phenomenon. Our model provides a basic tool to reproduce complex structural properties and to study the widespread higher-order dynamics, which has great potential for applications across fields.

Environmental changes play a critical role in determining the evolution of social dilemmas in many natural or social systems. Generally, the environmental changes include two prominent aspects: the global time-dependent fluctuations and the local strategy-dependent feedbacks. However, the impacts of these two types of environmental changes have only been studied separately, a complete picture of the environmental effects exerted by the combination of these two aspects remains unclear. Here we develop a theoretical framework that integrates group strategic behaviors with their general dynamic environments, where the global environmental fluctuations are associated with a nonlinear factor in public goods game and the local environmental feedbacks are described by the `eco-evolutionary game'. We show how the coupled dynamics of local game-environment evolution differs in static and dynamic global environments. In particular, we find the emergence of cyclic evolutions of group cooperation and local environment, which forms an interior irregular loop in the phase plane, depending on the relative changing speed of both global and local environments compared to the strategic change. Our results provide important insights toward how diverse evolutionary outcomes could emerge from the nonlinear interactions between strategies and the changing environments.

Brain health is an important research direction of neuroscience. In addition to the effects of diseases, we cannot ignore the negative effect of aging on brain health. There have been many studies on brain aging, but only a few have used dynamic models to analyze differences in micro brain characteristics in healthy people. In this article, we use the relaxed mean-field model (rMFM) to study the effects of normal aging. Two main parameters of this model are the recurrent connection strength and subcortical input strength. The sensitivity of the rMFM to the initial values of the parameters has not been fully discussed in previous research. We examine this issue through repeated numerical experiments and obtain a reasonable initial parameter range for this model. Differences in recurrent connection strength and subcortical input strength due to aging have also not been studied previously. We use statistical methods to find the regions of interest (ROIs) exhibiting significant differences between young and old groups. Further, we carry out a difference analysis on the process of change of these ROIs on a more detailed timescale. We find that even with the same final results, the trends of change in these ROIs are different. This shows that to develop possible methods to prevent or delay brain damage due to aging, more attention needs to be paid to the trends of change of different ROIs, not just the final results.

ObjectivesDynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) plays an important role in the differential diagnosis between radionecrosis and recurrence of brain metastases (BMs) after gamma knife radiosurgery (GKRS). While the perfusion condition of preliminary hyperperfusion and hypoperfusion BMs when recur has not been studied, as well the separating performance of quantitative DSC-PWI in both kinds of BMs.MethodsFrom February 2017 to October 2019, quantitative DSC-PWI was performed in patients with untreated BMs in this observational study. Patients were assigned to hyperperfusion and hypoperfusion group according the quantitative cerebral blood volume (qCBV). During follow-up after GKRS, patients with a diagnostic pitfall of radionecrosis and recurrence accepted second quantitative DSC-PWI. Final diagnosis was based on the histological results or follow-up results. Receiver operating curve analysis was used to explore the performance of qCBV.ResultsTwenty-nine patients (mean age: 61.3 ± 9.4 years old; male/female: 13/16) were assigned to the group of hypoperfusion group, and 26 patients (mean age: 58 ± 10.4 years old; male/female: 14/12) to hyperperfusion group. The mean qCBV values between hypoperfusion and hyperperfusion groups when recurred were not significantly different (3.17 ± 0.53 ml/100 g vs. 3.27 ± 0.47 ml/100 g, p = 0.63). qCBV was feasible to separate radionecrosis and recurrence in both groups (AUC=0.94 and AUC=0.93, separately).ConclusionBoth premilitary hyperperfusion and hypoperfusion BMs would transform to a high microvascular density when recurs. qCBV is feasible to distinguish radionecrosis and recurrence among both kinds of BMs after GKRS.

Deep learning utilizing deep neural networks (DNNs) has achieved a lot of success recently in many important areas such as computer vision, natural language processing, and recommendation systems. The lack of convexity for DNNs has been seen as a major disadvantage of many optimization methods, such as stochastic gradient descent, which greatly reduces the genelization of neural network applications. We realize that the convexity make sense in the neural network and propose the exponential multilayer neural network (EMLP), a class of parameter convex neural network (PCNN) which is convex with regard to the parameters of the neural network under some conditions that can be realized. Besides, we propose the convexity metric for the two-layer EGCN and test the accuracy when the convexity metric changes. For late experiments, we use the same architecture to make the exponential graph convolutional network (EGCN) and do the experiment on the graph classificaion dataset in which our model EGCN performs better than the graph convolutional network (GCN) and the graph attention network (GAT)., Comment: 10 pages

Additional file 1: Table S1. Strains and plasmids used in this study. Table S2. Primers used in this study. Fig. S1. Predict the transmembrane domain of MenA in B. subtilis. Fig. S2. Colony PCR of the engineered strain BS011.Fig. S3. Fluorescence intensity curve of amplified pos5P. Note S1. Deducing the overall stoichiometry of glycerol conversion to DMK-7 in B. subtilis. Note S2. Abbreviations. Note S3. Supplementary sequence.

Additional file 1: Figure S1. Terpenoid backbone biosynthesis of E. meningoseptica sp. F2 based on KEGG pathway assignment. A-CoA acetyl coenzyme A; AA-CoA acetoacetyl-CoA; HMG-CoA hydroxymethylglutaryl-CoA; IPP isopentenyl diphosphate; DMAPP dimethylallyl diphosphate; GPP geranyl diphosphate; FPP farnesyl diphosphate; GGPP geranylgeranyl diphosphate; OPP octaprenyl diphosphate. Figure S2. Chromatography of MK-n (n = 4, 5, 6, 7, 8) by HPLC system. A The sample was extracted from monoculture system of E. meningoseptica, B the sample was extracted from coculture system of E. meningoseptica and E. coli. Figure S3. A The purification of GB1-OPPS. M: protein molecular weight marker; lane 1: The total cell lysate supernatant flows through the column. Lanes 2���6: wash fractions with 10, 15, 20, 25, 30 mM imidazole, respectively. Lanes 7���8: Elution fractions with 250 mM imidazole, respectively. B Purified GB1-EmOPPS proteins was confirmed by mass spectrometry. Figure S4. Enzymatic assay of EmOPPS in vivo and vitro. A, B LC-MS analysis of the GB1-EmOPPS products with farnesyl diphosphate (FPP) and isopentenyl diphosphate (IPP) as substrates after hydrolyzation. C, D J01 strains could accumulated MK-n (n = 6, 7, 8). LC-MS analysis of the production of this strains. A, C Horizontal graphs refer to result of HPLC and vertical graphs B, D refer to mass spectra. Figure S5. The comparison of the mass spectrum of the fermentation results of the J01, J02 strain with that of the MK-n (n = 4, 5, 6, 7) standard, A MK-4, MK-5, MK-6, and MK-7 standard, respectively, for MS analysis. B The mass spectrum results of monocultured E. coli producing MK-8 as a reference. Figure S6. Analysis and identification of mevalonolactone by GC-MS from fermentation supernatant. Figure S7. The monoculture of different E. coli strains as comparison. The error bars represent the standard error of at least three biological replicates. Statistical significance (p < 0.01) compared to the original strains. Figure S8. Visualization of different inocula by confocal laser scanning microscope. A E. meningoseptica. B E. coli. C, D E. meningoseptica and E. coli co-culture. Red circles surround the connected E. meningoseptica and E. coli cells in view. C Bright field; D fluorescence image merged image. Table S1. Masses and identities of LC/MS analyses results. Table S2. Oligonucleotide primers used in this study.

In high speed CNC (Compute Numerical Control) machining, the feed rate scheduling has played an important role to ensure machining quality and machining efficiency. In this paper, a novel feed rate scheduling method is proposed for generating smooth feed rate profile conveniently with the consideration of both geometric error and kinematic error. First, a relationship between feed rate value and chord error is applied to determine the feed rate curve. Then, breaking points, which can split whole curve into several blocks, can be found out using proposed two step screening method. For every block, a feed rate profile based on the Sigmoid function is generated. With the consideration of kinematic limitation and machining efficiency, a time-optimal feed rate adjustment algorithm is proposed to further adjust feed rate value at breaking points. After planning feed rate profile for each block, all blocks feed rate profile will be connected smoothly. The resulting feed rate profile is more concise compared with the polynomial profile and more efficient compared with the trigonometric profile. Finally, simulations with two free-form NURBS curves are conducted and comparison with the sine-curve method are carried out to verify the feasibility and applicability of the proposed method.

Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning. And it turns out that the low-dimensional embedded representation obtained by graph representation learning are extremely useful in various typical tasks, such as node classification, content recommendation and link prediction. However, the existing methods mostly start from the microstructure (i.e., the edges) in the graph, ignoring the mesoscopic structure (high-order local structure). Here, we propose wGCN -- a novel framework that utilizes random walk to obtain the node-specific mesoscopic structures of the graph, and utilizes these mesoscopic structures to reconstruct the graph And organize the characteristic information of the nodes. Our method can effectively generate node embeddings for previously unseen data, which has been proven in a series of experiments conducted on citation networks and social networks (our method has advantages over baseline methods). We believe that combining high-order local structural information can more efficiently explore the potential of the network, which will greatly improve the learning efficiency of graph neural network and promote the establishment of new learning models., Comment: 8 pages, 3 figures. arXiv admin note: text overlap with arXiv:2007.15838

Feedrate scheduling is a key step in computer numerical control (CNC) machining, as it has a close relationship with machining time and surface quality, and has now become a hot issue in industry and academia. To reduce high chord errors and round-off errors, and generate continuous velocity, acceleration, and jerk profile of parametric interpolation, a novel and complete S-shape based feedrate scheduling algorithm is presented in this paper. The algorithm consists of three modules: bidirectional scanning module, velocity scheduling module and round-off error elimination module. The bidirectional scanning module with the limitations of chord error, normal acceleration/jerk and command feedrate aims to guarantee the continuity of the feed rate at the junctions between successive NURBS sub-curves. After the NURBS sub-curves have been classified into two cases, the velocity scheduling module firstly calculates the actual maximum federate, and then generates the feed rate profiles of all NURBS sub-curves according to our velocity scheduling function. Later, the round-off error elimination module is proposed to make the total interpolating time become an integer multiple of the interpolation period, which leads to the elimination of round-off errors. Finally, benchmarks are conducted to verify the applicability of the proposed method compared with some other methods.

First-order methods like stochastic gradient descent(SGD) are recently the popular optimization method to train deep neural networks (DNNs), but second-order methods are scarcely used because of the overpriced computing cost in getting the high-order information. In this paper, we propose the Damped Newton Stochastic Gradient Descent(DN-SGD) method and Stochastic Gradient Descent Damped Newton(SGD-DN) method to train DNNs for regression problems with Mean Square Error(MSE) and classification problems with Cross-Entropy Loss(CEL), which is inspired by a proved fact that the hessian matrix of last layer of DNNs is always semi-definite. Different from other second-order methods to estimate the hessian matrix of all parameters, our methods just accurately compute a small part of the parameters, which greatly reduces the computational cost and makes convergence of the learning process much faster and more accurate than SGD. Several numerical experiments on real datesets are performed to verify the effectiveness of our methods for regression and classification problems., Comment: 10 pages

Binary decision-making process is ubiquitous in social life and is of vital significance in many real-world issues, ranging from public health to political campaigns. While continuous opinion evolution independent of discrete choice behavior has been extensively studied, few works unveil how the group binary decision-making result is determined by the coupled dynamics of these two processes. To this end, we propose an agent-based model to study the collective behaviors of individual binary decision-making process through competitive opinion dynamics on social networks. Three key factors are considered: bounded confidence that describes the cognitive scope of the population, stubbornness level that characterizes the opinion updating speed, and the opinion strength that represents the asymmetry power or attractiveness of the two choices. We find that bounded confidence plays an important role in determining competing evolution results. As bounded confidence grows, population opinions experience polarization to consensus, leading to the emergence of phase transition from co-existence to winner-takes-all state under binary decisions. Of particular interest, we show how the combined effects of bounded confidence and asymmetry opinion strength may reverse the initial supportive advantage in competitive dynamics. Notably, our model qualitatively reproduces the important dynamical pattern during a brutal competition, namely, cascading collapse, as observed by real data. Finally and intriguingly, we find that individual cognitive heterogeneity can bring about randomness and unpredictability in binary decision-making process, leading to the emergence of indeterministic oscillation. Our results reveal how the diverse behavioral patterns of binary decision-making can be interpreted by the complicated interactions of the proposed elements, which provides important insights toward competitive dynamics

Conversion-type transition-metal phosphide anode materials with high theoretical capacity usually suffer from low-rate capability and severe capacity decay, which are mainly caused by their inferior electronic conductivities and large volumetric variations together with the poor reversibility of discharge product (Li3P), impeding their practical applications. Herein, guided by density functional theory calculations, these obstacles are simultaneously mitigated by confining amorphous FeP nanoparticles into ultrathin 3D interconnected P-doped porous carbon nanosheets (denoted as FeP@CNs) via a facile approach, forming an intriguing 3D flake-CNs-like configuration. As an anode for lithium-ion batteries (LIBs), the resulting FeP@CNs electrode not only reaches a high reversible capacity (837 mA h g-1 after 300 cycles at 0.2 A g-1) and an exceptional rate capability (403 mA h g-1 at 16 A g-1) but also exhibits extraordinary durability (2500 cycles, 563 mA h g-1 at 4 A g-1, 98% capacity retention). By combining DFT calculations, in situ transmission electron microscopy, and a suite of ex situ microscopic and spectroscopic techniques, we show that the superior performances of FeP@CNs anode originate from its prominent structural and compositional merits, which render fast electron/ion-transport kinetics and abundant active sites (amorphous FeP nanoparticles and structural defects in P-doped CNs) for charge storage, promote the reversibility of conversion reactions, and buffer the volume variations while preventing pulverization/aggregation of FeP during cycling, thus enabling a high rate and highly durable lithium storage. Furthermore, a full cell composed of the prelithiated FeP@CNs anode and commercial LiFePO4 cathode exhibits impressive rate performance while maintaining superior cycling stability. This work fundamentally and experimentally presents a facile and effective structural engineering strategy for markedly improving the performance of conversion-type anodes for advanced LIBs.

Background The species in family Elizabethkingia meningoseptica are interesting strain for investigating Vitamin K2 metabolic analysis. However, their genomic sequence, metabolic pathway, potential abilities, and evolutionary status are still unknown. Results This study therefore aimed to perform a genome sequencing of Elizabethkingia meningoseptica sp. F2 and further accomplished comparative analysis with other Vitamin K2 strains reveals overall identifying its unique/shared metabolic genes across genomes. The 3,874,794–base pair sequence of Elizabethkingia meningoseptica sp. F2 is presented. Of 3,539 genes annotation was applied. Results of synteny block demonstrated Elizabethkingia meningoseptica sp. F2 shares high levels of synteny with Elizabethkingia meningoseptica ATCC 13253 and Elizabethkingia meningoseptica NBRC 12535. Identification of Vitamin K2 metabolic pathway in Elizabethkingia meningoseptica sp. F2 were also accomplished. In addition, Elizabethkingia meningoseptica sp. F2 was resistant to gentamicin, streptomycin, ampicillin and caramycin, consistent with the presence of multiple genes encoding diverse multidrug efflux pump protein in the genome. Furthermore, By co-overexpression experiments of MenA and MenG, we showed that Vitamin K2 content was enhanced by 37% compared with control strain. Conclusions The genome analysis of Elizabethkingia meningoseptica sp. F2 in conjunction with the comparative metabolic pathways analysis among the E.coli, Bacillus subtilis and Streptomyces provided a useful information on the Vitamin K2 biosynthetic pathway and other related pathways at systems level.

Confirmation bias and peer pressure both have substantial impacts on the formation of collective decisions. Nevertheless, few attempts have been made to study how the interplay between these two mechanisms affects public opinion evolution. Here we propose an agent-based model of opinion dynamics which incorporates the conjugate effect of confirmation bias (characterized by the population identity scope and individual stubbornness) and peer pressure (described by a susceptibility threshold). We show that the number of opinion fragments first increases and then decreases to one as population identity scope becomes larger in a homogeneous population. Further, in heterogeneous situations, we find that even a small fraction of impressionable individuals who are sensitive to peer pressure could help eliminate public polarization when population identity scope is relatively large. Intriguingly, we highlight the emergence of 'impressionable moderates' who are easily influenced, hold wavering opinions, and are of vital significance in competitive campaigns.

Social reinforcement mechanism, which characterizes the promoting effects when exposing to multiple sources in social contagion process, is ubiquitous in information-technology ecosystem and has aroused great attention in recent years. While the impacts of social reinforcement on single-layer networks are well-documented, extension to multilayer networks is needed to study how reinforcement from different social circles influences the spreading dynamics. To this end, we incorporate multilayer social reinforcement into ignorant-spreader-ignorant (SIS) model on multiplex networks. Our theoretical analysis combines pairwise method and mean-field theory and agrees well with large-scale simulations. Surprisingly, we find this complex social contagion mechanism triggers the emergence of bistability phenomena, where extinction and outbreak states coexist. In particular, the hysteresis loop of stationary prevalence occurs in this bistable region, explaining why the fight towards the spread of rumors is protracted and difficult in modern society. Further, we show that the final state of bistable regions depends on the initial density of adopters, the critical value of which decreases as the contagion transmissibility or the multilayer reinforcement increases. In particular, we highlight two possible conditions for the outbreak of social contagion: to possess large contagion transmissibility, or to possess large initial density of adopters with strong multilayer reinforcement. Our results unveil the non-negligible power of social reinforcement on multiplex networks, which sheds lights on designing efficient strategies in spreading behaviors such as marketing and promoting innovations.

Competitive information diffusion on large-scale social networks reveals fundamental characteristics of rumor contagions and has profound influence on public opinion formation. There has been growing interest in exploring dynamical mechanisms of the competing evolutions recently. Nevertheless, the impacts of population homophily, which determines powerful collective human behaviors, remains unclear. In this paper, we incorporate homophily effects into a modified competitive ignorant-spreader-ignorant (SIS) rumor diffusion model with generalized population preference. Using microscopic Markov chain approach, we first derive the phase diagram of competing diffusion results and examine how competitive information spreads and evolves on social networks. We then explore the detailed effects of homophily, which is modeled by a rewiring mechanism. Results show that homophily promotes the formation of divided "echo chambers" and protects the disadvantaged information from extinction, which further changes or even reverses the evolutionary advantage, i.e., the difference of final proportions of the competitive information. We highlight the conclusion that the reversals may happen only when the initially disadvantaged information has stronger transmission ability, owning diffusion advantage over the other one. Our framework provides profound insight into competing dynamics with population homophily, which may pave ways for further controlling misinformation and guiding public belief systems. Moreover, the reversing condition sheds light on designing effective competing strategies in many real scenarios.

Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes. Some typical models are to learn the proper embedding between the image feature space and the semantic space, whilst it is important to learn discriminative features and comprise the coarse-to-fine image feature and semantic information. In this paper, we propose a discriminative embedding autoencoder with a regressor feedback model for ZSL. The encoder learns a mapping from the image feature space to the discriminative embedding space, which regulates both inter-class and intra-class distances between the learned features by a margin, making the learned features be discriminative for object recognition. The regressor feedback learns to map the reconstructed samples back to the the discriminative embedding and the semantic embedding, assisting the decoder to improve the quality of the samples and provide a generalization to the unseen classes. The proposed model is validated extensively on four benchmark datasets: SUN, CUB, AWA1, AWA2, the experiment results show that our proposed model outperforms the state-of-the-art models, and especially in the generalized zero-shot learning (GZSL), significant improvements are achieved.

In this paper, solution space organization of minimum vertex-cover problem is deeply investigated using the K\"{o}nig-Eg\'{e}rvary (KE) graph and theorem, in which a hierarchical decomposition mechanism named KE-layer structure of general graphs is proposed to reveal the complexity of vertex-cover. An algorithm to verify the KE graph is given by the solution space expression of vertex-cover, and the relation between multi-layer KE graphs and maximal matching is illustrated and proved. Furthermore, a framework to calculate the KE-layer number and approximate the minimal vertex-cover is provided, with different strategies of switching nodes and counting energy. The phase transition phenomenon between different KE-layers are studied with the transition points located, and searching of vertex-cover got by this strategy presents comparable advantage against several other methods. The graphical representation and hierarchical decomposition provide a new perspective to illustrate the intrinsic complexity for large-scale graphs/systems recognition., Comment: 20 pages, 6 figures

Models of contagion dynamics, originally developed for infectious diseases, have proven relevant to the study of information, news, and political opinions in online social systems. Modelling diffusion processes and predicting viral information cascades are important problems in network science. Yet, many studies of information cascades neglect the variation in infectivity across different pieces of information. Here, we employ early-time observations of online cascades to estimate the infectivity of distinct pieces of information. Using simulations and data from real-world Twitter retweets, we demonstrate that these estimated infectivities can be used to improve predictions about the virality of an information cascade. Developing our simulations to mimic the real-world data, we consider the effect of the limited effective time for transmission of a cascade and demonstrate that a simple model for slow but non-negligible decay of the infectivity captures the essential properties of retweet distributions. These results demonstrate the interplay between the intrinsic infectivity of a tweet and the complex network environment within which it diffuses, strongly influencing the likelihood of becoming a viral cascade., 16 pages, 10 figures

A new round of scientific and technological change and industrial transformation is emerging. Based on deep learning and big data, a third achievement of artificial intelligence is represented by AlphaGo and other typical application scenarios. When dealing with complex dynamic objects, the traditional artificial intelligence based on statistical linear dynamic modeling experiences bottlenecks related to interpretability, generalization, and reproducibility. It is urgent that a new generation of artificial intelligence theory be established that is based on complexity and multi-scale analysis, which we refer to as refined intelligence. To deal with the non-linearity of complex systems, we constructed a systems learning theory of embedded domain knowledge and a mathematical physical mechanism that is accurate and intelligent at three performance levels: complex data perception, complex system refined construction, and complex intelligent behavior analysis. Specifically, we built a scientific data system with interpretability, including embedded spatiotemporal characteristics and mathematical laws, through complex data perception. We also built a multi-level, multi-scale, and interpretable artificial intelligence dynamic learning model that can deal with the nonlinear relationship of complex logic based on complex system-defined construction. We developed a new theory and method for interpreting and controlling the evolution of artificial-intelligence-oriented behavior and global dynamic analysis based on complex intelligent-behavior analysis. We applied the above refined intelligence theory to the proposed method for crowd intelligence with crowd entropy, and found that it can measure complexity and provide effective guidance regarding system stimulation and aggregation behavior.

Low-energy ion implantation as a novel mutagen has been increasingly applied in the microbial mutagenesis for its higher mutation frequency and wider mutation spectra. In this work, N+ ion beam implantation was used to enhance Escherichia sp. in vitamin K2 yield. Optimization of process parameters under submerged fermentation was carried out to improve the vitamin K2 yield of mutant FM5-632. The results indicate that an excellent mutant FM5-632 with a yield of 123.2±1.6 μg/L, that is four times that of the original strain, was achieved by eight successive implantations under the conditions of 15 keV and 60×2.6×1013 ions/cm2. A further optimization increased the yield of the mutant by 39.7%, i.e. 172.1±1.2 μg/L which occurred in the mutant cultivated in the optimal fermentation culture medium composed of (per liter): 15.31 g glycerol, 10 g peptone, 2.89 g yeast extract, 5 g K2HPO4, 1 g NaCl, 0.5 g MgSO47H2O and 0.04 g cedar wood oil, incubated at 33 °C, pH 7.0 and 180 rpm for 120 h.

Rhegmatogenous retinal detachment (RRD) is one blinding disease, and has pathological features correlated with migration of retinal pigment epithelium (RPE) cells to viscous body. Matrix metalloproteinase-8 (MMP-8) participates in eye diseases including xerophthalmia and retinal disease. Its role in RRD, however, has not been illustrated with the functional mechanism. RPE cells from RRD model mice and normal mice were separated and cultured. MMP-8 expression plasmid was transfected into RPE cell in model group. Real time PCR and Western blot were employed to test expression level of MMP-8, whilst MTT method was used to test proliferation activity of RPE cells. Caspase 3 activity was quantified by test kit. Transwell migration assay was adopted to measure invasion ability of RPE cells. ELISA method was used to test expression level of inflammatory factors interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α). MMP-8 expression level was significantly decreased in RPE cells of RRD group, which also had enhanced cell proliferation and migration, accompanied with higher IL-1β and TNF-α levels (P

When analyzing the statistical and topological characteristics of complex networks, an effective and convenient way is to compute the centralities for recognizing influential and significant nodes or structures, yet most of them are restricted to local environment or some specific configurations. In this paper we propose a new centrality for nodes based on the von Neumann entropy, which allows us to investigate the importance of nodes in the view of spectrum eigenvalues distribution. By presenting the performances of this centrality with network examples in reality, it is shown that the von Neumann entropy node centrality is an excellent index for selecting crucial nodes as well as classical ones. Then to lower down the computational complexity, an approximation calculation to this centrality is given which only depends on its first and second neighbors. Furthermore, in the optimal spreader problem and reducing average clustering coefficients, this entropy centrality presents excellent efficiency and unveil topological structure features of networks accurately. The entropy centrality could reduce the scales of giant connected components fastly in Erdos-Renyi and scale-free networks, and break down the cluster structures efficiently in random geometric graphs. This new methodology reveals the node importance in the perspective of spectrum, which provides a new insight into networks research and performs great potentials to discover essential structural features in networks., Comment: 11 pages, 7 figures, 3 tables

Low temperature air plasma was used as the mutation tool for penicillin-producing strain Penicillium chrysogenum. The discharge conditions were RF power of 360 W, temperature of 40°C in a sealed chamber, and pressure of 10 Pa to 30 Pa. The result showed that the kinetics of the survival rate followed a typical saddle-shaped curve. Based on a statistic analysis, at the treating duration of 10 min, the positive mutation rate was as high as 37.5% while the negative mutation rate was low. The colonial morphology changed obviously when the plasma treating duration reached or exceeded 45 min. After both primary and secondary screening, a mutant designated as aPc051310 with high productivity of penicillin was obtained, and a strong mutagenic effect on P. chrysogenum was observed in the process. It was proved that after five generations, the mutant aPc051310 still exhibits a high productivity. All the results prove that the plasma mutation method could be developed as a convenient and effective tool to breed high-yield strains in the fermentation industry, while expanding the plasm application at the same time.

Co-fermentation of lignocellulose-based carbohydrates is a potential approach to improve the economics of microbial lipid production. To obtain an industrial strain that metabolizes glucose and xylose with a higher microbial lipid production, the strain Mortierella alpina was mutated by low-energy ion beam implantation. After nitrogen ion beam implantation, a mutant strain, M. alpina I502-8 (MAI502-8), was obtained. MAI502-8 was an efficient producer of microbial lipid from glucose and xylose (5:3 wt/wt). Moreover, the co-fermentation conditions were also optimized. Under optimal conditions, the maximum yield of biomass and microbial lipid reached 27.4 and 10.05 g L ―1 , respectively, which were 2.38-fold and 1.84-fold those in the initial media. Of the mixed sugar, 84.33 % was used up during co-fermentation.

In order to increase the production efficiency of coenzyme Q10, the original strain Agrobacterium tumefaciens ATCC 4452 was mutated by means of Nitrogen ions implantation. A mutant strain, ATX 12, with high contents of coenzyme Q10 was selected. Subsequently, the conditions such as carbohydrate concentration, nitrogen source concentration, inoculum's size, seed age, aeration and temperature which might affect the production of CoQ10 were investigated in detail. Under optimal conditions, the maximum concentration of the intracellular CoQ10 reached 200.3 mg/L after 80 h fed-batch fermentation, about 245% increasing in CoQ10 production after ion implantation, compared to the original strain.

In order to get an industrial strain which can yield a high concentration of lactic acid for ISPR (in situ product removal), the original strain Rhizopus oryzae RE3303 was mutated by low-energy ion beam implantation. A mutant RK02 was screened, and the factors such as the substrate concentration, nitrogen source concentration, inoculum size, seed age, aeration and temperature that affect the production of lactic acid were studied in detail. Under optimal conditions, the maximum concentration of L(+)-lactic acid reached 34.85 g/L after 30 h shake-flask cultivation without adding any neutralisation (5% Glucose added), which was a 146% increase in lactic acid production after ion implantation compared with the original strain. It was also shown that RK02 can be used in ISPR to reduce the number of times of separation.

Immobilized Rhizopus oryzae culturing may be a solution to the inhibited production of L(+)-lactic acid in submerged fermentation, which is caused by aggregated mycelia floc. In the present study, a R. oryzae mutant (RL6041) with a 90% conversion rate of glucose into L-lactic acid was obtained by N+ implantation under the optimized conditions of a beam energy of 15 keV and a dose of 2.6 × 1015 ions/cm2. Using polyurethane foam as the immobilization matrix, the optimal L-lactic acid production conditions were determined as 4 mm polyurethane foam, 150 r/min, 50 g/L ~ 80 g/L of initial glucose, 38°C and pH 6.0. 15-cycle repeated productions of L-lactic acid by immobilized RL6041 were performed under the optimized culturing conditions and over 80% of the glucose was converted into L-lactic acid in 30 hours on average. The results show that immobilized RL6041 is a promising candidate for continuous L-lactic acid production.

The wild type strain Rhizopus oryzae PW352 was mutated by means of nitrogen ion implantation (15 keV, 7.8 × 1014 ~ 2.08 × 1015 ions/cm2) to find an industrial strain with a higher L(+)-lactic acid yield, and two mutants RE3303 and RF9052 were isolated. In order to discuss the mechanism primarily, Lactate Dehydrogenase of Rhizopus oryzae was studied. While the two mutants produced L(+)-lactic acid by 75% more than the wild strain did, their specific activity of Lactate Dehydrogenase was found to be higher than that in the wild strain. The optimum temperature of Lactate Dehydrogenase in Rhizopus oryzae RF9052 was higher. Compared to the wild strain, the Michaelis constant (Km) value of Lactate Dehydrogenase in the mutants was changed. All these changes show that L(+)-lactic acid production has a correlation with the specific activity of Lactate Dehydrogenase. The low-energy ions, implanted into the strain, may improve the specific activity of Lactate Dehydrogenase by influencing its gene structure and protein structure.

An extracellular β-glucosidase from Aspergillus niger Au0847 was purified to homogeneity by precipitation with ammonium sulfate, anion exchange, and gel filtration. The purified protein was composed of two subunits with molecular masses of 110 and 120 kDa. Au0847 β-glucosidase exhibited relatively high thermostability and pH stability, and its highest activity was obtained at 65°C and pH 4.6, respectively. As a potential metalloprotein, its enzymatic activity was potently stimulated by manganese ion and DTT. The β-glucosidase displayed avid affinity and high catalytic efficiency for geniposide. Au0847 β-glucosidase has potential value as an industrial enzyme for the hydrolysis of geniposide to genipin.

In this paper, we want to show the abundance of chaotic systems with absolutely continuous probability measures in the generic regular family with perturbable points. More precisely, we prove that iffa:I → I, a ∈P is a regular family satisfying some conditions described in the next section, then there exists a Borel set Ω ⊂P of positive Lebesgue measure such that for everya ∈ Ω,fa admits an absolutely continuous invariant probability measure w.r.t. the Lebesgue measure. The idea of proof in this paper, as compared with that shown in [1] and [7], follows a similar line.

To solve the combinatorial optimization problems especially the minimal Vertex-cover problem with high efficiency, is a significant task in theoretical computer science and many other subjects. Aiming at detecting the solution space of Vertex-cover, a new structure named interaction between nodes is defined and discovered for random graph, which results in the emergence of the frustration and long-range correlation phenomenon. Based on the backbones and interactions with a node adding process, we propose an Interaction and Backbone Evolution Algorithm to achieve the reduced solution graph, which has a direct correspondence to the solution space of Vertex-cover. By this algorithm, the whole solution space can be obtained strictly when there is no leaf-removal core on the graph and the odd cycles of unfrozen nodes bring great obstacles to its efficiency. Besides, this algorithm possesses favorable exactness and has good performance on random instances even with high average degrees. The interaction with the algorithm provides a new viewpoint to solve Vertex-cover, which will have a wide range of applications to different types of graphs, better usage of which can lower the computational complexity for solving Vertex-cover.

Both aerodynamic and aeroacoustic performance of collateral axial flow fan system inside outdoor unit of air-conditioner is investigated by experiment and CFD simulation. Noise spectrum of outdoor unit is composed of broadband and discrete frequency noise. The main contribution of noise is broadband one. The discrete frequency noise is located at blade passing frequency and its harmonics of the upside and downside impeller respectively. Little interaction is found between downside and upside impeller through results of acoustic and flow filed. Vortex sound theory shows that broadband noise of outdoor unit is mainly caused by vortex shedding at trailing edge of impeller and discrete noise is caused by tip vortex. Based on acoustic analogy and CFD simulation, broadband and discrete frequency noise is well predicted with little error to experimental one.

In this paper we discuss the time dependence of solutions of the equation (2.3). From it we will get the global qualitative analysis of the equation.

This paper analyzes the scaling window of a random CSP model (i.e. model RB) for which we can identify the threshold points exactly, denoted by $r_{cr}$ or $p_{cr}$. For this model, we establish the scaling window $W(n,\delta)=(r_{-}(n,\delta), r_{+}(n,\delta))$ such that the probability of a random instance being satisfiable is greater than $1-\delta$ for $rr_{+}(n,\delta)$. Specifically, we obtain the following result $$W(n,\delta)=(r_{cr}-\Theta(\frac{1}{n^{1-\epsilon}\ln n}), \ r_{cr}+\Theta(\frac{1}{n\ln n})),$$ where $0\leq\epsilon

In this paper we analyze the qualitative behaviour of the equation $$\varepsilon \ddot X + q(X) \dot X + \varepsilon X = b p (t)$$ , wheree is a small parameter. We divide the interval of parameterb into four sets of subintervals.A, B, C andD. Forb∈A, B orD, we discuss the different structures of the attractors of the equation and their stabilities. When chaotic phenomena appear, we also estimate the entropy. Forb∈C, the set of bifurcation intervals, we analyze the bifurcating type and get a series of consequences from the results of Newhouse and Palis.