Development of a prototype plastic space erectable satellite

Prototype erectable communications satellite of spherical design using plastic memory effec

Development of a prototype plastic space erectable satellite Quarterly report, Jun. - Aug. 1966

Copper plated high-density polyethylene film evaluation for space erectable satellite desig

Stimulation of gastrointestinal antibody to Shiga toxin by orogastric immunization in mice

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141579/1/imcb69.pd

Evaluation of the health status of Araucaria araucana trees using hyperspectral images

Revista oficial de la Asociación Española de Teledetección[EN] The Araucaria araucana is an endemic species from Chile and Argentina, which has a high biological, scientific and cultural value and since 2016 has shown a severe affection of leaf damage in some individuals, causing in some cases their death. The purpose of this research was to detect, from hyperspectral images, the individuals of the Araucaria species (Araucaria araucana (Molina and K. Koch)) and its degree of disease, by isolating its spectral signature and evaluating its physiological state through indices of vegetation and positioning techniques of the inflection point of the red edge, in a sector of the Ralco National Reserve, Biobío Region, Chile. Seven images were captured with the HYSPEX VNIR-1600 hyperspectral sensor, with 160 bands and a random sampling was carried out in the study area, where 90 samples of Araucarias were collected. In addition, from the remote sensing techniques applied, spatial data mining was used, in which Araucarias were classified without symptoms of disease and with symptoms of disease. A 55.11% overall accuracy was obtained in the classification of the image, 53.4% in the identification of healthy Araucaria and 55.96% in the identification of affected Araucaria. In relation to the evaluation of their sanitary status, the index with the best percentage of accuracy is the MSR (70.73%) and the one with the lowest value is the SAVI (35.47%). The positioning technique of the inflection point of the red edge delivered an accuracy percentage of 52.18% and an acceptable Kappa index.[ES] La Araucaria araucana es una especie endémica de Chile y Argentina, presenta un alto valor biológico, científico, cultural y desde el año 2016 ha evidenciado una severa afección del daño foliar en algunos individuos, causando en ciertos casos su muerte. Esta investigación tiene por objetivo detectar a partir de imágenes hiperespectrales, los individuos de la especie Araucaria (Araucaria araucana (Molina y K. Koch)) y su grado de afección, mediante el aislamiento de su firma espectral y la evaluación de su estado sanitario mediante índices de vegetación y técnicas de posicionamiento del punto de inflexión del red edge, en un sector de la Reserva Nacional Ralco, Región del Biobío, Chile. Se capturaron siete imágenes con el sensor hiperespectral HYSPEX VNIR-1600, con 160 bandas y se realizó un muestreo aleatorio en el área de estudio, donde se recolectaron 90 muestras de Araucarias. Además, de las técnicas de teledetección aplicadas, se utilizó minería de datos espaciales, que permitió clasificar las Araucarias con y sin síntomas de afección. Se logró un 55,11% de exactitud global en la clasificación de la imagen, un 53,4% en la identificación de Araucarias sanas y un 55,96% en la identificación de Araucarias afectadas. En relación a la evaluación de su estado sanitario, el índice con mejor porcentaje de exactitud es el MSR (70,73%) y el con menor porcentaje de exactitud es el SAVI (35,47%). La técnica de posicionamiento del punto de inflexión del red edge entregó un porcentaje de exactitud de 52,18% y un índice de Kappa aceptable.Este artículo se ha realizado en el contexto de fin de grado del Magíster en Teledetección, Facultad de Ciencias de la Universidad Mayor y en el mar-co del Proyecto “Prospección fitosanitaria para determinar los niveles de afección de daño foliar en bosques de Araucaria araucana de las regiones del Biobío, Araucanía y Los Ríos, 2017/ID: 633-32-LE16, financiado por la Corporación Nacional Forestal (CONAF) de Chile. La autora principal agradece a la Universidad Mayor por la oportuni-dad de desarrollar esta investigación; en especial a Idania Briceño por sus valiosos comentarios y Waldo Pérez, por su apoyo en las campañas de terreno.Medina, N.; Vidal, P.; Cifuentes, R.; Torralba, J.; Keusch, F. (2018). Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales. Revista de Teledetección. (52):41-53. https://doi.org/10.4995/raet.2018.10916SWORD415352Adamczyk, J., Osberger, A. 2015. Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests. International Journal of Applied Earth Observation and Geoinformation, 37, 90-99. https://doi.org/10.1016/j.jag.2014.10.013Alonzo, M., Bookhagen, B., Roberts, D. A. 2014. Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment, 148, 70-83. https://doi.org/10.1016/J.RSE.2014.03.018Ángel, Y. 2012. Metodología para identificar cultivos de coca mediante análisis de parámetros red edge y espectroscopia de imágenes. Tesis magister, Universidad Nacional de Colombia, Colombia.Armesto, J., Villagrán, C., Arroyo, M. 1996. Ecología de los bosques nativos de Chile (Vol. 1). Santiago de Chile: Editorial Universitaria.Awad, M. M. 2018. Forest mapping: a comparison between hyperspectral and multispectral images and technologies. Journal of Forestry Research, 29(5), 1395-1405 https://doi.org/10.1007/s11676-017- 0528-yBaldeck, C. A., Asner, G. P., Martin, R. E., Anderson, C. B., Knapp, D. E., Kellner, J. R., Wright, S. J. 2015. Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy. PLOS ONE, 10(7), e0118403. https://doi.org/10.1371/journal.pone.0118403Birth, G., McVey, G. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6), 640-643. https://doi. org/10.2134/agronj1968.00021962006000060016xBorràs, J., Delegido, J., Pezzola, A., Pereira, M., Morassi, G., Camps-Valls, G. 2017. Clasificación de usos del suelo a partir de imágenes Sentinel-2. Revista de Teledetección, 48, 55-66. https://doi.org/10.4995/raet.2017.7133Centro del Clima y la Resiliencia (CR2). 2018. Explorador Climático. http://explorador.cr2.cl/ Último acceso: 28 de noviembre, 2018.Chen, J. M. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3), 229-242. https://doi.org/10.1080/07038992.1996.10855178Cho, M. A., Skidmore, A. K. 2006. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote sensing of environment, 101(2), 181-193. https://doi.org/10.1016/j.rse.2005.12.011Cho, M. A., Debba, P., Mutanga, O., Dudeni-Tlhone, N., Magadla, T., Khuluse, S. A. 2012. Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health. International Journal of Applied Earth Observation and Geoinformation, 16, 85-93.Clark, M. L., Roberts, D. A. 2012. Species-Level Differences in Hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based Classifier. Remote Sensing, 4(6), 1820-1855. https:// doi.org/10.3390/rs4061820CONAF (Corporación Nacional Forestal, CL). 2008. Catastro de los Recursos Vegetacionales Nativos de Chile, Región del Bíobio, Chile.Dalponte, M., Bruzzone, L., Gianelle, D. 2012. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sensing of Environment, 123, 258-270. https://doi.org/10.1016/J.RSE.2012.03.013Dalponte, M., Orka, H. O., Gobakken, T., Gianelle, D., Naesset, E. 2013. Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51(5), 2632- 2645. https://doi.org/10.1109/TGRS.2012.2216272Dawson, T. P., Curran, P. J. 1998. A new technique for interpolating red edge position. International Journal of Remote Sensing, 19(11), 2133−2139.https://doi. org/10.1080/014311698214910Drake, F. 2004. Uso sostenible en bosques de Araucaria araucana (Mol.) K. Koch; aplicación de modelos de gestión. Tesis doctoral, Escuela Técnica Superior de Ingenieros Agrónomos y de Montes, Universidad de Córdoba, Córdoba, España.Fassnacht, F. E., Latifi, H., Ghosh, A., Joshi, P. K., Koch, B. 2014. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sensing of Environment, 140, 533-548.https:// doi.org/10.1016/j.rse.2013.09.014Fassnacht, F. E., Stenzel, S., Gitelson, A. A. 2015. Non-destructive estimation of foliar carotenoid content of tree species using merged vegetation indices. Journal of Plant Physiology, 176, 210-217. https://doi.org/10.1016/J.JPLPH.2014.11.003Gholizadeh, A., Mišurec, J., Kopačková, V., Mielke, C., Rogass, C. 2016. Assessment of Red-Edge Position Extraction Techniques: A Case Study for Norway Spruce Forests Using HyMap and Simulated Sentinel-2 Data. Forests, 7(226), 1-17. https://doi.org/10.3390/f7100226Guyot, G., Baret, F., Major, D. 1988. High spectral resolution: Determination of spectral shifts between the red and the near infrared. International Archives of Photogrammetry and Remote Sensing, 11(750-760).Hakkenberg, C. R., Peet, R. K., Urban, D. L., Song, C. 2018. Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing. Ecological Applications, 28(1), 177- 190. https://doi.org/10.1002/eap.1638Hall, M. A. 1998. Correlation-based feature subset selection for machine learning. Thesis degree of doctor, University of Waikato, New Zealand.Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W. 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment, 158, 220-234. https://doi.org/10.1016/j.rse.2014.11.005Horler, D., Dockray, M., Barber, J. 1983. The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2), 273-288. https://doi.org/10.1080/01431168308948546Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295- 309. https://doi.org/10.1016/0034-4257(88)90106-XJeffrey, A. 1985. Mathematics for Engineers and Scientists. Wokingham, UK: Van Nostrand Reinhold.Kemerer, A., Mari, N., Di Bella, C., Rebella, C. 2008. Comparación de técnicas de clasificación de cultivos a partir de información Multi E Hyperespectral. Revista de Teledetección, 29, 67-72. Accesible en: http:// www.aet.org.es/revistas/revista29/Revista-AET-29-7. pdf Último acceso: 28 de noviembre, 2018.Kokaly, R., Despain, D., Clark, R., Livo, K. 2003. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote sensing of environment, 84(3), 437-456. https://doi.org/10.1016/S0034-4257(02)00133-5Landis, J., Koch, G. 1977. The measurement of observeragreement for categorical data. Biometrics. 33, 159-174. https://doi.org/10.2307/2529310Liang S. 2005. Quantitative Remote Sensing of Land Surfaces. New Jersey, A John Wiley & Sons.Liu, L., Coops, N. C., Aven, N. W, Pang, Y. 2017. Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data. Remote Sensing of Environment, 200, 170-182. https://doi.org/10.1016/J.RSE.2017.08.010Melendo-Vega, J. R., Martín, M. P., Vilar del Hoyo, L., Pacheco-Labrador, J., Echavarría, P., Martínez-Vega, J. 2017. Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectroradiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección, 48, 13-28. https://doi.org/10.4995/raet.2017.7481Ministerio del Medio Ambiente. 2008. Ficha de especie: Araucaria araucana (Molina) K. Koch. Inventario nacional de especies de Chile. http://especies. mma.gob.cl/CNMWeb/Web/WebCiudadana/ficha_ indepen.aspx?EspecieId=240&Version=1 Último acceso:20 de Mayo, 2017.Naidoo, L., Cho, M. A., Mathieu, R., Asner, G. 2012. Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 167-179. https://doi.org/10.1016/J.ISPRSJPRS.2012.03.005Ojeda, N., Sandoval, V., Soto, H., Casanova, J., Herrera, M., Morales, L., Espinosa, A., San Martín, J. 2011. Discriminación de bosques de Araucaria araucana en el Parque Nacional Conguillío, centro-sur de Chile, mediante datos Landsat TM. Bosque (Valdivia), 32(2), 113-125. https://doi.org/10.4067/S0717-92002011000200002Peñuelas, J., Filella, I., Biel, C., Serrano, L., Save, R. 1993. The reflectance at the 950-970 nm region as an indicator of plant water status. International journal of remote sensing, 14(10), 1887-1905. https://doi.org/10.1080/01431169308954010Premoli, A., Quiroga, P., Gardner, M. 2013. Araucaria araucana. The IUCN Red List of Threatened Species 2013: e.T31355A2805113. Último acceso: 15 de Marzo, 2017, de https://doi.org/10.2305/IUCN. UK.2013-1.RLTS.T31355A2805113.enRoig, M. 2010. Identificación y clasificación de formaciones forestales mediante imágenes hiperespectrales aéreas. Tesis Escuela de ingeniería forestal. Universidad Mayor de Chile, 76 p.Roujean, J., Breon, M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote sensing of Environment, 51(3), 375-384. https://doi.org/10.1016/0034- 4257(94)00114-3Rouse, W., Haas, H., Schell, J., Deering, D. 1974. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA SP-351 I: 309-317.Shafri, H., Hamdan, N. 2009. Hyperspectral Imagery for Mapping Disease Infection in Oil Palm Plantation Using Vegetation Indices and Red Edge Techniques. American Journal of Applied Sciences, 6(6), 1031. https://doi.org/10.3844/ajassp.2009.1031.1035Shafri, H., Salleh, M., Ghiyamat, A. 2006. Hyperspectral remote sensing of vegetation using red edge position techniques. American Journal of Applied Sciences, 3(6), 1864-1871. https://doi.org/10.3844/ajassp.2006.1864.1871Shi, Y., Skidmore, A. K., Wang, T., Holzwarth, S., Heiden, U., Pinnel, N., Zhu, X., Heurich, M. 2018. Tree species classification using plant functional traits from LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 73, 207-219. https://doi.org/10.1016/J.JAG.2018.06.018Sims, D., Gamon, J. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote sensing of environment, 81(2), 337-354. https://doi.org/10.1016/S0034-4257(02)00010-XSmith, K., Steven, M., Colls, J. 2004. Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote sensing of environment, 92(2), 207-217. https://doi.org/10.1016/j.rse.2004.06.002Somers, B., Verbesselt, J., Ampe, E. M., Sims, N., Verstraeten, W. W., Coppin, P. 2010. Spectral mixture analysis to monitor defoliation in mixedaged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO1Hyperion data. International Journal of Applied Earth Observation and Geoinformation, 12(4), 270- 277. https://doi.org/10.1016/J.JAG.2010.03.005Torralba, J. 2012. Generación de algoritmo para la identificación de alerce (Fitzroya cupressoides) mediante análisis de imágenes hiperespectrales en el lago Tagua-Tagua, X Región, Chile. Proyecto final de Grado en Ingeniería Forestal y del Medio Natural, Universidad Castilla-La Mancha, 95 p.Vogelmann, J., Rock, B., Moss, D. 1993. Red edge spectral measurements from sugar maple leaves. Remote sensing, 14(8), 1563-1575. https://doi. org/10.1080/01431169308953986Willis, K. 2015. Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233-242. https://doi.org/10.1016/j.biocon.2014.12.006Wright, C., Gallant, A. 2007. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment, 107(4), 582-605. https://doi.org/10.1016/j.rse.2006.10.019Zarco-Tejada, P. J., Hornero, A., Hernández-Clemente, R., Beck, P. S. A. 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 134- 148. https://doi.org/10.1016/j.isprsjprs.2018.01.01

Effect of nocturnal oxygen and acetazolamide on exercise performance in patients with pre-capillary pulmonary hypertension and sleep-disturbed breathing: randomized, double-blind, cross-over trial

Aim Sleep-disturbed breathing (SDB) is common in pre-capillary pulmonary hypertension (PH) and impairs daytime performance. In lack of proven effective treatments, we tested whether nocturnal oxygen therapy (NOT) or acetazolamide improve exercise performance and quality of life in patients with pre-capillary PH and SDB. Methods This was a randomized, placebo-controlled, double-blind, three period cross-over trial. Participants received NOT (3 L/min), acetazolamide tablets (2 × 250 mg), and sham-NOT/placebo tablets each during 1 week with 1-week washout between treatment periods. Twenty-three patients, 16 with pulmonary arterial PH, 7 with chronic thromboembolic PH, and with SDB defined as mean nocturnal oxygen saturation 10 h−1 with daytime PaO2 ≥7.3 kPa participated. Assessments at the end of the treatment periods included a 6 min walk distance (MWD), SF-36 quality of life, polysomnography, and echocardiography. Results Medians (quartiles) of the 6 MWD after NOT, acetazolamide, and placebo were 480 m (390;528), 440 m (368;468), and 454 m (367;510), respectively, mean differences: NOT vs. placebo +25 m (95% CI 3-46, P= 0.027), acetazolamide vs. placebo −9 m (−34-17, P = 0.223), and NOT vs. acetazolamide +33 (12-45, P < 0.001). SF-36 quality of life was similar with all treatments. Nocturnal oxygen saturation significantly improved with both NOT and acetazolamide. Right ventricular fractional area change was greater on NOT compared with placebo (P = 0.042) and acetazolamide (P = 0.027). Conclusions In patients with pre-capillary PH and SDB on optimized pharmacological therapy, NOT improved the 6 MWD compared with placebo already after 1 week along with improvements in SDB and haemodynamics. ClinicalTrials.gov NTC0142719

Understanding Society Innovation Panel Wave 8: results from methodological experiments

This paper presents some preliminary findings from Wave 8 of the Innovation Panel (IP8) of Understanding Society: The UK Household Longitudinal Study. Understanding Society is a major panel survey in the UK. May 2015, the eighth wave of the Innovation Panel went into the field. IP8 used a mixed-mode design, using on-line interviews and face-to-face interviews. This paper describes the design of IP8, the experiments carried and the preliminary findings from early analysis of the data

Using interpretative phenomenological analysis to inform physiotherapy practice: An introduction with reference to the lived experience of cerebellar ataxia

The attached file is a pre-published version of the full and final paper which can be found at the link below.This article has been made available through the Brunel Open Access Publishing Fund.Qualitative research methods that focus on the lived experience of people with health conditions are relatively underutilised in physiotherapy research. This article aims to introduce interpretative phenomenological analysis (IPA), a research methodology oriented toward exploring and understanding the experience of a particular phenomenon (e.g., living with spinal cord injury or chronic pain, or being the carer of someone with a particular health condition). Researchers using IPA try to find out how people make sense of their experiences and the meanings they attach to them. The findings from IPA research are highly nuanced and offer a fine grained understanding that can be used to contextualise existing quantitative research, to inform understanding of novel or underresearched topics or, in their own right, to provoke a reappraisal of what is considered known about a specified phenomenon. We advocate IPA as a useful and accessible approach to qualitative research that can be used in the clinical setting to inform physiotherapy practice and the development of services from the perspective of individuals with particular health conditions.This article is available through the Brunel Open Access Publishing Fund

Measuring discomfort from glare: recommendations for good practice

This article presents a review of the methods used for subjective evaluation of discomfort from glare, focusing on the two procedures most frequently used in past research – adjustment and category rating. Evidence is presented to demonstrate that some aspects of these procedures influence the evaluation, such as the range of glare source luminances available in an adjustment procedure, leading to biased evaluations and which hence reduce the reliability and validity of the data. The article offers recommendations for good practice when using these procedures and also suggests alternative methods that might be explored in further work

IgG1 Fc N-glycan galactosylation as a biomarker for immune activation.

Immunoglobulin G (IgG) Fc N-glycosylation affects antibody-mediated effector functions and varies with inflammation rooted in both communicable and non-communicable diseases. Worldwide, communicable and non-communicable diseases tend to segregate geographically. Therefore, we studied whether IgG Fc N-glycosylation varies in populations with different environmental exposures in different parts of the world. IgG Fc N-glycosylation was analysed in serum/plasma of 700 school-age children from different communities of Gabon, Ghana, Ecuador, the Netherlands and Germany. IgG1 galactosylation levels were generally higher in more affluent countries and in more urban communities. High IgG1 galactosylation levels correlated with low total IgE levels, low C-reactive protein levels and low prevalence of parasitic infections. Linear mixed modelling showed that only positivity for parasitic infections was a significant predictor of reduced IgG1 galactosylation levels. That IgG1 galactosylation is a predictor of immune activation is supported by the observation that asthmatic children seemed to have reduced IgG1 galactosylation levels as well. This indicates that IgG1 galactosylation levels could be used as a biomarker for immune activation of populations, providing a valuable tool for studies examining the epidemiological transition from communicable to non-communicable diseases

Ingestion of micronutrient fortified breakfast cereal has no influence on immune function in healthy children: A randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>This study investigated the influence of 2-months ingestion of an "immune" nutrient fortified breakfast cereal on immune function and upper respiratory tract infection (URTI) in healthy children during the winter season.</p> <p>Methods</p> <p>Subjects included 73 children (N = 42 males, N = 31 females) ranging in age from 7 to 13 years (mean ± SD age, 9.9 ± 1.7 years), and 65 completed all phases of the study. Subjects were randomized to one of three groups--low, moderate, or high fortification--with breakfast cereals administered in double blinded fashion. The "medium" fortified cereal contained B-complex vitamins, vitamins A and C, iron, zinc, and calcium, with the addition of vitamin E and higher amounts of vitamins A and C, and zinc in the "high" group. Immune measures included delayed-typed hypersensitivity, global IgG antibody response over four weeks to pneumococcal vaccination, salivary IgA concentration, natural killer cell activity, and granulocyte phagocytosis and oxidative burst activity. Subjects under parental supervision filled in a daily log using URTI symptoms codes.</p> <p>Results</p> <p>Subjects ingested 3337 ± 851 g cereal during the 2-month study, which represented 14% of total diet energy intake and 20-85% of selected vitamins and minerals. Despite significant increases in nutrient intake, URTI rates and pre- to- post-study changes in all immune function measures did not differ between groups.</p> <p>Conclusions</p> <p>Data from this study indicate that ingestion of breakfast cereal fortified with a micronutrient blend for two winter months by healthy, growing children does not significantly influence biomarkers for immune function or URTI rates.</p