Harmful Microcystis blooms (HMBs) seriously threaten the ecology of environments and human health. Microcystins (MCs) produced by Microcystis are powerful mediators of HMB induction and maintenance. In this study, microcystinase A (MlrA), an enzyme with MC-degrading ability, was successfully obtained at over 90% purity for the first time through overexpression in Escherichia coli K12 TB1. The obtained MlrA exhibited high stability at high temperature and under alkaline conditions, while also exhibiting a long half-life. MlrA selectively inhibited MC-producing Microcystis cultures, but had no effect on MC-nonproducing Synechocystis cultures. The inhibition mechanism of MlrA against Microcystis was investigated by evaluating the morphological and physiological characteristics of cultures. MlrA effectively degraded extracellular MCs and decreased the synthesis of intracellular MCs by causing downregulation of genes involved in the microcystin biosynthesis pathway. Concomitantly, MlrA inhibited Microcystis photosynthesis by causing the downregulated expression of important photosynthesis pathway genes and interrupting electron transport chain activities and pigment synthesis. Thus, MlrA achieved the inhibition of Microcystis growth by reducing its photosynthetic capacity and intracellular MC contents, while also degrading extracellular MCs. On the basis of these results, we propose a new paradigm to achieve the simultaneous removal of MCs and HMBs using the single enzyme characterized here.
Industrial Information Technology infrastructures are often vulnerable to cyberattacks. To ensure security to the computer systems in an industrial environment, it is required to build effective intrusion detection systems to monitor the cyber-physical systems (e.g., computer networks) in the industry for malicious activities. This article aims to build such intrusion detection systems to protect the computer networks from cyberattacks. More specifically, we propose a novel unsupervised machine learning approach that combines the K-Means algorithm with the Isolation Forest for anomaly detection in industrial big data scenarios. Since our objective is to build the intrusion detection system for the big data scenario in the industrial domain, we utilize the Apache Spark framework to implement our proposed model that was trained in large network traffic data (about 123 million instances of network traffic) stored in Elasticsearch. Moreover, we evaluate our proposed model on the live streaming data and find that our proposed system can be used for real-time anomaly detection in the industrial setup. In addition, we address different challenges that we face while training our model on large datasets and explicitly describe how these issues were resolved. Based on our empirical evaluation in different use cases for anomaly detection in real-world network traffic data, we observe that our proposed system is effective to detect anomalies in big data scenarios. Finally, we evaluate our proposed model on several academic datasets to compare with other models and find that it provides comparable performance with other state-of-the-art approaches.
We show that the coupled complex systems can evolve into a new kind of self-organized critical state where each subsystem is not critical, however, they cooperate to be critical. This criticality is different from the classical BTW criticality where the single system itself evolves into a critical state. We also find that the outflows can be accumulated in the coupled systems. This will lead to the emergency of spatiotemporal intermittency in the critical state.PACS numbers: 89.75.-k,05.40.-a,02.50-r Since proposed in 1980s, the self-organized criticality (SOC) [1][2][3] has been one of the most popular theory in complex science and is widely used as a way of understanding emergent complex behavior in physical [4][5][6][7][8], biological [9][10][11] and even social systems [12][13][14][15]. According to this theory, the ubiquitous power law in nature can be interpreted as the hallmark of critical state, a stable state that systems evolve into by themselves. The SOC is usually illustrated by a simple cellular automaton, the so-called BTW sandpile model [1][2][3]. The dynamics of this model can be imaged as a transport phenomenon, where sand grains in a very high heap (with a local height gradient exceeding the threshold) is unstable and will tumble down the slope of this heap (against the local height gradient). We note that this dynamics is not enough to describe the real complex systems where a movement along the local gradient is also possible. For example, in our daily life you may have considered to migrate to a bigger city for a better job. Migration to a bigger city is a movement along the population gradient. Here, someone's final choice is determined by the equilibrium between the population gradient and other gradients, such as the job gradients, the education gradients and even shopping gradients. In other words, the dynamics of migration is coupled with other dynamics. In fact, similar coupling also exists in the natural systems. A well-known example is the cross effect in the transport arising in a mixture if both the concentrations and the temperature are non-uniform over the system. The cross effect can be simply illustrated in a isotropic binary mixture without viscosity when no external forces are supposed to be present and the pressure is uniform over the system [16]:(1)Symbols J q , J d , T , C and ρ are the heat flux, the diffusion flow, the temperature, the concentration of one of the components in the mixture and the density of mixture respectively. The diffusion coefficient D and the heat conductivity λ are positive and are related to the normal * liulei@mail.iap.ac.cn heat conduction and diffusion respectively. The symbol s T is called Soret coefficient which is related to the cross effect in the transport and can be negative or positive. The coefficients α ≥ 0 and β ≥ 0. From Eqs. (1) and (2), one can see that the direction of heat or diffusion flow is determined by the equilibrium between the local temperature and the local concentration gradient. Two transport processes are coupled t...
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