Identification of the Diterpenoids Produced by Endophytic Fungus of Torreya fargesii and Its Inhibitory Effect on Hela Cells

[Objectives] The purpose of this study is to dissociate endophytic fungus producing diterpenoids from Torreya fargesii tissue and examines its inhibiting effect on tumor cells. [Methods]Plant endophytes were isolated and purified to study their resistance to Gram-positive( G+) and Gram-negative bacteria( G-). High performance liquid chromatography( HPLC) was used for analysis of the retention time,relative peak area and percentage content of its metabolite. By liquid chromatography-mass spectrometry( HPLC-MS),the material characteristic of the ion pair information of the metabolites was measured. The bacterial strain was also classified. [Results] The results showed that the secondary metabolites produced by the strain BP6 T3 possessed double resistance to G+and G-bacteria. The strain was identified as Penicillium sp by preliminary classification. Through HPLC analysis,the retention time of fermentation extracts was 12. 8 min with almost the same as the standard of taxol. According to the chromatograph,the relative peak area was 12 887. 11,the average relative percentage was about 15. 8%,and the content of taxol analogs in fermentation broth reached 16. 59 mg/L. The material characteristic of the formation of ion fragments of taxane analogues in metabolic extracts was identical to that of the taxol standard determined by HPLC-MS. It can be initially determined that strain BP6 T3 can produce taxane compounds. Taxol substance produced by this strain had obvious inhibitory effect on Hela cells with the concentration increasing. Different precursors had a significant effect on the production of paclitaxel metabolites in this strain. L-phenylalanine was used as the precursor and the yield increased most,with an increase rate of 425. 7%. [Conclusions] The strain is expected to be used for mass production in antitumor drug taxol.

Multiple Impact Factor Based Accuracy Analysis for Power Quality Disturbance Detection

Nowadays,power quality problems are affecting people’s daily life and production activities.With an aim to improve disturbance detection accuracy,a novel analysis approach,based on multiple impact factors,is proposed in this paper.First,a multiple impact factors analysis is implemented in which two perspectives,i.e.,the wavelet analysis and disturbance features are simultaneously considered.Five key factors,including wavelet function,wavelet decomposition level,redundant algorithm,event type and disturbance intensity,and start and end moment of disturbance,have been considered.Next,an impact factor based accuracy analysis algorithm is proposed,through which each factor’s potential impact on disturbance location accuracy is investigated.Three transforms,i.e.,the classic wavelet,lifting wavelet and redundant lifting wavelet are employed,and their superiority on disturbance location accuracy is investigated.Finally,simulations are conducted for verification.Through the proposed method,the wavelet based parameters can be validly selected in order to accurately detect power quality disturbance.

An Integrated Cyber-Physical Simulation Environment for Smart Grid Applications

The concept of Cyber-Physical Systems （CPSs）, which combine computation, networking, and physical processes, is considered to be beneficial to smart grid applications. This study presents an integrated simulation environment to provide a unified platform for the investigation of smart grid applications involving power grid monitoring, communication, and control. In contrast to the existing approaches, this environment allows the network simulator to operate independently, importing its results to the power system simulation. This resolves conflicts between discrete event simulation and continuous simulation. In addition, several data compensation methods are proposed and investigated under different network delay conditions. A case study of wide-area monitoring and control is provided, and the efficiency of the proposed simulation framework has been evaluated based on the experimental results.

Cloud Computing-Based Forensic Analysis for Collaborative Network Security Management System

Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service （DDoS） attacks on victim hosts. New emerging botnet attacks degrade the status of Internet security further. To address these problems, a practical collaborative network security management system is proposed with an effective collaborative Unified Threat Management （UTM） and traffic probers. A distributed security overlay network with a centralized security center leverages a peer-to-peer communication protocol used in the UTMs collaborative module and connects them virtually to exchange network events and security rules. Security functions for the UTM are retrofitted to share security rules. In this paper, we propose a design and implementation of a cloud-based security center for network security forensic analysis. We propose using cloud storage to keep collected traffic data and then processing it with cloud computing platforms to find the malicious attacks. As a practical example, phishing attack forensic analysis is presented and the required computing and storage resources are evaluated based on real trace data. The cloud- based security center can instruct each collaborative UTM and prober to collect events and raw traffic, send them back for deep analysis, and generate new security rules. These new security rules are enforced by collaborative UTM and the feedback events of such rules are returned to the security center. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly and effectively.

Mobile Internet Big Data Platform in China Unicom

China Unicorn, the largest WCDMA 3G operator in China, meets the requirements of the historical Mobile Internet Explosion, or the surging of Mobile Internet Traffic from mobile terminals. According to the internal statistics of China Unicom, mobile user traffic has increased rapidly with a Compound Annual Growth Rate （CAGR） of 135%. Currently China Unicorn monthly stores more than 2 trillion records, data volume is over 525 TB, and the highest data volume has reached a peak of 5 PB. Since October 2009, China Unicom has been developing a home-brewed big data storage and analysis platform based on the open source Hadoop Distributed File System （HDFS） as it has a long-term strategy to make full use of this Big Data. All Mobile Internet Traffic is well served using this big data platform. Currently, the writing speed has reached 1 390 000 records per second, and the record retrieval time in the table that contains trillions of records is less than 100 ms. To take advantage of this opportunity to be a Big Data Operator, China Unicom has developed new functions and has multiple innovations to solve space and time constraint challenges presented in data processing. In this paper, we will introduce our big data platform in detail. Based on this big data platform, China Unicom is building an industry ecosystem based on Mobile Internet Big Data, and considers that a telecom operator centric ecosystem can be formed that is critical to reach prosperity in the modern communications business.

Collaborative Network Security in Multi-Tenant Data Center for Cloud Computing

A data center is an infrastructure that supports Internet service. Cloud comput the face of the Internet service infrastructure, enabling even small organizations to quickly ng is rapidly changing build Web and mobile applications for millions of users by taking advantage of the scale and flexibility of shared physical infrastructures provided by cloud computing. In this scenario, multiple tenants save their data and applications in shared data centers, blurring the network boundaries between each tenant in the cloud. In addition, different tenants have different security requirements, while different security policies are necessary for different tenants. Network virtualization is used to meet a diverse set of tenant-specific requirements with the underlying physical network enabling multi-tenant datacenters to automatically address a large and diverse set of tenants requirements. In this paper, we propose the system implementation of vCNSMS, a collaborative network security prototype system used n a multi-tenant data center. We demonstrate vCNSMS with a centralized collaborative scheme and deep packet nspection with an open source UTM system. A security level based protection policy is proposed for simplifying the security rule management for vCNSMS. Different security levels have different packet inspection schemes and are enforced with different security plugins. A smart packet verdict scheme is also integrated into vCNSMS for ntelligence flow processing to protect from possible network attacks inside a data center network

QoS-Aware Virtual Machine Scheduling for Video Streaming Services in Multi-Cloud

Video streaming services are trending to be deployed on cloud. Cloud computing offers better stability and lower price than traditional IT facilities. Huge storage capacity is essential for video streaming service. More and more cloud providers appear so there are increasing cloud platforms to choose. A better choice is to use more than one data center, which is called multi-cloud. In this paper a closed-loop approach is proposed for optimizing Quality of Service （QoS） and cost. Modules of monitoring and controlling data centers are required as well as the application feedback such as video streaming services. An algorithm is proposed to help choose cloud providers and data centers in a multi-cloud environment as a video service manager. Performance with different video service workloads are evaluated. Compared with using only one cloud provider, dynamically deploying services in multi-cloud is better in aspects of both cost and QoS. If cloud service costs are different among data centers, the algorithm will help make choices to lower the cost and keep a high QoS.

Electricity Services Based Dependability Model of Power Grid Communication Networking

The technology of Ultra-High Voltage （UHV） transmission requires higher dependability for electric power grid. Power Grid Communication Networking （PGCN）, the fundamental information infrastructure, severs data transmission including control signal, protection signal, and common data services. Dependability is the necessary requirement to ensure services timely and accurately. Dependability analysis aims to predicate operation status and provide suitable strategies getting rid of the potential dangers. Due to the dependability of PGCN may be affected by external environment, devices quality, implementation strategies, and so on, the scale explosion and the structure complexity make the PGCN＇s dependability much challenging. In this paper, with the observation of interdependency between power grid and PGCN, we propose an electricity services based dependability analysis model of PGCN. The model includes methods of analyzing its dependability and procedures of designing the dependable strategies. We respectively discuss the deterministic analysis method based on matrix analysis and stochastic analysis model based on stochastic Petri nets.

Performance Modeling for Data Monitoring Services in Smart Grid: A Network Calculus Based Approach

This paper focuses on solving the modeling issues of monitoring system service performance based on the network calculus theory.First,we formulate the service model of the smart grid monitoring system.Then,we derive the flow arrival curve based on the incremental process related functions.Next,we develop flow arrival curves for the case of the incremental process being a fractional Gaussian process,and then we obtain the generalized Cauchy process.Three technical theorems related to network calculus are presented as our main results.Mathematically,the variance of arrival flow for the continuous time case is derived.Assuming that the incremental process of network flow is a Gaussian stationary process,and given the auto-correlation function of the incremental process with violation probability,the formula of the arrival curve is derived.In addition,the overall flow variance under the discrete time case is explicitly derived.The theoretical results are evaluated in smart grid applications.Simulations indicate that the generalized Cauchy process outperforms the fractional Gaussian process for our considered problem.

Energy Sharing and Frequency Regulation in Energy Network via Mixed H_(2)/H_(∞) Control with Markovian Jump

In this paper,the problem of mixed optimization for energy sharing and frequency regulation in a typical energy network scenario where energy routers(ERs)interconnected AC microgrids(MGs)is investigated.Continuous-time Markov chains are introduced to describe the switching paths in the power dynamics of MGs.Such that the modelling of considered energy network system could be closer to the real-world engineering practice.Advanced parameter estimation techniques are integrated into the proposed method to achieve better modelling accuracy and controlling performance.Based on the parameters of MG power dynamics,the mixed H_(2)/H_(∞) controllers are obtained via stochastic control theory.The feasibility and efficacy of the proposed approach are evaluated in numerical examples.

Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors

In this paper, we study an application of deep learning to the advanced laser interferometer gravitational wave observatory(LIGO)and advanced Virgo coincident detection of gravitational waves(GWs) from compact binary star mergers. This deep learning method is an extension of the Deep Filtering method used by George and Huerta(2017) for multi-inputs of network detectors.Simulated coincident time series data sets in advanced LIGO and advanced Virgo detectors are analyzed for estimating source luminosity distance and sky location. As a classifier, our deep neural network(DNN) can effectively recognize the presence of GW signals when the optimal signal-to-noise ratio(SNR) of network detectors ≥ 9. As a predictor, it can also effectively estimate the corresponding source space parameters, including the luminosity distance D, right ascension α, and declination δ of the compact binary star mergers. When the SNR of the network detectors is greater than 8, their relative errors are all less than 23%.Our results demonstrate that Deep Filtering can process coincident GW time series inputs and perform effective classification and multiple space parameter estimation. Furthermore, we compare the results obtained from one, two, and three network detectors;these results reveal that a larger number of network detectors results in a better source location.

A Survey of Bitmap Index Compression Algorithms for Big Data

With the growing popularity of Internet applications and the widespread use of mobile Internet, Internet traffic has maintained rapid growth over the past two decades. Internet Traffic Archival Systems（ITAS） for packets or flow records have become more and more widely used in network monitoring, network troubleshooting, and user behavior and experience analysis. Among the three key technologies in ITAS, we focus on bitmap index compression algorithm and give a detailed survey in this paper. The current state-of-the-art bitmap index encoding schemes include： BBC, WAH, PLWAH, EWAH, PWAH, CONCISE, COMPAX, VLC, DF-WAH, and VAL-WAH. Based on differences in segmentation, chunking, merge compress, and Near Identical（NI） features, we provide a thorough categorization of the state-of-the-art bitmap index compression algorithms. We also propose some new bitmap index encoding algorithms, such as SECOMPAX, ICX, MASC, and PLWAH＋, and present the state diagrams for their encoding algorithms. We then evaluate their CPU and GPU implementations with a real Internet trace from CAIDA. Finally, we summarize and discuss the future direction of bitmap index compression algorithms. Beyond the application in network security and network forensic, bitmap index compression with faster bitwise-logical operations and reduced search space is widely used in analysis in genome data, geographical information system, graph databases, image retrieval, Internet of things, etc. It is expected that bitmap index compression will thrive and be prosperous again in Big Data era since 1980s.

Improved Approximate Minimum Degree Ordering Method and Its Application for Electrical Power Network Analysis and Computation

Electrical power network analysis and computation play an important role in the planning and operation of the power grid,and they are modeled mathematically as differential equations and network algebraic equations.The direct method based on Gaussian elimination theory can obtain analytical results.Two factors affect computing efficiency:the number of nonzero element fillings and the length of elimination tree.This article constructs mapping correspondence between eliminated tree nodes and quotient graph nodes through graph and quotient graph theories.The Approximate Minimum Degree(AMD)of quotient graph nodes and the length of the elimination tree nodes are composed to build an Approximate Minimum Degree and Minimum Length(AMDML)model.The quotient graph node with the minimum degree,which is also the minimum length of elimination tree node,is selected as the next ordering vector.Compared with AMD ordering method and other common methods,the proposed method further reduces the length of elimination tree without increasing the number of nonzero fillings;the length was decreased by about 10%compared with the AMD method.A testbed for experiment was built.The efficiency of the proposed method was evaluated based on different sizes of coefficient matrices of power flow cases.

TIFAflow: Enhancing Traffic Archiving System with Flow Granularity for Forensic Analysis in Network Security

The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system （such as Hadoop, ZFS, etc.） and open cloud computing platform （such as OpenStack, CloudStack, and Eucalyptus, etc.）, it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well- known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine＋FAstbit （TIFA） with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.

Chimeric cerebral organoids reveal the essentials of neuronal and astrocytic APOE4 for Alzheimer's tau pathology

The apolipoprotein E4(APOE4)genotype is one of the strongest genetic risk factors for Alzheimer’s disease(AD),and is generally believed to cause widespread pathological alterations in various types of brain cells.Here,we developed a novel engineering method of creating the chimeric human cerebral organoids(chCOs)to assess the differential roles of APOE4 in neurons and astrocytes.First,the astrogenic factors NFIB and SOX9 were introduced into induced pluripotent stem cells(iPSCs)to accelerate the induction of astrocytes.Then the above induced iPSCs were mixed and cocultured with noninfected iPSCs under the standard culturing condition of cerebral organoids.As anticipated,the functional astrocytes were detected as early as 45 days,and it helped more neurons matured in chCOs in comparation of the control human cerebral organoids(hCOs).More interestingly,this method enabled us to generate chCOs containing neurons and astrocytes with different genotypes,namely APOE3 or APOE4.Then,it was found in chCOs that astrocytic APOE4 already significantly promoted lipid droplet formation and cholesterol accumulation in neurons while both astrocytic and neuronal APOE4 contributed to the maximum effect.Most notably,we observed that the cooccurrence of astrocytic and neuronal APOE4 were required to elevate neuronal phosphorylated tau levels in chCOs while Aβlevels were increased in chCOs with neuronal APOE4.Altogether,our results not only revealed the essence of both neuronal and astrocytic APOE4 for tau pathology,but also suggested chCOs as a valuable pathological model for AD research and drug discovery.

Distributed Energy Sharing in Energy Internet Through Distributed Averaging

This paper proposes a distributed averaging iteration algorithm for energy sharing in microgrids of Energy Internet based on common gossip algorithms. This algorithm is completely distributed and only requires communications between neighbors. Through this algorithm, the Energy Internet not only allocates the energy effectively based on the load condition of grids, but also reasonably schedules the energy transmitted between neighboring grids. This study applies theoretical analysis to discuss the condition in which this algorithm can finally reach supply-and-demand balance. Subsequently, the related simulation validates the performance of the algorithm under various conditions.

MobSafe:Cloud Computing Based Forensic Analysis for Massive Mobile Applications Using Data Mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win＋Intel alliance in PC, Android＋ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app＇s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.

COMBAT:A New Bitmap Index Coding Algorithm for Big Data

Bitmap indexing has been widely used in various applications due to its speed in bitwise operations. However, it can consume large amounts of memory. To solve this problem, various bitmap coding algorithms have been proposed. In this paper, we present COMbining Binary And Ternary encoding （COMBAT）, a new bitmap index coding algorithm. Typical algorithms derived from Word Aligned Hybrid （WAH） are COMPressed Adaptive indeX （COMPAX） and Compressed ＂n＂ Composable Integer Set （CONCISE）, which can combine either two or three continuous words after WAH encoding. COMBAT combines both mechanisms and results in more compact bitmap indexes. Moreover, querying time of COMBAT can be faster than that of COMPAX and CONCISE, since bitmap indexes are smaller and it would take less time to load them into memory. To prove the advantages of COMBAT, we extend a theoretical analysis model proposed by our group, which is composed of the analysis of various possible bitmap indexes. Some experimental results based on real data are also provided, which show COMBAT＇s storage and speed superiority. Our results demonstrate the advantages of COMBAT and codeword statistics are provided to solidify the proof.

Community structure and activity potentials of archaeal communities in hadal sediments of the Mariana and Mussau trenches

Hadal trenches are the least explored marine habitat on earth. Archaea has been shown to be the dominant group in trench sediments. However, the activity potentials and detailed diversity of these communities as well as their inter-trench variations are still not known. In this study, we combined datasets from two pairs of primers to investigate at high resolution the structure and activity potentials of the archaeal communities in vertically sectioned sediment cores taken from the deepest points of the Mariana (10,853 m) and Mussau (7011 m) trenches. The compositions of the potentially active communities revealed, via 16S ribosomal RNA gene (rDNA) and RNA (rRNA), significant differences between samples. Marine Group I (MGI), with nine identified subgroups, was the most dominant class in the active archaeal communities of the two trenches. Significantly different species composition and vertical variations were observed between the two trenches. Vertical transitions from aerobic MGI α to anaerobic MGI η and υ subgroups were observed in MST but not in MT sediments, which might be related to the faster microbial oxygen consumption in MST. These results provide a better understanding on archaeal activity and diversity in trench sediments.

On the Mathematical Nature of Wireless Broadcast Trees

Trees are arguably one of the most important data structures widely used in information theory and computing science. Different numbers of intermediate nodes in wireless broadcast trees may exert great impacts on the energy consumption of individual nodes, which are typically equipped with a limited power supply in a wireless sensor network; this limitation may eventually determine how long the given wireless sensor network can last. Thus, obtaining a deep understanding of the mathematical nature of wireless broadcast trees is of great importance. In this paper, we give new proof of Cayley＇s well-known theorem for counting labeled trees. A distinct feature of this proof is that we purely use combinatorial structures instead of constructing a bijection between two kinds of labeled trees, which is in contrast to all existing proofs. Another contribution of this work is the presentation of a new theorem on trees based on the number of intermediate nodes in the tree. To the best of our knowledge,this work is the first to present a tree enumeration theorem based on the number of intermediate nodes in the tree.