Optimization of human papillomavirus type 16 (HPV-16) L1 expression in plants: Comparison of the suitability of different HPV-16 L1 gene variants and different cell-compartment localization

Virus-like particle-based vaccines for high-risk human papillomaviruses (HPVs) appear to have great promise; however, cell culture-derived vaccines will probably be very expensive. The optimization of expression of different codon-optimized versions of the HPV-16 L1 capsid protein gene in plants has been explored by means of transient expression from a novel suite of Agrobacterium tumefaciens binary expression vectors, which allow targeting of recombinant protein to the cytoplasm, endoplasmic reticulum (ER) or chloroplasts. A gene resynthesized to reflect human codon usage expresses better than the native gene, which expresses better than a plant-optimized gene. Moreover, chloroplast localization allows significantly higher levels of accumulation of L1 protein than does cytoplasmic localization, whilst ER retention was least successful. High levels of L1 (>17% total soluble protein) could be produced via transient expression: the protein assembled into higher-order structures visible by electron microscopy, and a concentrated extract was highly immunogenic in mice after subcutaneous injection and elicited high-titre neutralizing antibodies. Transgenic tobacco plants expressing a human codon-optimized gene linked to a chloroplast-targeting signal expressed L1 at levels up to 11% of the total soluble protein. These are the highest levels of HPV L1 expression reported for plants: these results, and the excellent immunogenicity of the product, significantly improve the prospects of making a conventional HPV vaccine by this means. © 2007 SGM.

A protocol for the rapid isolation of full geminivirus genomes from dried plant tissue

A high-throughput method of isolating and cloning geminivirus genomes from dried plant material, by combining an Extract-n-Amp™-based DNA isolation technique with rolling circle amplification (RCA) of viral DNA, is presented. Using this method an attempt was made to isolate and clone full geminivirus genomes/genome components from 102 plant samples, including dried leaves stored at room temperature for between 6 months and 10 years, with an average hands-on-time to RCA-ready DNA of 15 min per 20 samples. While storage of dried leaves for up to 6 months did not appreciably decrease cloning success rates relative to those achieved with fresh samples, efficiency of the method decreased with increasing storage time. However, it was still possible to clone virus genomes from 47% of 10-year-old samples. To illustrate the utility of this simple method for high-throughput geminivirus diversity studies, six Maize streak virus genomes, an Abutilon mosaic virus DNA-B component and the DNA-A component of a previously unidentified New Word begomovirus species were fully sequenced. Genomic clones of the 69 other viruses were verified as such by end sequencing. This method should be extremely useful for the study of any circular DNA plant viruses with genome component lengths smaller than the maximum size amplifiable by RCA. © 2008 Elsevier B.V. All rights reserved.

Transient expression of Human papillomavirus type 16 L1 protein in Nicotiana benthamiana using an infectious tobamovirus vector

A Tobacco mosaic virus (TMV)-derived vector was used to express a native Human papillomavirus type 16 (HPV-16) L1 gene in Nicotiana benthamiana by means of infectious in vitro RNA transcripts inoculated onto N. benthamiana plants. HPV-16 L1 protein expression was quantitated by enzyme-linked immunosorbent assays (ELISA) after concentration of the plant extract. We estimated that the L1 product yield was 20-37 μg/kg of fresh leaf material. The L1 protein in the concentrated extract was antigenically characterised using the neutralising and conformation-specific Mabs H16:V5 and H16:E70, which bound to the plant-produced protein. Particles observed by transmission electron microscopy were mainly capsomers but virus-like particles (VLPs) similar to those produced in other systems were also present. Immunisation of rabbits with the concentrated plant extract induced a weak immune response. This is the first report of the successful expression of an HPV L1 gene in plants using a plant virus vector. © 2006 Elsevier B.V. All rights reserved.

Plant-produced cottontail rabbit papillomavirus L1 protein protects against tumor challenge : A proof-of-concept study

The native cottontail rabbit papillomavirus (CRPV) L1 capsid protein gene was expressed transgenically via Agrobacterium tumefaciens transformation and transiently via a tobacco mosaic virus (TMV) vector in Nicotiana spp. L1 protein was detected in concentrated plant extracts at concentrations up to 1.0 mg/kg in transgenic plants and up to 0.4 mg/kg in TMV-infected plants. The protein did not detectably assemble into viruslike particles; however, immunoelectron microscopy showed presumptive pentamer aggregates, and extracted protein reacted with conformation-specific and neutralizing monoclonal antibodies. Rabbits were injected with concentrated protein extract with Freund's incomplete adjuvant. All sera reacted with baculovirus-produced CRPV L1; however, they did not detectably neutralize infectivity in an in vitro assay. Vaccinated rabbits were, however, protected against wart development on subsequent challenge with live virus. This is the first evidence that a plant-derived papillomavirus vaccine is protective in an animal model and is a proof of concept for human papillomavirus vaccines produced in plants. Copyright © 2006, American Society for Microbiology. All Rights Reserved.

Effect of a novel compound from Lycopodium obscurum L. on osteogenic activity of osteoblasts in vitro

We investigated the potential of an extract of Lycopodium obscurum L.; stigmastane-3-oxo-21-oic acid (SA), to enhance osteogensis of mouse osteoblastic MC3T3-E1 cells. SA at a concentration of 16 µM was found to have no significant effect upon the viability of the cells, thus concentrations of 8 µM and 16 µM of SA were used in all further experiments. Both concentrations of SA had an inhibitory affect upon alkaline phosphatase activity (ALP) after 8 days incubation, however, after 16 days activity was restored to control levels. However Alizarin red S staining showed increased levels of mineralization for both concentrations after 16 days culture. Real time PCR showed inhibition of genes Runx2 and Osterix genes responsible for the up-regulation of ALP. However early time point (8 days) up-regulation of bone matrix mineralization genes OPN and OCN, and late time point (16 days) up-regulation of both Jun-D and Fra-2 mRNA expression was significantly enhanced. These results suggest a potential me-chanism of SA in enhancing bone fracture healing is through the up-regulating bone matrix minera-lization.

Learning personalized tag ontology from user tagging information

The cross-sections of the Social Web and the Semantic Web has put folksonomy in the spot light for its potential in overcoming knowledge acquisition bottleneck and providing insight for "wisdom of the crowds". Folksonomy which comes as the results of collaborative tagging activities has provided insight into user's understanding about Web resources which might be useful for searching and organizing purposes. However, collaborative tagging vocabulary poses some challenges since tags are freely chosen by users and may exhibit synonymy and polysemy problem. In order to overcome these challenges and boost the potential of folksonomy as emergence semantics we propose to consolidate the diverse vocabulary into a consolidated entities and concepts. We propose to extract a tag ontology by ontology learning process to represent the semantics of a tagging community. This paper presents a novel approach to learn the ontology based on the widely used lexical database WordNet. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. We provide empirical evaluations by using the semantic information contained in the ontology in a tag recommendation experiment. The results show that by using the semantic relationships on the ontology the accuracy of the tag recommender has been improved.

Candidate radial-inflow turbines and high-density working fluids for geothermal power systems

Optimisation of Organic Rankine Cycles (ORCs) for binary-cycle geothermal applications could play a major role in the competitiveness of low to moderate temperature geothermal resources. Part of this optimisation process is matching cycles to a given resource such that power output can be maximised. Two major and largely interrelated components of the cycle are the working fluid and the turbine. Both components need careful consideration. Due to the temperature differences in geothermal resources a one-size-fits-all approach to surface power infrastructure is not appropriate. Furthermore, the traditional use of steam as a working fluid does not seem practical due to the low temperatures of many resources. A variety of organic fluids with low boiling points may be utilised as ORC working fluids in binary power cycle loops. Due to differences in thermodynamic properties, certain fluids are able to extract more heat from a given resource than others over certain temperature and pressure ranges. This enables the tailoring of power cycle infrastructure to best match the geothermal resource through careful selection of the working fluid and turbine design optimisation to yield the optimum overall cycle performance. This paper presents the rationale for the use of radial-inflow turbines for ORC applications and the preliminary design of several radial-inflow turbines based on a selection of promising ORC cycles using five different high-density working fluids: R134a, R143a, R236fa, R245fa and n-Pentane at sub- or trans-critical conditions. Numerous studies published compare a variety of working fluids for various ORC configurations. However, there is little information specifically pertaining to the design and implementation of ORCs using realistic radial turbine designs in terms of pressure ratios, inlet pressure, rotor size and rotational speed. Preliminary 1D analysis leads to the generation of turbine designs for the various cycles with similar efficiencies (77%) but large differences in dimensions (139289 mm rotor diameter). The highest performing cycle (R134a) was found to produce 33% more net power from a 150°C resource flowing at 10 kg/s than the lowest performing cycle (n-Pentane).

Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Monitoring smartphones for anomaly detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80 of the amount of monitored features.

Using a pharmacokinetic model to interpret biomonitoring data of PBDEs in the Australian population

From human biomonitoring data that are increasingly collected in the United States, Australia, and in other countries from large-scale field studies, we obtain snap-shots of concentration levels of various persistent organic pollutants (POPs) within a cross section of the population at different times. Not only can we observe the trends within this population with time, but we can also gain information going beyond the obvious time trends. By combining the biomonitoring data with pharmacokinetic modeling, we can re-construct the time-variant exposure to individual POPs, determine their intrinsic elimination half-lives in the human body, and predict future levels of POPs in the population. Different approaches have been employed to extract information from human biomonitoring data. Pharmacokinetic (PK) models were combined with longitudinal data1, with single2 or multiple3 average concentrations of a cross-sectional data (CSD), or finally with multiple CSD with or without empirical exposure data4. In the latter study, for the first time, the authors based their modeling outputs on two sets of CSD and empirical exposure data, which made it possible that their model outputs were further constrained due to the extensive body of empirical measurements. Here we use a PK model to analyze recent levels of PBDE concentrations measured in the Australian population. In this study, we are able to base our model results on four sets5-7 of CSD; we focus on two PBDE congeners that have been shown3,5,8-9 to differ in intake rates and half-lives with BDE-47 being associated with high intake rates and a short half-life and BDE-153 with lower intake rates and a longer half-life. By fitting the model to PBDE levels measured in different age groups in different years, we determine the level of intake of BDE-47 and BDE-153, as well as the half-lives of these two chemicals in the Australian population.

The moderating effect of knowledge sharing on the relationship between manufacturing activities and business performance

This paper investigates the critical role of knowledge sharing (KS) in leveraging manufacturing activities, namely integrated supplier management (ISM) and new product development (NPD) to improve business performance (BP) within the context of Taiwanese electronic manufacturing companies. The research adopted a sequential mixed method research design, which provided both quantitative empirical evidence as well as qualitative insights, into the moderating effect of KS on the relationships between these two core manufacturing activities and BP. First, a questionnaire survey was administered, which resulted in a sample of 170 managerial and technical professionals providing their opinions on KS, NPD and ISM activities and the BP level within their respective companies. On the basis of the collected data, factor analysis was used to verify the measurement model, followed by correlation analysis to explore factor interrelationships, and finally moderated regression analyses to extract the moderating effects of KS on the relationships of NPD and ISM with BP. Following the quantitative study, six semi-structured interviews were conducted to provide qualitative in-depth insights into the value added from KS practices to the targeted manufacturing activities and the extent of its leveraging power. Results from quantitative statistical analysis indicated that KS, NPD and ISM all have a significant positive impact on BP. Specifically, IT infrastructure and open communication were identified as the two types of KS practices that could facilitate enriched supplier evaluation and selection, empower active employee involvement in the design process, and provide support for product simplification and the modular design process, thereby improving manufacturing performance and strengthening company competitiveness. The interviews authenticated many of the empirical findings, suggesting that in the contemporary manufacturing context KS has become an integral part of many ISM and NPD activities and when embedded properly can lead to an improvement in BP. The paper also highlights a number of useful implications for manufacturing companies seeking to leverage their BP through innovative and sustained KS practices.

Static analysis of executables for collaborative malware detection on Android

Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.

Detecting Symbian OS malware through static function call analysis

Smartphones become very critical part of our lives as they offer advanced capabilities with PC-like functionalities. They are getting widely deployed while not only being used for classical voice-centric communication. New smartphone malwares keep emerging where most of them still target Symbian OS. In the case of Symbian OS, application signing seemed to be an appropriate measure for slowing down malware appearance. Unfortunately, latest examples showed that signing can be bypassed resulting in new malware outbreak. In this paper, we present a novel approach to static malware detection in resource-limited mobile environments. This approach can be used to extend currently used third-party application signing mechanisms for increasing malware detection capabilities. In our work, we extract function calls from binaries in order to apply our clustering mechanism, called centroid. This method is capable of detecting unknown malwares. Our results are promising where the employed mechanism might find application at distribution channels, like online application stores. Additionally, it seems suitable for directly being used on smartphones for (pre-)checking installed applications.

Product reputation model : an opinion mining based approach

Product rating systems are very popular on the web, and users are increasingly depending on the overall product ratings provided by websites to make purchase decisions or to compare various products. Currently most of these systems directly depend on users’ ratings and aggregate the ratings using simple aggregating methods such as mean or median [1]. In fact, many websites also allow users to express their opinions in the form of textual product reviews. In this paper, we propose a new product reputation model that uses opinion mining techniques in order to extract sentiments about product’s features, and then provide a method to generate a more realistic reputation value for every feature of the product and the product itself. We considered the strength of the opinion rather than its orientation only. We do not treat all product features equally when we calculate the overall product reputation, as some features are more important to customers than others, and consequently have more impact on customers buying decisions. Our method provides helpful details about the product features for customers rather than only representing reputation as a number only.

Ontology-based feature modeling for construction information extraction from a building information model

A building information model (BIM) provides a rich representation of a building's design. However, there are many challenges in getting construction-specific information from a BIM, limiting the usability of BIM for construction and other downstream processes. This paper describes a novel approach that utilizes ontology-based feature modeling, automatic feature extraction based on ifcXML, and query processing to extract information relevant to construction practitioners from a given BIM. The feature ontology generically represents construction-specific information that is useful for a broad range of construction management functions. The software prototype uses the ontology to transform the designer-focused BIM into a construction-specific feature-based model (FBM). The formal query methods operate on the FBM to further help construction users to quickly extract the necessary information from a BIM. Our tests demonstrate that this approach provides a richer representation of construction-specific information compared to existing BIM tools.