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Cissampelos is a significant genus comprising of approximately 21 species of the medicinal plants (Menispermaceae). The plants of this genus are used in traditional medicine for the treatment of various ailments such as asthma, arthritis, dysentery, hyperglycemia, cardiopathy, hypertension and other related problems. These plants are rich in bioactive dibenzylisoquinoline and aborphine as well as small amounts of other ingredients. In recent years, the chemical constituents and pharmacological activities of Cissampelos genus have been paid more and more attention due to their diversity. Herein, we compile the chemical constituents and biological activities on this genus, and summarize the 13 C-NMR data of the main bioactive ingredients. All information comes from scientific databases such as Google Scholar, PubMed, Sci-Finder, ScienceDirect, Web of Science and CNKI. It provides valuable data for the future research and development of Cissampelos genus.

In the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants to complete a training task. As a collaborative learning framework, federated learning is designed to preserve user data privacy, where participants jointly train a global model without uploading their private training data to a third party server. Nevertheless, federated learning is under the threat of poisoning attacks, where adversaries can upload malicious model updates to contaminate the global model. To detect and mitigate poisoning attacks in federated learning, we propose a poisoning defense mechanism, which uses generative adversarial networks to generate auditing data in the training procedure and removes adversaries by auditing their model accuracy. Experiments conducted on two well-known datasets, MNIST and Fashion-MNIST, suggest that federated learning is vulnerable to the poisoning attack, and the proposed defense method can detect and mitigate the poisoning attack.

With software-defined networking (SDN) becoming the leading technology for large-scale networks, it is definitely expected that SDN will suffer various types of distributed denial-of-service (DDoS) attacks because of its centralized control logic. However, almost all of existing works concentrate on the controller overloading DDoS attacks, while vulnerabilities exposed by data plane of SDN for DDoS attacks are largely ignored. In this paper, we firstly investigate a flow rule flooding DDoS attack. By thoroughly analyzing the flow table size and miss rate, we find that attackers are able to inflict significant performance degradation over the system with limited volume of attack resource. We then prove that it is possible for attackers to maximize the performance degradation and minimize the attack rate at the same time. Besides the flooding DDoS attack, we also study a novel DDoS attack targeting data plane of SDN. By utilizing the entry lifetime management mechanism of flow tables, this attack almost never exhibits an intensive controller access behavior. It flies under the radar by inflicting non-notable performance impact on the system, while it creates heavy long-term financial burden on the target application. Finally, we present a potential countermeasure for this stealthy DDoS attack. Through extensive experiments, we conclude that DDoS attacks targeting data plane are possible. Copyright © 2016 John Wiley & Sons, Ltd.

Analytic quantifiers of the symmetric quantum discord for two-qubit X type states and block-diagonal states and the symmetric measurement induced nonlocality for any two qubit states are established on the basis of the quantum skew information.

Spirostanol saponins are a class of steroidal saponins with many pharmacological activities. The structural complexity of the spirostanol saponins presents a daunting challenge in separating their 25 R/S diastereomers. Using two CHIRALPAK IC columns coupled in series, six 25 (R/S)-spirostanol saponin diastereomers from the Trigonella foenum-graecum L. seed were successfully separated using supercritical fluid chromatography (SFC) for the first time. In addition, three 25 (R/S)-spirostanol saponin diastereomers were isolated into their respective individual isomers. The structures of the isolated isomers were unambiguously confirmed by NMR analysis. The SFC method development strategy and its associated underlying principles presented in this paper are generally applicable. SFC is a viable addition to the natural product research toolbox, especially for stereoselective analysis and purification.

The protein tyrosine phosphatase N22 (PTPN22) gene C1858T polymorphism has been reported to be associated with susceptibility to type 1 diabetes (T1D) in relatively small sample sizes. This study aimed at investigating the pooled association by carrying out a meta-analysis on the published studies. The Medline, EBSCO, and BIOSIS databases were searched to identify eligible studies published in English before June 2012. The association was assessed by odds ratio (OR) with 95% confidence intervals (CI). The presence of heterogeneity and publication bias was explored by using meta-regression analysis and Begg's test, respectively. A total of 28 studies were involved in this meta-analysis. Across all populations, significant associations were found between the PTPN22 C1858T polymorphism and susceptibility to T1D under genotypic (TT vs. CC [OR = 3.656, 95% CI: 3.139-4.257], CT vs. CC [OR = 1.968, 95% CI: 1.683-2.300]), recessive (OR = 3.147, 95% CI: 2.704-3.663), and dominant models (OR = 1.957, 95% CI: 1.817-2.108). In ethnicity- and sex-stratified analyses, similar associations were found among Caucasians and within Caucasian male and female strata. The meta-analysis results suggest that the PTPN22 C1858T polymorphism was associated with susceptibility to T1D among the Caucasian population, and males who carried the -1858T allele were more susceptible to T1D than females.

An ultra-performance liquid chromatography coupled with a hybrid quadrupole time-of-flight tandem mass spectrometry method was established to rapidly identify and guide the isolation of target saponins from fenugreek seeds. Based on the online screening performance, totally forty-six furostanol saponins were detected and elucidated. Among them, twenty compounds were predicted to be new. To rapidly obtain new furostanol saponins from these seeds, a further phytochemical study was carried out under the guidance of the ultra-performance liquid chromatography coupled with a hybrid quadrupole time-of-flight tandem mass spectrometry. Finally, six new furostanol saponins, named as trigoneosides XIV (1), XV (2), XVI (3), XVIIa (4), XVIIb (5), and XIV (6), together with one known furostanol saponin, parvifloside (7), were rapidly obtained, and their definitive structures were determined by NMR and chemical evidence.

Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Internet-based delay-sensitive applications. It is an important but unsolved issue, however, to efficiently schedule tasks in network processors with multicore and multithread for improving the system throughput as much as possible. Profiling can gather runtime environment information and guide the compiler to optimize programs through scheduling tasks based on the runtime context. This paper proposes a profiling-based task scheduling approach, targeting on improving the throughput of multicore network processor Intel IXP systems in the balanced pipeline way. In this work, we investigate a profiling-based task scheduling framework, a task scheduling algorithm, and a set of performance models. Our task allocation scheme maps tasks onto the pipeline architecture and multiple threads of network processors in parallel, which incorporates the profiling context and global thread refinement. We evaluate our task scheduling algorithm by implementing representative network applications on the Intel IXP network processor. Experimental results demonstrate that our algorithm is able to schedule tasks in a balanced pipeline fashion and achieve the high throughput and data transmission rate. Copyright © 2012 John Wiley & Sons, Ltd.

A new furostanol saponin, sisalasaponin C (1), and a new spirostanol saponin, sisalasaponin D (2), were isolated from the fresh leaves of Agave sisalana, along with three other known steroidal saponins and two stilbenes. Their structures were identified as (3β,5α,6α,22α,25R)-3,26-bis[(β-D-glucopyrano- syl)oxy]-22-hydroxyfurostan-6-yl β-D-glucopyranoside (1), (3β,5α,25R)-12-oxospirostan-3-yl 6-deoxy-α-L-mannopyranosyl-(14)-β-D-glucopyranosyl-(13)-[β-D-xylopyranosyl-(13)-β-D-glucopyranosyl-(12)]-β-D-glucopyranosyl-(14)-β-D-galactopyranoside (2), (3β,5α,6α,22α,25R)-22-methoxyfurostane-3,6,26-triyl tris-β-D-glucopyranoside, cantalasaponin-1, polianthoside D, (E)- and (Z)-2,3,4′,5-tetrahydroxystilbene 2-O-β-D-glucopyranosides. The last three known compounds were isolated from the fresh leaves of Agavaceae for the first time. The structures of the new compounds were elucidated by detailed spectroscopic analysis, including 1D- and 2D-NMR experiments, and chemical techniques.

Two new spirostanol saponins, kingianoside I (1) and kingianoside K (2), corresponding to (3β,23S,25R)-23-hydroxy-12-oxospirost-5-en-3-yl 4-O-β-D-glucopyranosyl-β-D-galactopyranoside (1) and (3β,25R)-7-oxospirost-5-en-3-yl α-L-arabinofuranosyl-(14)-[6-deoxy-α-L-mannopyranosyl-(12)]-β-D-glucopyranoside (2), along with 13 known compounds, daucosterol, (25R)-kingianoside G, (25RS)-kingianoside A, pratioside D1, (25RS)-pratioside D1, (25S)-kingianoside C, kingianoside C, ginsenoside Rb1, saponins Tb and Pb, dioscin, gracillin, and saponin Pa, were isolated from the processed rhizomes of Polygonatum kingianum. The structures of the new compounds were elucidated by detailed spectroscopic analyses, including 1D- and 2D-NMR techniques, and chemical methods. Compound 2 contains a novel unusual spirostanol saponin aglycone. Ginsenoside Rb1 and saponin Tb were isolated for the first time from the genus Polygonatum. The 13 known compounds were detected for the first time in the processed Polygonatum kingianum.

The aim was to investigate the effects of echo time (TE) on diffusion quantification of brain white matter. Seven rhesus monkeys (all males; age, 4 – 6 years; weight, 5–7 kg) underwent diffusion tensor imaging (DTI) with a series of TEs in 1.5T and 3.0T MR scanners. The mean diffusivity (MD), fractional anisotropy (FA), primary (1), and transverse eigenvalues (23) were measured in a region of interest at the bilateral internal capsule. Pearson correlation showed that the FA and 1 increased and 23 decreased with TE both at 1.5T and 3.0T except for the MD. Repeated measurement analysis of variance (ANOVA) also showed significantly higher FA and lower MD and 23 at 3.0T than those at 1.5T (P < 0.01), but no statistical differences were found in 1 between these two field strengths (P 0.709). These findings implied that TE and field strength might influence diffusion quantification in brain white matter. Magn Reson Med 61:755–760, 2009. © 2009 Wiley-Liss, Inc.

Summary Scale-free networks are often used to model a wide range of real-world networks, such as social, technological, and biological networks. Understanding the structure of scale-free networks evolves into a big data problem for business, management, and protein function prediction. In the past decade, there has been a surge of interest in exploring the properties of scale-free networks. Two interesting properties have attracted much attention: the assortative mixing and community structure. However, these two properties have been studied separately in either theoretical models or real-world networks. In this paper, we show that the structural features of communities are highly related with the assortative mixing in scale-free networks. According to the value of assortativity coefficient, scale-free networks can be categorized into assortative, disassortative, and neutral networks, respectively. We systematically analyze the community structure in these three types of scale-free networks through six metrics: node embeddedness, link density, hub dominance, community compactness, the distribution of community sizes, and the presence of hierarchical communities. We find that the three types of scale-free networks exhibit significant differences in these six metrics of community structures. First, assortative networks present high embeddedness, meaning that many links lying within communities but few links lying between communities. This leads to the high link density of communities. Second, disassortative networks exhibit great hubs in communities, which results in the high compactness of communities that nodes can reach each other via short paths. Third, in neutral networks, a big portion of links act as community bridges, so they display sparse and less compact communities. In addition, we find that (dis)assortative networks show hierarchical community structure with power-law-distributed community sizes, while neutral networks present no hierarchy. Understanding the structure of communities from the angle of assortative mixing patterns of nodes can provide insights into the network structure and guide us in modeling information propagation in different categories of scale-free networks. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.