Atmospheric pressure gas plasma-induced colorectal cancer cell death is mediated by Nox2-ASK1 apoptosis pathways and oxidative stress is mitigated by Srx-Nrf2 anti-oxidant system

Atmospheric pressure gas plasma (AGP) generates reactive oxygen species (ROS) that induce apoptosis in cultured cancer cells. The majority of cancer cells develop a ROS-scavenging anti-oxidant system regulated by Nrf2, which confers resistance to ROS-mediated cancer cell death. Generation of ROS is involved in the AGP-induced cancer cell death of several colorectal cancer cells (Caco2, HCT116 and SW480) by activation of ASK1-mediated apoptosis signaling pathway without affecting control cells (human colonic sub-epithelial myofibroblasts; CO18, human fetal lung fibroblast; MRC5 and fetal human colon; FHC). However, the identity of an oxidase participating in AGP-induced cancer cell death is unknown. Here, we report that AGP up-regulates the expression of Nox2 (NADPH oxidase) to produce ROS. RNA interference designed to target Nox2 effectively inhibits the AGP-induced ROS production and cancer cell death. In some cases both colorectal cancer HT29 and control cells showed resistance to AGP treatment. Compared to AGP-sensitive Caco2 cells, HT29 cells show a higher basal level of the anti-oxidant system transcriptional regulator Nrf2 and its target protein sulfiredoxin (Srx) which are involved in cellular redox homeostasis. Silencing of both Nrf2 and Srx sensitized HT29 cells, leads to ROS overproduction and decreased cell viability. This indicates that in HT29 cells, Nrf2/Srx axis is a protective factor against AGP-induced oxidative stress. The inhibition of Nrf2/Srx signaling should be considered as a central target in drug-resistant colorectal cancer treatments.

An extended regression approach to estimating loads and their uncertainties in Great Barrier Reef catchments

There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates by minimizing the biases and making use of possible predictive variables. The load estimation procedure can be summarized by the following four steps: - (i) output the flow rates at regular time intervals (e.g. 10 minutes) using a time series model that captures all the peak flows; - (ii) output the predicted flow rates as in (i) at the concentration sampling times, if the corresponding flow rates are not collected; - (iii) establish a predictive model for the concentration data, which incorporates all possible predictor variables and output the predicted concentrations at the regular time intervals as in (i), and; - (iv) obtain the sum of all the products of the predicted flow and the predicted concentration over the regular time intervals to represent an estimate of the load. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized regression (rating-curve) approach with additional predictors that capture unique features in the flow data, namely the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and cumulative discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. The model also has the capacity to accommodate autocorrelation in model errors which are the result of intensive sampling during floods. Incorporating this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach using the concentrations of total suspended sediment (TSS) and nitrogen oxide (NOx) and gauged flow data from the Burdekin River, a catchment delivering to the Great Barrier Reef. The sampling biases for NOx concentrations range from 2 to 10 times indicating severe biases. As we expect, the traditional average and extrapolation methods produce much higher estimates than those when bias in sampling is taken into account.

Evaluation of residence time on nitrogen oxides removal in non-thermal plasma reactor

Non-thermal plasma (NTP) has been introduced over the last few years as a promising after- treatment system for nitrogen oxides and particulate matter removal from diesel exhaust. NTP technology has not been commercialised as yet, due to its high rate of energy consumption. Therefore, it is important to seek out new methods to improve NTP performance. Residence time is a crucial parameter in engine exhaust emissions treatment. In this paper, different electrode shapes are analysed and the corresponding residence time and NOx removal efficiency are studied. An axisymmetric laminar model is used for obtaining residence time distribution numerically using FLUENT software. If the mean residence time in a NTP plasma reactor increases, there will be a corresponding increase in the reaction time and consequently the pollutant removal efficiency increases. Three different screw thread electrodes and a rod electrode are examined. The results show the advantage of screw thread electrodes in comparison with the rod electrode. Furthermore, between the screw thread electrodes, the electrode with the thread width of 1 mm has the highest NOx removal due to higher residence time and a greater number of micro-discharges. The results show that the residence time of the screw thread electrode with a thread width of 1 mm is 21% more than for the rod electrode.

Experimental investigation of the effects of oxygenated fuels on exhaust emissions in a heavy duty diesel engine

Physical and chemical properties of biofuels vary among various feedstocks and their subsequent conversions to fuels. The biofuels contain various amounts of oxygen, and this has a significant influence on exhaust emission. This oxygen content has been considered in order to investigate its effect on diesel engine exhaust emissions. The experiments have been conducted with a heavy duty diesel engine and various oxygenated fuels. It is found that the amount of oxygen in the fuel has a high level of influence on its exhaust emissions, and this provides agreement with diesel emissions results such as PN reduction. By increasing the amount of oxygen in the blend (by adding more biofuel), the particulate number (PN) is reduced and NOx increases gradually. However, the variation of PN and NOx are not similar for waste cooking biodiesel (WCBD) and butanol blend, even though their oxygen content are the same in the blends. This is due to the source of the biofuel and their internal chemistry.

Variability in exposure to ambient ultrafine particles in urban schools: Comparative assessment between Australia and Spain

Ambient ultrafine particle number concentrations (PNC) have inhomogeneous spatio-temporal distributions and depend on a number of different urban factors, including background conditions and distant sources. This paper quantitatively compares exposure to ambient ultrafine particles at urban schools in two cities in developed countries, with high insolation climatic conditions, namely Brisbane (Australia) and Barcelona (Spain). The analysis used comprehensive indoor and outdoor air quality measurements at 25 schools in Brisbane and 39 schools in Barcelona. PNC modes were analysed with respect to ambient temperature, land use and urban characteristics, combined with the measured elemental carbon concentrations, NOx (Brisbane) and NO2 (Barcelona). The trends and modes of the quantified weekday average daily cycles of ambient PNC exhibited significant differences between the two cities. PNC increases were observed during traffic rush hours in both cases. However, the mid-day peak was dominant in Brisbane schools and had the highest contribution to total PNC for both indoors and outdoors. In Barcelona, the contribution from traffic was highest for ambient PNC, while the mid-day peak had a slightly higher contribution for indoor concentrations. Analysis of the relationships between PNC and land use characteristics in Barcelona schools showed a moderate correlation with the percentage of road network area and an anti-correlation with the percentage of green area. No statistically significant correlations were found for Brisbane. Overall, despite many similarities between the two cities, school-based exposure patterns were different. The main source of ambient PNC at schools was shown to be traffic in Barcelona and mid-day new particle formation in Brisbane. The mid-day PNC peak in Brisbane could have been driven by the combined effect of background and meteorological conditions, as well as other local/distant sources. The results have implications for urban development, especially in terms of air quality mitigation and management at schools.