Study of Selected United States Air Force Magazines

Technical Journalis

Determination of diquat by flow injection-chemiluminescence

A simple, economic, sensitive and rapid method for the determination of the pesticide diquat was described. This new method was based on the coupling of flow injection analysis methodology and direct chemiluminescent detection; to the authors' knowledge, this approach had not been used up to now with this pesticide. It was based on its oxidation with ferricyanide in alkaline medium; significant improvements in the analytical signal were achieved by using high temperatures and quinine as sensitiser. Its high throughput (144 h(-1)), together with its low limit of detection (2 ng mL(-1)), achieved without need of preconcentration steps, permitted the reliable quantification of diquat over the linear range of (0.01-0.6) mu g mL(-1) in samples from different origins (river, tap, mineral and ground waters), even in the presence of a 40-fold concentration of paraquat, a pesticide commonly present in the commercial formulations of diquat.López-Paz, JL.; Catalá-Icardo, M.; Antón Garrido, B. (2009). Determination of diquat by flow injection-chemiluminescence. Analytical and Bioanalytical Chemistry. 394(4):1073-1079. doi:10.1007/s00216-009-2609-zS107310793944Hayes WJ Jr, Laws ER Jr (1991) Handbook of pesticide toxicology, Academic Press, San DiegoUS Environmental Protection Agency. http://www.epa.gov/06WDW/contaminants/dw_contamfs/diquat.html (accessed in August 2008)Horwitz W (2000) Official methods of analysis of AOAC International 17th edition. AOAC International, Gaithersburg, MD, USAHara S, Sasaki N, Takase D, Shiotsuka S, Ogata K, Futagami K, Tamura K (2007) Anal Sci 23(5):523–531Rial Otero R, Cancho Grande B, Pérez Lamela C, Simal Gandara J, Aria Estevez M (2006) J Chromatogr Sci 44(9):539–542Aramendia MA, Borau V, Lafont F, Marinas JM, Moreno JM, Porras JM, Urbano FJ (2006) Food Chem 97(1):181–188Nuñez O, Moyano E, Galceran MT (2004) Anal Chim Acta 525(2):183–190Martinez Vidal JL, Belmonte Vega A, Sanchez Lopez FJ, Garrido Frenich AJ (2004) Chromatogr A 1050(2):179–184Lee XP, Kumazawa T, Fujishiro M, Hasegawa C, Arinobu T, Seno H, Sato K (2004) J Mass Spectrom 39(10):1147–1152De Almeida RM, Yonamine M (2007) J Chromatogr B 853(1–2):260–264De Souza D, Machado SAS (2006) Electroanalysis 18(9):862–872De Souza D, Da Silva MRC, Machado SAS (2006) Electroanalysis 18(23):2305–2313Picó Y, Rodriguez R, Manes J (2003) Trends Anal Chem 22(3):133–151Ishiwata T (2004) Bunseki Kagaku 53(8):863–864Carneiro MC, Puignou L, Galcerán MT (2000) Anal Chim Acta 408:263Luque M, Rios A, Valcarcel M (1998) Analyst 123(11):2383–2387Perez Ruiz T, Martínez Lozano C, Tomas V (1991) Int J Environ Anal Chem 44(4):243–252Perez Ruiz T, Martínez Lozano C, Tomas V (1991) Anal Chim Acta 244(1):99–104Townshend A (1990) Analyst 115:495–500López Paz JL, Catalá Icardo M (2008) Anal Chim Acta 625:173–179Pawlicová Z, Sahuquillo I, Catalá Icardo M, García Mateo JV, Martínez Calatayud J (2006) Anal Sci 22:29–34Albert García JR, Catalá Icardo M, Martínez Calatayud J (2006) Talanta 69:608–614Tomlin CDS (1997) The pesticide manual, 11th edn.The British Crop Protection CouncilUKCatalá-Icardo M, Martínez-Calatayud J (2008) Crit Rev Anal Chem 38:118–130Ministerio de Medio Ambiente y Medio Rural y Marino. http://www.marm.es/ (accessed in September 2008)US Environmental Protection Agency. http://www.epa.gov/OGWWDW/contaminants (accessed in October 2008

Natural and anthropogenic changes to mangrove distributions in the Pioneer River Estuary (QLD, Australia)

We analyzed a time series of aerial photographs and Landsat satellite imagery of the Pioneer River Estuary (near Mackay, Queensland, Australia) to document both natural and anthropogenic changes in the area of mangroves available to filter river runoff between 1948 and 2002. Over 54 years, there was a net loss of 137 ha (22%) of tidal mangroves during four successive periods that were characterized by different driving mechanisms: (1) little net change (1948– 1962); (2) net gain from rapid mangrove expansion (1962–1972); (3) net loss from clearing and tidal isolation (1972–1991); and (4) net loss from a severe species-specific dieback affecting over 50% of remaining mangrove cover (1991–2002). Manual digitization of aerial photographs was accurate for mapping changes in the boundaries of mangrove distributions, but this technique underestimated the total loss due to dieback. Regions of mangrove dieback were identified and mapped more accurately and efficiently after applying the Normalized Difference Vegetation Index (NDVI) to Landsat Thematic Mapper satellite imagery, and then monitoring changes to the index over time. These remote sensing techniques to map and monitor mangrove changes are important for identifying habitat degradation, both spatially and temporally, in order to prioritize restoration for management of estuarine and adjacent marine ecosystems

How robust are the natural history parameters used in chlamydia transmission dynamic models? A systematic review

Transmission dynamic models linked to economic analyses often form part of the decision making process when introducing new chlamydia screening interventions. Outputs from these transmission dynamic models can vary depending on the values of the parameters used to describe the infection. Therefore these values can have an important influence on policy and resource allocation. The risk of progression from infection to pelvic inflammatory disease has been extensively studied but the parameters which govern the transmission dynamics are frequently neglected. We conducted a systematic review of transmission dynamic models linked to economic analyses of chlamydia screening interventions to critically assess the source and variability of the proportion of infections that are asymptomatic, the duration of infection and the transmission probability. We identified nine relevant studies in Pubmed, Embase and the Cochrane database. We found that there is a wide variation in their natural history parameters, including an absolute difference in the proportion of asymptomatic infections of 25% in women and 75% in men, a six-fold difference in the duration of asymptomatic infection and a four-fold difference in the per act transmission probability. We consider that much of this variation can be explained by a lack of consensus in the literature. We found that a significant proportion of parameter values were referenced back to the early chlamydia literature, before the introduction of nucleic acid modes of diagnosis and the widespread testing of asymptomatic individuals. In conclusion, authors should use high quality contemporary evidence to inform their parameter values, clearly document their assumptions and make appropriate use of sensitivity analysis. This will help to make models more transparent and increase their utility to policy makers

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS