High-energy particle transport in 3D hydrodynamic models of colliding-wind binaries

Massive stars in binary systems (as WR140, WR147 or $\eta$ Carinae) have long been regarded as potential sources of high-energy $\gamma$-rays. The emission is thought to arise in the region where the stellar winds collide and produce relativistic particles which subsequently might be able to emit $\gamma$-rays. Detailed numerical hydrodynamic simulations have already offered insight in the complex dynamics of the wind collision region (WCR), while independent analytical studies, albeit with simplified descriptions of the WCR, have shed light on the spectra of charged particles. In this paper, we describe a combination of these two approaches. We present a 3D-hydrodynamical model for colliding stellar winds and compute spectral energy distributions of relativistic particles for the resulting structure of the WCR. The hydrodynamic part of our model incorporates the line-driven acceleration of the winds, gravity, orbital motion and the radiative cooling of the shocked plasma. In our treatment of charged particles we consider diffusive shock acceleration in the WCR and the subsequent cooling via inverse Compton losses (including Klein-Nishina effects), bremsstrahlung, collisions and other energy loss mechanisms.Comment: 28 pages, 9 figures / accepted for publication in The Astrophysical Journa

PDB3: USE OF BOOTSTRAP IN A COST-OF-ILLNESS STUDY TO DERIVE ACCURACY OF ESTIMATES

Farrando, Jordi;Febles, Maria Dolors ;Henrich, Jordi;Tarrasó, Olga ;Fuertes, J.C.;Pérez, S

Thermoforming of planar polymer optical waveguides for integrated optics in smart packaging materials

The innovations in smart packaging will open up a wide range of opportunities in the future. This work describes the processing of additive manufactured and planar integrated polymer optical waveguides for use in smart packaging products. The previously published combination of flexographic and Aerosol Jet printing is complemented by thermoforming and thus creates three-dimensional integrated multimode waveguides with optical attenuation of 1.9 dB/cm ± 0.1 dB/cm @ 638 nm. These properties will be the basis to develop smart applications in packaging materials

Gamma-ray follow-up studies on Eta Carinae

Observations of high energy gamma rays recently revealed a persistent source in spatial coincidence with the binary system Eta Carinae. Since modulation of the observed gamma-ray flux on orbital time scales has not been reported so far, an unambiguous identification was hitherto not possible. Particularly the observations made by the Fermi Large Area Telescope (LAT) posed additional questions regarding the actual emission scenario owing to the existence of two energetically distinct components in the gamma-ray spectrum of this source, best described by an exponentially cutoff power-law function (CPL) at energies below 10 GeV and a power-law (PL) component dominant at higher energies. The increased exposure in conjunction with the improved instrumental response functions of the LAT now allow us to perform a more detailed investigation of location, spectral shape, and flux time history of the observed gamma-ray emission. For the first time, we are able to report a weak but regular flux decrease over time. This can be understood and interpreted in a colliding-wind binary scenario for orbital modulation of the gamma-ray emission. We find the spectral shape of the gamma-ray signal in agreement with a single emitting particle population in combination with significant absorption by gamma-gamma pair production. Studying the correlation of the flux decrease with the orbital separation of the binary components allows us to predict the behaviour up to the next periastron passage in 2014.Comment: 11 pages, 9 figure

Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation.Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086S224233232Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94(4), 441-449. doi:10.1016/j.rse.2004.10.013Arroyo, L. A., Pascual, C., & Manzanera, J. A. (2008). Fire models and methods to map fuel types: The role of remote sensing. Forest Ecology and Management, 256(6), 1239-1252. doi:10.1016/j.foreco.2008.06.048Ashworth, A., Evans, D. L., Cooke, W. H., Londo, A., Collins, C., & Neuenschwander, A. (2010). Predicting Southeastern Forest Canopy Heights and Fire Fuel Models using GLAS Data. Photogrammetric Engineering & Remote Sensing, 76(8), 915-922. doi:10.14358/pers.76.8.915Buddenbaum, H., Seeling, S., & Hill, J. (2013). Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands. International Journal of Remote Sensing, 34(13), 4511-4524. doi:10.1080/01431161.2013.776721Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29(2), 147-159. doi:10.1016/0034-4257(89)90023-0CHUVIECO, E., & SALAS, J. (1996). Mapping the spatial distribution of forest fire danger using GIS. International journal of geographical information systems, 10(3), 333-345. doi:10.1080/02693799608902082Chuvieco, E., Riaño, D., Aguado, I., & Cocero, D. (2002). Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. International Journal of Remote Sensing, 23(11), 2145-2162. doi:10.1080/01431160110069818Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martı́nez-Vega, J., de la Riva, J., & Pérez, F. (2004). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92(3), 322-331. doi:10.1016/j.rse.2004.01.019Cruz, M. G., Alexander, M. E., & Wakimoto, R. H. (2003). Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire, 12(1), 39. doi:10.1071/wf02024Drake, J. B., Dubayah, R. O., Clark, D. B., Knox, R. G., Blair, J. B., Hofton, M. A., … Prince, S. (2002). Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79(2-3), 305-319. doi:10.1016/s0034-4257(01)00281-4Erdody, T. L., & Moskal, L. M. (2010). Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment, 114(4), 725-737. doi:10.1016/j.rse.2009.11.002Falkowski, M. J., Gessler, P. E., Morgan, P., Hudak, A. T., & Smith, A. M. S. (2005). Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. Forest Ecology and Management, 217(2-3), 129-146. doi:10.1016/j.foreco.2005.06.013Flannigan, M. ., Stocks, B. ., & Wotton, B. . (2000). Climate change and forest fires. Science of The Total Environment, 262(3), 221-229. doi:10.1016/s0048-9697(00)00524-6García, M., Popescu, S., Riaño, D., Zhao, K., Neuenschwander, A., Agca, M., & Chuvieco, E. (2012). Characterization of canopy fuels using ICESat/GLAS data. Remote Sensing of Environment, 123, 81-89. doi:10.1016/j.rse.2012.03.018González-Olabarria, J.-R., Rodríguez, F., Fernández-Landa, A., & Mola-Yudego, B. (2012). Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements. Forest Ecology and Management, 282, 149-156. doi:10.1016/j.foreco.2012.06.056Hall, S. A., Burke, I. C., Box, D. O., Kaufmann, M. R., & Stoker, J. M. (2005). Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208(1-3), 189-209. doi:10.1016/j.foreco.2004.12.001Harding, D. J. (2005). ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophysical Research Letters, 32(21). doi:10.1029/2005gl023471Heinzel, J., & Koch, B. (2011). Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 13(1), 152-160. doi:10.1016/j.jag.2010.09.010Höfle, B., Hollaus, M., & Hagenauer, J. (2012). Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 134-147. doi:10.1016/j.isprsjprs.2011.12.003HYDE, P., DUBAYAH, R., PETERSON, B., BLAIR, J., HOFTON, M., HUNSAKER, C., … WALKER, W. (2005). Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems. Remote Sensing of Environment, 96(3-4), 427-437. doi:10.1016/j.rse.2005.03.005Keane, R. E., Burgan, R., & van Wagtendonk, J. (2001). Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire, 10(4), 301. doi:10.1071/wf01028Kim, Y., Yang, Z., Cohen, W. B., Pflugmacher, D., Lauver, C. L., & Vankat, J. L. (2009). Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment, 113(11), 2499-2510. doi:10.1016/j.rse.2009.07.010Koetz, B., Morsdorf, F., Sun, G., Ranson, K. J., Itten, K., & Allgower, B. (2006). Inversion of a Lidar Waveform Model for Forest Biophysical Parameter Estimation. IEEE Geoscience and Remote Sensing Letters, 3(1), 49-53. doi:10.1109/lgrs.2005.856706Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests. Remote Sensing of Environment, 70(3), 339-361. doi:10.1016/s0034-4257(99)00052-8Listopad, C. M. C. S., Drake, J. B., Masters, R. E., & Weishampel, J. F. (2011). Portable and Airborne Small Footprint LiDAR: Forest Canopy Structure Estimation of Fire Managed Plots. Remote Sensing, 3(7), 1284-1307. doi:10.3390/rs3071284Mallet, C., & Bretar, F. (2009). Full-waveform topographic lidar: State-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 1-16. doi:10.1016/j.isprsjprs.2008.09.007Morsdorf, F., Meier, E., Kötz, B., Itten, K. I., Dobbertin, M., & Allgöwer, B. (2004). LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sensing of Environment, 92(3), 353-362. doi:10.1016/j.rse.2004.05.013Neuenschwander, A. L. (2009). Landcover classification of small-footprint, full-waveform lidar data. Journal of Applied Remote Sensing, 3(1), 033544. doi:10.1117/1.3229944Reich, R. M., Lundquist, J. E., & Bravo, V. A. (2004). Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. International Journal of Wildland Fire, 13(1), 119. doi:10.1071/wf02049Reitberger, J., Krzystek, P., & Stilla, U. (2008). Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing, 29(5), 1407-1431. doi:10.1080/01431160701736448Riaño, D., Chuvieco, E., Salas, J., Palacios-Orueta, A., & Bastarrika, A. (2002). Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems. Canadian Journal of Forest Research, 32(8), 1301-1315. doi:10.1139/x02-052Riaño, D. (2003). Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sensing of Environment, 86(2), 177-186. doi:10.1016/s0034-4257(03)00098-1Riaño, D., Chuvieco, E., Condés, S., González-Matesanz, J., & Ustin, S. L. (2004). Generation of crown bulk density for Pinus sylvestris L. from lidar. Remote Sensing of Environment, 92(3), 345-352. doi:10.1016/j.rse.2003.12.014Riaño, D., Chuvieco, E., Ustin, S. L., Salas, J., Rodríguez-Pérez, J. R., Ribeiro, L. M., … Fernández, H. (2007). Estimation of shrub height for fuel-type mapping combining airborne LiDAR and simultaneous color infrared ortho imaging. International Journal of Wildland Fire, 16(3), 341. doi:10.1071/wf06003SKOWRONSKI, N., CLARK, K., NELSON, R., HOM, J., & PATTERSON, M. (2007). Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey. Remote Sensing of Environment, 108(2), 123-129. doi:10.1016/j.rse.2006.09.032Skowronski, N. S., Clark, K. L., Duveneck, M., & Hom, J. (2011). Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems. Remote Sensing of Environment, 115(2), 703-714. doi:10.1016/j.rse.2010.10.012Van Leeuwen, M., & Nieuwenhuis, M. (2010). Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129(4), 749-770. doi:10.1007/s10342-010-0381-4Vaughn, N. R., Moskal, L. M., & Turnblom, E. C. (2012). Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar. Remote Sensing, 4(2), 377-403. doi:10.3390/rs4020377Wagner, W., Hollaus, M., Briese, C., & Ducic, V. (2008). 3D vegetation mapping using small‐footprint full‐waveform airborne laser scanners. International Journal of Remote Sensing, 29(5), 1433-1452. doi:10.1080/01431160701736398Wilson, B. A., Ow, C. F. Y., Heathcott, M., Milne, D., McCaffrey, T. M., Ghitter, G., & Franklin, S. E. (1994). Landsat MSS Classification of Fire Fuel Types in Wood Buffalo National Park, Northern Canada. Global Ecology and Biogeography Letters, 4(2), 33. doi:10.2307/2997751Zhao, K., Popescu, S., Meng, X., Pang, Y., & Agca, M. (2011). Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment, 115(8), 1978-1996. doi:10.1016/j.rse.2011.04.00

Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables

The quantification of forest ecosystems is important for a variety of purposes, including the assessment of wildlife habitat, nutrient cycles, timber yield and fire propagation. This research assesses the estimation of forest structure, composition and deadwood variables from small-footprint airborne lidar data, both discrete return (DR) and full waveform (FW), acquired under leaf-on and leaf-off conditions. The field site, in the New Forest, UK, includes managed plantation and ancient, semi-natural, coniferous and deciduous woodland. Point clouds were rendered from the FW data through Gaussian decomposition. An area-based regression approach (using Akaike Information Criterion analysis) was employed, separately for the DR and FW data, to model 23 field-measured forest variables. A combination of plot-level height, intensity/amplitude and echo-width variables (the latter for FW lidar only) generated from both leaf-on and leaf-off point cloud data were utilised, together with individual tree crown (ITC) metrics from rasterised leaf-on height data. Statistically significant predictive models (p<0.05) were generated for all 23 forest metrics using both the DR and FW lidar datasets, with R2 values for the best fit models in the range R2=0.43-0.94 for the DR data and R2=0.28-0.97 for the FW data (with normalised RMSE values being 18%-66% and 16%-48% respectively). For all but two forest metrics the difference between the NRMSE of the best performing DR and FW models was ≤7%, and there was an even split (11:12) as to which lidar dataset (DR or FW) generated the best model per forest metric. Overall, the DR data performed better at modelling structure variables, whilst the FW data performed better at modelling composition and deadwood variables. Neither showed a clear advantage at modelling variables from a particular vegetation layer (canopy, shrub or ground). Height, intensity/amplitude, and ITC-derived crown variables were shown to be important inputs across the best performing models (DR or FW), but the additional echo-width variables available from FW point data were relatively unimportant. Of perhaps greater significance to the choice between lidar data type (i.e. DR or FW) in determining the predictive power of the best performing models was the selection of leaf-on and/or leaf-off data. Of the 23 best models, 10 contained both leaf-on and leaf-off lidar variables, whilst 11 contained only leaf-on and two only leaf-off data. We therefore conclude that although FW lidar has greater vertical profile information than DR lidar, the greater complimentary information about the entire forest canopy profile that is available from both leaf-on and leaf-off data is of more benefit to forest inventory, in general, than the selection between DR or FW lidar

Aggressive PDACs show hypomethylation of repetitive elements and the execution of an intrinsic IFN program linked to a ductal cell of origin

Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive desmoplasia, which challenges the molecular analyses of bulk tumor samples. Here we FACS-purified epithelial cells from human PDAC and normal pancreas and derived their genome-wide transcriptome and DNA methylome landscapes. Clustering based on DNA methylation revealed two distinct PDAC groups displaying different methylation patterns at regions encoding repeat elements. Methylation(low) tumors are characterized by higher expression of endogenous retroviral (ERV) transcripts and dsRNA sensors which leads to a cell intrinsic activation of an interferon signature (IFNsign). This results in a pro-tumorigenic microenvironment and poor patient outcome. Methylation(low)/IFNsign(high) and Methylation(high)/IFNsign(low) PDAC cells preserve lineage traits, respective of normal ductal or acinar pancreatic cells. Moreover, ductal-derived Kras(G12D)/Trp53(−/−) mouse PDACs show higher expression of IFNsign compared to acinar-derived counterparts. Collectively, our data point to two different origins and etiologies of human PDACs, with the aggressive Methylation(low)/IFNsign(high) subtype potentially targetable by agents blocking intrinsic IFN-signaling