Activation of matrix metalloproteinase-2 (MMP-2) by membrane type 1 matrix metalloproteinase through an artificial receptor for ProMMP-2 generates active MMP-2

The suggested model for pro-matrix metalloproteinase-2 (proMMP-2) activation by membrane type 1 MMP (MT1-MMP) implicates the complex between MT1-MMP and tissue inhibitor of MMP-2 (TIMP-2) as a receptor for proMMP-2. To dissect this model and assess the pathologic significance of MMP-2 activation, an artificial receptor for proMMP-2 was created by replacing the signal sequence of TIMP-2 with cytoplasmic/transmembrane domain of type II transmembrane mosaic serine protease (MSP-T2). Unlike TIMP-2, MSP-T2 served as a receptor for proMMP-2 without inhibiting MT1-MMP, and generated TIMP-2-free active MMP-2 even at a low level of MT1-MMP. Thus, MSP-T2 did not affect direct cleavage of the substrate testican-1 by MT1-MMP, whereas TIMP-2 inhibited it even at the level that stimulates proMMP-2 processing. Expression of MSP-T2 in HT1080 cells enhanced MMP-2 activation by endogenous MT1-MMP and caused intensive hydrolysis of collagen gel. Expression of MSP-T2 in U87 glioma cells, which express a trace level of endogenous MT1-MMP, induced MMP-2 activation and enhanced cell-associated protease activity, activation of extracellular signal-regulated kinase, and metastatic ability into chick embryonic liver and lung. MT1-MMP can exert both maximum MMP-2 activation and direct cleavage of substrates with MSP-T2, which cannot be achieved with TIMP-2. These results suggest that MMP-2 activation by MT1-MMP potentially amplifies protease activity, and combination with direct cleavage of substrate causes effective tissue degradation and enhances tumor invasion and metastasis, which highlights the complex role of TIMP-2. MSP-T2 is a unique tool to analyze physiologic and pathologic roles of MMP-2 and MT1-MMP in comparison with TIMP-2.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Resilience and opportunity : regions and the roll-out of Australia's greenhouse gas abatement programs. A manual for Queensland's NRM regions

Voluntary and compliance markets for forest carbon and other (emission avoidance and biosequestration) activities are growing internationally and across Australia. Queensland and its Natural Resource Management (NRM) regions have an opportunity to take a variety of actions to help guide these markets to secure multiple landscape benefits and to build landscape resilience in the face of climate change. As the national arrangements for offsets within Australia’s Clean Energy Package (CEP) and emissions trading environment emerge, Queensland’s regions can prepare themselves and their landholding communities to take advantage of these opportunities to deliver improved climate resilience in their regional landscapes.

A review of the potential role of greenhouse gas abatement in native vegetation management in Queensland's rangelands

Concern about the risk of harmful human-induced climate change has resulted in international efforts to reduce greenhouse gas emissions to the atmosphere. We review the international and national context for consideration of greenhouse abatement in native vegetation management and discuss potential options in Queensland. Queensland has large areas of productive or potentially productive land with native woody vegetation cover with approximately 76 million ha with woody cover remaining in 1991. High rates of tree clearing, predominantly to increase pasture productivity, continued throughout the 1990s with an average 345,000 ha/a estimated to have been cleared, including non-remnant (woody regrowth) as well as remnant vegetation. Estimates of greenhouse gas emissions associated with land clearing currently have a high uncertainty but clearing was reported to contribute a significant proportion of Australia's total greenhouse gas emissions from 1990 (21%) to 1999 (13%). In Queensland, greenhouse emissions from land clearing were estimated to have been 54.5 Mt CO(2)-e in 1999. Management of native vegetation for timber harvesting and the proliferation of woody vegetation (vegetation thickening) in the grazed woodlands also represent large carbon fluxes. Forestry (plantations and native forests) in Queensland was reported to be a 4.4 Mt CO(2)-e sink in 1999 but there are a lack of comprehensive data on timber harvesting in private hardwood forests. Vegetation thickening is reported for large areas of the c. 60 million ha grazed woodlands in Queensland. The magnitude of the carbon sink in 27 million ha grazed eucalypt woodlands has been estimated to be 66 Mt CO(2)-e/a but this sink is not currently included in Australia's inventory of anthropogenic greenhouse emissions. Improved understanding of the function and dynamics of natural and managed ecosystems is required to support management of native vegetation to preserve and enhance carbon stocks for greenhouse benefits while meeting objectives of sustainable and productive management and biodiversity protection.

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.

Environmental performance and returns to pollution abatement in China

Because of China's extremely rapid economic growth, the scale and seriousness of environmental problems is no longer in doubt. Whether pollution abatement technologies are utilized more efficiently is crucial in the analysis of environmental management in China. This study analyzes how the performance of environmental management has changed over time using province level data for 1992-2003. Mixed results for environmental performance are shown using nonparametric estimation technique. We find that environmental performance index, abatement effort, and increasing returns to pollution abatement play important roles in determining the pollution level over the period of the study.

Effects of artificial shade on attack by the mahogany shoot borer, Hypsipyla robusta (Moore)

Swietenia macrophylla King (Meliaceae: Swietenioideae) provides one of the premier timbers of the world. The mahogany shoot borer Hypsipyla robusta Moore (Lepidoptera: Pyralidae) is an economically important pest of S. macrophylla throughout Asia, Africa and the Pacific. No viable method of controlling this pest is known. Previous observations have suggested that the presence of overhead shade may reduce attack by H. robusta, but this has not been investigated experimentally. This research was therefore designed to assess the influence of light availability on shoot-borer attack on S. macrophylla, by establishing seedlings under three different artificial shade regimes, then using these seedlings to test oviposition preference of adult moths, neonate larval survival and growth and development of shoot borer larvae. Oviposition preference of shoot borer moths was tested on leaves from seedlings grown under artificial shade for 63 weeks. A significant difference in choice was recorded between treatments, with 27.4 ± 1.5 eggs laid under high shade and 87.1 ± 1.8 under low shade. Neonate larval survival on early flushing leaflets of S. macrophylla did not differ significantly between shade treatments. Larval growth rate, estimated by measuring daily frass width, was significantly higher for those larvae fed on seedlings from the high and medium shade treatments (0.1 mm/day), than the low shade treatment (0.06 mm/day). In laboratory-reared larvae, the total mass of frass produced was significantly higher in the high shade treatment (0.4 g) than under the low shade treatment (0.2 g). Longer tunnel lengths were bored by larvae in plants grown under high shade (12.0 ± 2.4 cm) than under low shade (7.07 ± 1.9 cm). However, pupal mass under low shade was 48% higher than that under the high shade treatment, suggesting that plants grown under high shade were of lower nutritional quality for shoot borer larvae. These results indicate that shading of mahogany seedlings may reduce the incidence of shoot borer attack, by influencing both oviposition and larval development. The establishment of mahogany under suitable shade regimes may therefore provide a basis for controlling shoot borer attack using silvicultural approaches.

Book review : "Artificial Hells: participatory art and the politics of spectatorship by Claire Bishop"

In the years since Nicolas Bourriaud’s Relational Aesthetics (1998) was published, a plethora of books (Shannon Jackson’s Social Works: Performing Art, Supporting Publics [2011], Nato Thompson’s Living as Form: Socially Engaged Art from 1991–2011 [2011], Grant Kester’s Conversation Pieces: Community and Communication in Modern Art [2004], Pablo Helguera’s Education for Socially Engaged Art: A Material and Techniques Handbook [2011]), conferences and articles have surfaced creating a rich and textured discourse that has responded to, critiqued and reconfigured the proposed social utopias of Bourriaud’s aesthetics. As a touchstone for this emerging discourse, Relational Aesthetics outlines in a contemporary context the plethora of social and process-based art forms that took as their medium the ‘social’. It is, however, Clare Bishop’s book Artificial Hells: Participatory Art and the Politics of Spectatorship (Verso), that offers a deeper art historical and theoretically considered rendering of this growing and complicated form of art, and forms a central body of work in this broad constellation of writings about participatory art, or social practice art/socially engaged art (SEA), as it is now commonly known...

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Identification of New Zealand bats in flight from analysis of echolocation calls by artificial neural networks.

Time-expanded and heterodyned echolocation calls of the New Zealand long-tailed Chalinolobus tuberculatus and lesser short-tailed bat Mystacina tuberculata were recorded and digitally analysed. Temporal and spectral parameters were measured from time-expanded calls and power spectra generated for both time-expanded and heterodyned calls. Artificial neural networks were trained to classify the calls of both species using temporal and spectral parameters and power spectra as input data. Networks were then tested using data not previously seen. Calls could be unambiguously identified using parameters and power spectra from time-expanded calls. A neural network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 40 kHz (the frequency with the most energy of the fundamental of C. tuberculatus call), could identify 99% and 84% of calls of C. tuberculatus and M. tuberculata, respectively. A second network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 27 kHz (the frequency with the most energy of the fundamental of M. tuberculata call), could identify 34% and 100% of calls of C. tuberculatus and M. tuberculata, respectively. This study represents the first use of neural networks for the identification of bats from their echolocation calls. It is also the first study to use power spectra of time-expanded and heterodyned calls for identification of chiropteran species. The ability of neural networks to identify bats from their echolocation calls is discussed, as is the ecology of both species in relation to the design of their echolocation calls.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.