Biopharming the SimpliRED™ HIV diagnostic reagent in barley, potato and tobacco

This is the first report of an antibody-fusion protein expressed in transgenic plants for direct use in a medical diagnostic assay. By the use of gene constructs with appropriate promoters, high level expression of an anti-glycophorin single-chain antibody fused to an epitope of the HIV virus was obtained in the leaves and stems of tobacco, tubers of potato and seed of barley. This fusion protein replaces the SimpliRED™ diagnostic reagent, used for detecting the presence of HIV-1 antibodies in human blood. The reagent is expensive and laborious to produce by conventional means since chemical modifications to a monoclonal antibody are required. The plant-produced fusion protein was fully functional (by ELISA) in crude extracts and, for tobacco at least, could be used without further purification in the HIV agglutination assay. All three crop species produced sufficient reagent levels to be superior bioreactors to bacteria or mice, however barley grain was the most attractive bioreactor as it expressed the highest level (150 μg of reagent g-1), is inexpensive to produce and harvest, poses a minuscule gene flow problem in the field, and the activity of the reagent is largely undiminished in stored grain. This work suggests that barley seed will be an ideal factory for the production of antibodies, diagnostic immunoreagents, vaccines and other pharmaceutical proteins.

Air pollution, temperature and pediatric influenza in Brisbane, Australia

Previous studies have demonstrated the importance of weather variables in influencing the incidence of influenza. However, the role of air pollution is often ignored in identifying the environmental drivers of influenza. This research aims to examine the impacts of air pollutants and temperature on the incidence of pediatric influenza in Brisbane, Australia. Lab-confirmed daily data on influenza counts among children aged 0-14years in Brisbane from 2001 January 1st to 2008 December 31st were retrieved from Queensland Health. Daily data on maximum and minimum temperatures for the same period were supplied by the Australian Bureau of Meteorology. Winter was chosen as the main study season due to it having the highest pediatric influenza incidence. Four Poisson log-linear regression models, with daily pediatric seasonal influenza counts as the outcome, were used to examine the impacts of air pollutants (i.e., ozone (O3), particulate matter≤10μm (PM10) and nitrogen dioxide (NO2)) and temperature (using a moving average of ten days for these variables) on pediatric influenza. The results show that mean temperature (Relative risk (RR): 0.86; 95% Confidence Interval (CI): 0.82-0.89) was negatively associated with pediatric seasonal influenza in Brisbane, and high concentrations of O3 (RR: 1.28; 95% CI: 1.25-1.31) and PM10 (RR: 1.11; 95% CI: 1.10-1.13) were associated with more pediatric influenza cases. There was a significant interaction effect (RR: 0.94; 95% CI: 0.93-0.95) between PM10 and mean temperature on pediatric influenza. Adding the interaction term between mean temperature and PM10 substantially improved the model fit. This study provides evidence that PM10 needs to be taken into account when evaluating the temperature-influenza relationship. O3 was also an important predictor, independent of temperature.

The impact of tobacco smoking on treatment for comorbid depression and alcohol misuse

Tobacco use is a major public health concern, and is associated with a number of mental illnesses as well as increased alcohol/other drug (AOD). Research into treatment for individuals experiencing such comorbidities is limited. Participants (n = 447) were those enrolled in the Depression and Alcohol Integrated and Single-focused Interventions project (Baker et al. 2010), and the Self Help for Alcohol/other drugs and DEpression project (Kay-Lambkin et al. Medical Journal of Australia 195:S44-S50, 2011a, Journal of Medical Internet Research 13(1):e11p11, b), who reported current depression and hazardous alcohol use at entry to the study. Smoking cessation was not targeted in, nor a goal of, treatment. After controlling for socioeconomic variables, tobacco use was not associated with higher levels of depressive symptoms at baseline; however heavy smokers (30+ cigarettes per day) consumed significantly more alcohol at baseline than did non-smokers (13 vs. 9 standard drinks per day). Baseline smoking severity did not impact on depression or alcohol use outcomes over a 12-month period. Reductions in tobacco use between baseline and 3-month follow-up were significantly associated with reductions in depression and alcohol consumption over the same time period. The study results suggest that tobacco use does not interfere with treatment for depression and alcohol use problems, and adds weight to the idea of considering specific treatment for tobacco use in the context of treatment for alcohol/other drug use.

Using image quality metrics to detect smoke-affected laser data

Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.

Selective combination of visual and thermal imaging for resilient localization in adverse conditions : day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.

Combining multiple sensor modalities for a localisation robust to smoke

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference.

Trade in 'Dirty Air' : carbon crime and the politics of pollution

‘Carbon trading fraudsters may have accounted for up to 90% of all market activity in some European countries, with criminals pocketing billions, mainly in Britain, France, Spain, Denmark and Holland, according to Europol and the European law enforcement agency.’ (Mason, 2009). ‘Carbon offset projects often result in land grabs, local environmental and social conflicts, as well as the repression of local communities and movements. The CDM approval process for projects allows little space for the voices of Indigenous Peoples and local communities – in fact, no project has ever been rejected on the grounds of rights violations, despite these being widespread’. (Carbon Trade Watch, 2013)

Preventing pollution attacks in multi-source network coding

Network coding is a method for achieving channel capacity in networks. The key idea is to allow network routers to linearly mix packets as they traverse the network so that recipients receive linear combinations of packets. Network coded systems are vulnerable to pollution attacks where a single malicious node floods the network with bad packets and prevents the receiver from decoding correctly. Cryptographic defenses to these problems are based on homomorphic signatures and MACs. These proposals, however, cannot handle mixing of packets from multiple sources, which is needed to achieve the full benefits of network coding. In this paper we address integrity of multi-source mixing. We propose a security model for this setting and provide a generic construction.

Sustainable Urban Water Environment : Climate, Pollution and Adaptation

"This multi-disciplinary book provides practical solutions for safeguarding the sustainability of the urban water environment. Firstly, the importance of the urban water environment is highlighted and the major problems urban water bodies face and strategies to safeguard the water environment are explored. Secondly, the diversity of pollutants entering the water environment through stormwater runoff are discussed and modelling approaches for factoring in climate change and future urban and transport scenarios are proposed. Thirdly, by linking the concepts of sustainable urban ecosystems and sustainable urban and transport development, capabilities of two urban sustainability assessment models are demonstrated."--publisher website

Transcript and metabolite profiling for the evaluation of tobacco tree and poplar as feedstock for the bio-based industry

The global demand for food, feed, energy and water poses extraordinary challenges for future generations. It is evident that robust platforms for the exploration of renewable resources are necessary to overcome these challenges. Within the multinational framework MultiBioPro we are developing biorefinery pipelines to maximize the use of plant biomass. More specifically, we use poplar and tobacco tree (Nicotiana glauca) as target crop species for improving saccharification, isoprenoid, long chain hydrocarbon contents, fiber quality, and suberin and lignin contents. The methods used to obtain these outputs include GC-MS, LC-MS and RNA sequencing platforms. The metabolite pipelines are well established tools to generate these types of data, but also have the limitations in that only well characterized metabolites can be used. The deep sequencing will allow us to include all transcripts present during the developmental stages of the tobacco tree leaf, but has to be mapped back to the sequence of Nicotiana tabacum. With these set-ups, we aim at a basic understanding for underlying processes and at establishing an industrial framework to exploit the outcomes. In a more long term perspective, we believe that data generated here will provide means for a sustainable biorefinery process using poplar and tobacco tree as raw material. To date the basal level of metabolites in the samples have been analyzed and the protocols utilized are provided in this article.

Air pollution and invisible violence

In recent years air pollution has been referred to as an ‘invisible killer’, and ‘an invisible health crisis’ (European Respiratory Society 2012). As other chapters in this collection have argued, the invisibility of crime is manifested through various lenses: lack of knowledge, lack of political and media attention, an absence of policing and regulatory focus, and an unwitting and ill-informed public. All such arguments pertain to air pollution; however, toxic emissions are also literally invisible from sight and consciousness, as are the associated consequences.

A comparative study of airborne particulate pollution at two bus stations of different site geography

Airborne particulate pollutant is considered to be one of the major harmful emissions produced by vehicle engines as it has been directly linked to serious health problems. Passengers spend long times at bus stations and may be exposed to high concentrations of pollution. Particle pollution at two bus stations in Brisbane, Australia were monitored. The two bus stations consisted of markedly different site geography and surroundings with one situated in a street canyon and the other elevated above ground level. The same flow of traffic operated through both stations. Real time measurements of ultrafine particle concentration, size distribution and meteorological conditions were carried out on the platform continuously over several days. The results showed that the particle number concentrations were significantly different at the two stations, suggesting that the layout of site geometry and surroundings was a dominant determining factor through the injection of fresh air into the station platforms and the rates of dilution.

Assessment on the effect of pollution abatement on environmental efficiency with Markov chain Monte Carlo simulation

Both environmental economists and policy makers have shown a great deal of interest in the effect of pollution abatement on environmental efficiency. In line with the modern resources available, however, no contribution is brought to the environmental economics field with the Markov chain Monte Carlo (MCMC) application, which enables simulation from a distribution of a Markov chain and simulating from the chain until it approaches equilibrium. The probability density functions gained prominence with the advantages over classical statistical methods in its simultaneous inference and incorporation of any prior information on all model parameters. This paper concentrated on this point with the application of MCMC to the database of China, the largest developing country with rapid economic growth and serious environmental pollution in recent years. The variables cover the economic output and pollution abatement cost from the year 1992 to 2003. We test the causal direction between pollution abatement cost and environmental efficiency with MCMC simulation. We found that the pollution abatement cost causes an increase in environmental efficiency through the algorithm application, which makes it conceivable that the environmental policy makers should make more substantial measures to reduce pollution in the near future.

Decomposition analysis of air pollution abatement in China: empirical study for ten industrial sectors from 1998 to 2009

This study analyzes the management of air pollutant substance in Chinese industrial sectors from 1998 to 2009. Decomposition analysis applying the logarithmic mean divisia index is used to analyze changes in emissions of air pollutants with a focus on the following five factors: coal pollution intensity (CPI), end-of-pipe treatment (EOP), the energy mix (EM), productive efficiency change (EFF), and production scale changes (PSC). Three pollutants are the main focus of this study: sulfur dioxide (SO2), dust, and soot. The novelty of this paper is focusing on the impact of the elimination policy on air pollution management in China by type of industry using the scale merit effect for pollution abatement technology change. First, the increase in SO2 emissions from Chinese industrial sectors because of the increase in the production scale is demonstrated. However, the EOP equipment that induced this change and improvements in energy efficiency has prevented an increase in SO2 emissions that is commensurate with the increase in production. Second, soot emissions were successfully reduced and controlled in all industries except the steel industry between 1998 and 2009, even though the production scale expanded for these industries. This reduction was achieved through improvements in EOP technology and in energy efficiency. Dust emissions decreased by nearly 65% between 1998 and 2009 in the Chinese industrial sectors. This successful reduction in emissions was achieved by implementing EOP technology and pollution prevention activities during the production processes, especially in the cement industry. Finally, pollution prevention in the cement industry is shown to result from production technology development rather than scale merit. © 2013 Elsevier Ltd. All rights reserved.

Does an environmental Kuznets curve for waste pollution exist in China?

China is an emerging and leading world economy. The pace of economic change has been tremendously rapid since the beginning of economic reforms. Despite the importance of the Environmental Kuznets Curve (EKC) and environmental problems in China, no previous study has tested the EKC in China because of the difficulty in obtaining data and the need to adjust the data. The focus of this paper is to test the EKC in China using province level data over the period 1992-2003. This study applies non-parametric techniques to estimate the relationship between income and the environmental quality of wastewater, air pollution and solid waste. Copyright © 2009 Inderscience Enterprises Ltd.