Trust transitivity in social networks

Non-centralized recommendation-based decision making is a central feature of several social and technological processes, such as market dynamics, peer-to-peer file-sharing and the web of trust of digital certification. We investigate the properties of trust propagation on networks, based on a simple metric of trust transitivity. We investigate analytically the percolation properties of trust transitivity in random networks with arbitrary degree distribution, and compare with numerical realizations. We find that the existence of a non-zero fraction of absolute trust (i.e. entirely confident trust) is a requirement for the viability of global trust propagation in large systems: The average pair-wise trust is marked by a discontinuous transition at a specific fraction of absolute trust, below which it vanishes. Furthermore, we perform an extensive analysis of the Pretty Good Privacy (PGP) web of trust, in view of the concepts introduced. We compare different scenarios of trust distribution: community- and authority-centered. We find that these scenarios lead to sharply different patterns of trust propagation, due to the segregation of authority hubs and densely-connected communities. While the authority-centered scenario is more efficient, and leads to higher average trust values, it favours weakly-connected "fringe" nodes, which are directly trusted by authorities. The community-centered scheme, on the other hand, favours nodes with intermediate degrees, in detriment of the authorities and its "fringe" peers.Comment: 11 pages, 9 figures (with minor corrections

Membrane Interactions and Uncoating of Aichi Virus, a Picornavirus That Lacks a VP4.

Kobuviruses are an unusual and poorly characterized genus within the picornavirus family and can cause gastrointestinal enteric disease in humans, livestock, and pets. The human kobuvirus Aichi virus (AiV) can cause severe gastroenteritis and deaths in children below the age of 5 years; however, this is a very rare occurrence. During the assembly of most picornaviruses (e.g., poliovirus, rhinovirus, and foot-and-mouth disease virus), the capsid precursor protein VP0 is cleaved into VP4 and VP2. However, kobuviruses retain an uncleaved VP0. From studies with other picornaviruses, it is known that VP4 performs the essential function of pore formation in membranes, which facilitates transfer of the viral genome across the endosomal membrane and into the cytoplasm for replication. Here, we employ genome exposure and membrane interaction assays to demonstrate that pH plays a critical role in AiV uncoating and membrane interactions. We demonstrate that incubation at low pH alters the exposure of hydrophobic residues within the capsid, enhances genome exposure, and enhances permeabilization of model membranes. Furthermore, using peptides we demonstrate that the N terminus of VP0 mediates membrane pore formation in model membranes, indicating that this plays an analogous function to VP4. IMPORTANCE To initiate infection, viruses must enter a host cell and deliver their genome into the appropriate location. The picornavirus family of small nonenveloped RNA viruses includes significant human and animal pathogens and is also a model to understand the process of cell entry. Most picornavirus capsids contain the internal protein VP4, generated from cleavage of a VP0 precursor. During entry, VP4 is released from the capsid. In enteroviruses this forms a membrane pore, which facilitates genome release into the cytoplasm. Due to high levels of sequence similarity, it is expected to play the same role for other picornaviruses. Some picornaviruses, such as Aichi virus, retain an intact VP0, and it is unknown how these viruses rearrange their capsids and induce membrane permeability in the absence of VP4. Here, we have used Aichi virus as a model VP0 virus to test for conservation of function between VP0 and VP4. This could enhance understanding of pore function and lead to development of novel therapeutic agents that block entry

Generation of Antibodies against Foot-and-Mouth-Disease Virus Capsid Protein VP4 Using Hepatitis B Core VLPs as a Scaffold

The picornavirus foot-and-mouth disease virus (FMDV) is the causative agent of the economically important disease of livestock, foot-and-mouth disease (FMD). VP4 is a highly conserved capsid protein, which is important during virus entry. Previous published work has shown that antibodies targeting the N-terminus of VP4 of the picornavirus human rhinovirus are broadly neutralising. In addition, previous studies showed that immunisation with the N-terminal 20 amino acids of enterovirus A71 VP4 displayed on the hepatitis B core (HBc) virus-like particles (VLP) can induce cross-genotype neutralisation. To investigate if a similar neutralising response against FMDV VP4 could be generated, HBc VLPs displaying the N-terminus of FMDV VP4 were designed. The N-terminal 15 amino acids of FMDV VP4 was inserted into the major immunodominant region. HBc VLPs were also decorated with peptides of the N-terminus of FMDV VP4 attached using a HBc-spike binding tag. Both types of VLPs were used to immunise mice and the resulting serum was investigated for VP4-specific antibodies. The VLP with VP4 inserted into the spike, induced VP4-specific antibodies, however the VLPs with peptides attached to the spikes did not. The VP4-specific antibodies could recognise native FMDV, but virus neutralisation was not demonstrated. This work shows that the HBc VLP presents a useful tool for the presentation of FMDV capsid epitopes

The in vivo properties of STX243: a potent angiogenesis inhibitor in breast cancer

The steroidal-based drug 2-ethyloestradiol-3,17-O,O-bis-sulphamate (STX243) has been developed as a potent antiangiogenic and antitumour compound. The objective of this study was to ascertain whether STX243 is more active in vivo than the clinically relevant drug 2-methoxyoestradiol (2-MeOE2) and the structurally similar compound 2-MeOE2-3,17-O,O-bis-sulphamate (STX140). The tumour growth inhibition efficacy, antiangiogenic potential and pharmacokinetics of STX243 were examined using four in vivo models. Both STX243 and STX140 were capable of retarding the growth of MDA-MB-231 xenograft tumours (72 and 63%, respectively), whereas no inhibition was observed for animals treated with 2-MeOE2. Further tumour inhibition studies showed that STX243 was also active against MCF-7 paclitaxel-resistant tumours. Using a Matrigel plug-based model, in vivo angiogenesis was restricted with STX243 and STX140 (50 and 72%, respectively, using a 10 mg kg−1 oral dose), thereby showing the antiangiogenic activity of both compounds. The pharmacokinetics of STX243 were examined at two different doses using adult female rats. The compound was orally bioavailable (31% after a single 10 mg kg−1 dose) and resistant to metabolism. These results show that STX243 is a potent in vivo drug and could be clinically effective at treating a number of oncological conditions

High modularity creates scaling laws

Scaling laws have been observed in many natural and engineered systems. Their existence can give useful information about the growth or decay of one quantitative feature in terms of another. For example, in the field of city analytics, it is has been fruitful to compare some urban attribute, such as energy usage or wealth creation, with population size. In this work, we use network science and dynamical systems perspectives to explain that the observed scaling laws, and power laws in particular, arise naturally when some feature of a complex system is measured in terms of the system size. Our analysis is based on two key assumptions that may be posed in graph theoretical terms. We assume (a) that the large interconnection network has a well-defined set of communities and (b) that the attribute under study satisfies a natural continuity-type property. We conclude that precise mechanistic laws are not required in order to explain power law effects in complex systems—very generic network-based rules can reproduce the behaviors observed in practice. We illustrate our results using Twitter interaction between accounts geolocated to the city of Bristol, UK

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases