Galactic Cosmic Ray Origins and OB Associations: Evidence from SuperTIGER Observations of Elements 26_{26}Fe through 40_{40}Zr

We report abundances of elements from $_{26}$Fe to $_{40}$Zr in the cosmic radiation measured by the SuperTIGER (Trans-Iron Galactic Element Recorder) instrument during 55 days of exposure on a long-duration balloon flight over Antarctica. These observations resolve elemental abundances in this charge range with single-element resolution and good statistics. These results support a model of cosmic-ray origin in which the source material consists of a mixture of 19$^{+11}_{-6}$\% material from massive stars and $\sim$81\% normal interstellar medium (ISM) material with solar system abundances. The results also show a preferential acceleration of refractory elements (found in interstellar dust grains) by a factor of $\sim$4 over volatile elements (found in interstellar gas) ordered by atomic mass (A). Both the refractory and volatile elements show a mass-dependent enhancement with similar slopes.Comment: 9 pages, 12 figures, 2 tables, accepted by Ap

STEREO and ACE observations of CIR particles

In the present solar minimum, corotating interaction regions (CIRs) produce frequent particle enhancements at 1 AU as observed at STEREO and ACE. As the two STEREO spacecraft move apart, differences in CIR time profiles observed at each spacecraft are becoming large. The timing differences are often roughly similar to the corotation time lag between the two spacecraft, however many of the features seen at Ahead and Behind require more than just a time shift. Perhaps transient disturbances in the solar wind affect connection to or transport from the shock, or temporal changes occur in the CIR shock itself. Additional timing differences of >1 day result from the different heliographic latitudes of the two STEREO spacecraf

Evolución del comportamiento espectral y la composición química en el dosel arbóreo de una dehesa

Revista oficial de la Asociación Española de Teledetección[EN] In the context of the BIOSPEC and FLUXPEC projects (http://www.lineas.cchs.csic.es/fluxpec/), spectral and biophysical variables measurements at leaf level have been conducted in the tree canopy of a holm oak dehesa (Quercus ilex) ecosystem during four vegetative periods. Measurements of bi-conical reflectance factor of intact leaf (ASD Fieldspec 3® spectroradiometer), specific leaf mass (SLM), leaf water content (LWC), nutrient (N, P, K, Ca, Mg, Mn, Fe, and Zn) and chlorophyll concentration were performed. The spectral measurements have been related with the biophysical variables by stepwise and partial least squares regression analyses. These analyses allowed to identify the spectral bands and regions that best explain the evolution of the biophysical variables and to estimate the nutrient contents during the leaf maturation process. Statistically significant estimates of the majority of the variables studied were obtained. Wavelengths that had the highest contributions explaining the chemical composition of the forest canopy were located in spectral regions of the red edge, the green visible region, and the shortwave infrared.[ES] En el contexto de los proyectos BIOSPEC y FLUXPEC (http://www.lineas.cchs.csic.es/fluxpec/), se han rea-lizado mediciones espectrales y de variables biofísicas a nivel de hoja en el dosel arbóreo de una dehesa de encina (Quercus ilex) durante cuatro períodos vegetativos. Se han llevado a cabo mediciones de reflectividad bi-cónica de hoja intacta (ASD Fieldspec 3®spectroradiometer), masa foliar específica (SLM), contenido de agua (LWC), concen-traciones de nutrientes (N, P, K, Ca, Mg, Mn, Fe, y Zn) y clorofilas. Las mediciones espectrales se han relacionado con las variables biofísicas mediante análisis de regresión múltiple por pasos (SWR) y regresión de mínimos cuadrados parciales (PLSR). Estos análisis han permitido identificar las bandas y regiones espectrales que explican la evolución de las variables biofísicas y estimar los contenidos de nutrientes a lo largo del proceso de maduración de las hojas en la copa. Se han obtenido modelos estadísticamente significativos para la mayoría de las variables foliares estudiadas. Las longitudes de onda que aportan mayor información sobre la composición química del dosel, se encuentran en las regiones espectrales del límite del rojo, la región verde del visible y el infrarrojo medio de onda corta (SWIR).Este trabajo ha sido financiado por los proyectos BIOSPEC (CGL2008-02301/CLI, Ministerio de Ciencia e innovación) y FLUXPEC (CGL-2012 34383, Ministerio de Economía y Competitividad).González-Cascón, R.; Pacheco-Labrador, J.; Martín, MP. (2016). Evolution of spectral behavior and chemical composition in the tree canopy of a dehesa ecosystem. Revista de Teledetección. (46):31-43. https://doi.org/10.4995/raet.2016.5688SWORD31434

N-terminal acetylation promotes synaptonemal complex assembly in C. elegans

N-terminal acetylation of the first two amino acids on proteins is a prevalent cotranslational modification. Despite its abundance, the biological processes associated with this modification are not well understood. Here, we mapped the pattern of protein N-terminal acetylation in Caenorhabditis elegans, uncovering a conserved set of rules for this protein modification and identifying substrates for the N-terminal acetyltransferase B (NatB) complex. We observed an enrichment for global protein N-terminal acetylation and also specifically for NatB substrates in the nucleus, supporting the importance of this modification for regulating biological functions within this cellular compartment. Peptide profiling analysis provides evidence of cross-talk between N-terminal acetylation and internal modifications in a NAT substrate-specific manner. In vivo studies indicate that N-terminal acetylation is critical for meiosis, as it regulates the assembly of the synaptonemal complex (SC), a proteinaceous structure ubiquitously present during meiosis from yeast to humans. Specifically, N-terminal acetylation of NatB substrate SYP-1, an SC structural component, is critical for SC assembly. These findings provide novel insights into the biological functions of N-terminal acetylation and its essential role during meiosis

Estimation of grassland biophysical parameters in a “dehesa” ecosystem from field spectroscopy and airborne hyperspectral imagery

[EN] The aim of this paper is the estimation of biophysical vegetation parameters from its optical properties. The variables Fuel Moisture Content (FMC), Canopy Water Content (CWC), Leaf Area Index (LAI), dry matter (Cm) and AboveGround Biomass (AGB) were estimated in the laboratory from vegetation samples collected simultaneously with the acquisition of spectral data from the Compact Airborne Spectrographic Imager (CASI) sensor and the field spectroradiometer ASD FieldSpec® 3. Spectral vegetation indices found in the literature were computed from hyperspectral data. Their linear relationships with the biophysical variables measured in the field were analysed. Results show consistent relationships between analysed biophysical parameters and spectral indices, mainly those using SWIR and red-egde bands which reveal the importance of these spectral regions for the estimation of biophysical variables in herbaceous covers. Determination coefficients (R2) above 0.91 and RRMSE of 21.4% have been obtained for the spectral indexes calculated whit ASD data, and 0.91 R2 and RRMSE of 15.5% for the spectral indexes calculated whit CASI data.[ES] Este trabajo aborda la estimación de variables biofísicas de un pastizal de dehesa a partir de información óptica generada por sensores próximos y remotos. Las variables de contenido de humedad del combustible (FMC), contenido de agua del dosel (CWC), índice de área foliar (LAI), materia seca (Cm) y biomasa superficial (AGB) fueron estimadas en laboratorio a partir de muestras de vegetación tomadas simultáneamente a la adquisición de datos hiperespectrales del sensor Compact Airbone Spectrographic Imager (CASI) y del espectro-radiómetro de campo ASD FieldSpec®3. A partir de la información espectral se han calculado diversos índices extraídos de la literatura y se han analizado las relaciones lineales existentes con las variables biofísicas medidas en campo. Los resultados muestran relaciones consistentes entre las variables biofísicas y los índices espectrales, especialmente en el caso de los índices basados en bandas del infrarrojo medio de onda corta (SWIR) y del red-edge, poniendo de manifiesto la importancia de estas regiones en la estimación de variables biofísicas en cubiertas de pastizal. Se han obteniendo coeficientes de determinación (R2) superiores a 0,91 y un error cuadrático medio relativo (RRMSE) de 21,4%, para los índices espectra-les calculados con datos ASD; yR2 de 0,91 y RRMSE de 15,5% para los índices espectrales calculados con datos CASI.Este trabajo se ha realizado en el contexto de los proyectos BIOSPEC (CGL2008-02301/CLI) financiado por el Ministerio e Innovación y FLUχPEC (CGL2012-34383) financiado por el Ministerio de Economía y Competitividad. Agradecemos al Ministerio de Educación, Cultura y Deporte la financiación recibida a través del programa de becas FPU del investigador predoctoral José Ramón Melendo. Nuestro agradecimiento al personal de SpecLab-CSIC, Universidad de Alcalá e Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria que ha participado en la recogida y procesamiento de datos.Melendo-Vega, JR.; Martín, MP.; 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 espectro-radiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección. (48):13-28. https://doi.org/10.4995/raet.2017.7481SWORD132848Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337-352. doi:10.1016/j.rse.2003.12.013Hardisky, M.A., Klemas, V., Smart, R.M. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrametry Engineering and Remote Sensing, 49, 77-83Hernández-Clemente, R., Navarro-Cerrillo, R. M., Suárez, L., Morales, F., & Zarco-Tejada, P. J. (2011). Assessing structural effects on PRI for stress detection in conifer forests. Remote Sensing of Environment, 115(9), 2360-2375. doi:10.1016/j.rse.2011.04.036Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment, 115(8), 2141-2151. doi:10.1016/j.rse.2011.04.018Hilker, T., Coops, N. C., Hall, F. G., Black, T. A., Wulder, M. A., Nesic, Z., & Krishnan, P. (2008). Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sensing of Environment, 112(6), 2777-2788. doi:10.1016/j.rse.2008.01.011Hill, M.J., Hanan, N.P., Hoffmann, W., Scholes, R., Prince, S., Ferwerda, J., Lucas, R.M., Baker, I., Arneth, A., Higgings, S.I., Barret, D.J., Disney, M., Hutley, L. 2011. Remote sensing and modeling of savannas: The state of the dis-union. 34th International Symposium on Remote Sensing of Environment. Sydney, 1-6.HongRui, R., GuangSheng, Z., Feng, Z., XinShi, Z. 2011. Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of Inner Mongolia. Chinese Science Bulletin, 57, 1716-1722.Huete, A. . (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. doi:10.1016/0034-4257(88)90106-xKuusk, A. (1995). A fast, invertible canopy reflectance model. Remote Sensing of Environment, 51(3), 342-350. doi:10.1016/0034-4257(94)00059-vLee, K.-S., Cohen, W. B., Kennedy, R. E., Maiersperger, T. K., & Gower, S. T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91(3-4), 508-520. doi:10.1016/j.rse.2004.04.010Li, W., Niu, Z., Liang, X., Li, Z., Huang, N., Gao, S., … Muhammad, S. (2015). Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling. International Journal of Applied Earth Observation and Geoinformation, 41, 88-98. doi:10.1016/j.jag.2015.04.020Liu, J., Miller, J.R., Haboudane, D., Pattey, E. 2004. Exploring the relationship between red edge parameters and crop variables for precision agriculture. 2004 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Anchorage, 1276-1279.Mahalanobis, P.C. 1936. On the generalised distance in statistics. Proceedings National Institute of Science, India, 49-55Nagler, P. L., Inoue, Y., Glenn, E. ., Russ, A. ., & Daughtry, C. S. . (2003). Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sensing of Environment, 87(2-3), 310-325. doi:10.1016/j.rse.2003.06.001Pacheco-Labrador, J., González-Cascón, R., Martín, M. P., & Riaño, D. (2014). Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 26, 105-118. doi:10.1016/j.jag.2013.05.013Perez-Priego, O., Guan, J., Rossini, M., Fava, F., Wutzler, T., Moreno, G., … Migliavacca, M. (2015). Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem. Biogeosciences, 12(21), 6351-6367. doi:10.5194/bg-12-6351-2015Pinty, B., & Verstraete, M. M. (1992). GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101(1), 15-20. doi:10.1007/bf00031911Privette, J. ., Myneni, R. ., Knyazikhin, Y., Mukelabai, M., Roberts, G., Tian, Y., … Leblanc, S. . (2002). Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment, 83(1-2), 232-243. doi:10.1016/s0034-4257(02)00075-5Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126. doi:10.1016/0034-4257(94)90134-1Riano, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P. J., & Ustin, S. L. (2005). Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 819-826. doi:10.1109/tgrs.2005.843316Richter, K., Atzberger, C., Hank, T. B., & Mauser, W. (2012). Derivation of biophysical variables from Earth observation data: validation and statistical measures. Journal of Applied Remote Sensing, 6(1), 063557-1. doi:10.1117/1.jrs.6.063557Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring Vegetation Systems in the Great Plains whit ERTS. Proceeding, 3rd Earth Resource Technology Satellite (ERTS) Symposium, NASA, Washington DC, 1, 48-62SCHMIDTLEIN, S. (2004). Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sensing of Environment, 92(1), 126-138. doi:10.1016/j.rse.2004.05.004Serrano, L., Peñuelas, J., & Ustin, S. L. (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data. Remote Sensing of Environment, 81(2-3), 355-364. doi:10.1016/s0034-4257(02)00011-1SHAPIRO, S. S., & WILK, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591-611. doi:10.1093/biomet/52.3-4.591Smith, G. M., & Milton, E. J. (1999). The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing, 20(13), 2653-2662. doi:10.1080/014311699211994Wieneke, S., Ahrends, H., Damm, A., Pinto, F., Stadler, A., Rossini, M., & Rascher, U. (2016). 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Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1), 109-124. doi:10.1016/s0034-4257(02)00197-

Consejo genético y detección de vías moleculares en pacientes con cáncer hereditario

INTRODUCCIÓN: El cáncer de mama es la primera causa de muerte de la mujer en la Argentina, con una incidencia estimada de más de 19.000 casos nuevos por año. Dentro de estos, el tipo de cáncer hereditario más común es el de mama/ovario hereditario, provocado por mutaciones en los genes BRCA 1(Breast cáncer) y BRCA 2. A su vez el cáncer colorrectal es la segunda causa de muerte en Argentina, con una incidencia estimada de más de 11.000 casos nuevos por año. OBJETIVO: de la presente investigación es evaluar la utilidad de la realización de los estudios genéticos en personas con cáncer hereditario en el contexto del consejo genético, con un asesoramiento antes y después de realizarse la prueba genética POBLACIÓN Y MÉTODOS: Se estudiaron 34 mujeres con diagnóstico de cáncer de mama/ovario y 31 pacientes de ambos sexos con diagnóstico de cáncer colorrectal (CCR). En las mujeres se analizaron los genes BRCA 1 y 2 por secuenciación de próxima generación (NSG) y grandes rearreglos de los genes BRCA 1 y 2 por amplificación de sonda dependiente de la ligadura multiplex (MLPA). En las personas de ambos sexos se determinó la Inestabilidad de Microsatelites(IMS), el análisis de mismatch repair (MMR) por MLPA y la mutación del gen BRAF (Protooncogen B-Raf) RESULTADOS: Los resultados mostraron que las pacientes con cáncer de mama / ovario con antecedentes familiares tienen un alto porcentaje de BRCA negativo. En cuanto a los cambios fenotípicos, el más predominante en este estudio, fue el subtipo triple negativo y la paciente con BRCA 2 positivo presentó este fenotipo. Con respecto al estudio del cáncer de colon detectamos cuatro pacientes con IMS-alta y mutación del V600E del gen BRAF. Cuando se les realizó el análisis de MLPA en los genes MSH6, MLH1, MSH2 y PMS2 a los efectos de establecer la diferencia entre CCR y síndrome de Lynch, los resultados fueron negativos, por lo tanto, estos pacientes fueron diagnosticados como CCR esporádico. CONCLUSIONES: Como lo demuestra este trabajo, para el consejo genético, el estudio de vías moleculares en pacientes con cáncer hereditario es un instrumento de ayuda para la valoración del riesgo génico

Cosmic-Ray Positrons: Are There Primary Sources?

Cosmic rays at the Earth include a secondary component originating in collisions of primary particles with the diffuse interstellar gas. The secondary cosmic rays are relatively rare but carry important information on the Galactic propagation of the primary particles. The secondary component includes a small fraction of antimatter particles, positrons and antiprotons. In addition, positrons and antiprotons may also come from unusual sources and possibly provide insight into new physics. For instance, the annihilation of heavy supersymmetric dark matter particles within the Galactic halo could lead to positrons or antiprotons with distinctive energy signatures. With the High-Energy Antimatter Telescope (HEAT) balloon-borne instrument, we have measured the abundances of positrons and electrons at energies between 1 and 50 GeV. The data suggest that indeed a small additional antimatter component may be present that cannot be explained by a purely secondary production mechanism. Here we describe the signature of the effect and discuss its possible origin.Comment: 15 pages, Latex, epsfig and aasms4 macros required, to appear in Astroparticle Physics (1999

Estimation of real evapotranspiration (ETR) and potential evapotranspiration (ETP) in the southwest of the Buenos Aires Province (Argentina) using MODIS images

[EN] Using regression analysis between actual evapotranspiration (ETR) and potential evapotranspiration (ETP) values obtained in seven meteorological observatories and remote sensing derived data from MODIS images (Surface temperature and Normalized Difference Vegetation Index - NDVI) models for estimating ETR and ETP in the southwest of the Buenos Aires Province (Argentina) were developed for the 2000–2014 period. Both models were satisfactorily evaluated in the meteorological observatories used. A regression model was adjusted for ETR with a determination coefficient of 0,6959. Regression model was nonlinear in the case of the ETP variable with a determination coefficient of 0,8409. The individual regression analysis for each meteorological observatories explicate the behavior of the regression for the total data set of ETR and ETP. According to these results, the utility of remote sensing in determination of ETR and ETP in areas without meteorological data was confirmed.[ES] Se han elaborado modelos para el cálculo de evapotranspiración real (ETR) y de evapotranspiración poten-cial (ETP) en base a un análisis de regresión múltiple entre dichos parámetros estimados en siete estaciones meteoro-lógicas y dos variables derivadas de imágenes satelitales MODIS: Temperatura de Superficie (TS) e Índice Normalizado de Diferencia de Vegetación (Normalized Difference Vegetation Index -NDVI). Dichos modelos permitieron estimar ETR y ETP en el sudoeste de la provincia de Buenos Aires (Argentina) en base al análisis del período 2000/2014. Ambos fueron calibrados satisfactoriamente en cada una de las estaciones meteorológicas utilizadas. Se ajustó un modelo de regresión múltiple lineal a la variable ETR, con un coeficiente de determinación de 0,6959. En el caso de la variable ETP el modelo de regresión ajustado fue no lineal y su coeficiente de determinación de 0,8409. El análisis de regresión individual de cada una de las estaciones meteorológicas permitió explicar el comportamiento de la regresión basada en el conjunto completo de datos, tanto para la variable ETR como para la variable ETP. Los resultados refuerzan la ventaja de la teledetección en la estimación de ETR y ETP en zonas en donde no se dispone de datos meteorológicos.Marini, F.; Santamaría, M.; Oricchio, P.; Di Bella, CM.; Basualdo, A. (2017). Estimación de evapotranspiración real (ETR) y de evapotranspiración potencial (ETP) en el sudoeste bonaerense (Argentina) a partir de imágenes MODIS. Revista de Teledetección. (48):29-41. doi:10.4995/raet.2017.6743.SWORD294148Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212-213, 198-212. doi:10.1016/s0022-1694(98)00253-4Bastiaanssen, W. G. . (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229(1-2), 87-100. doi:10.1016/s0022-1694(99)00202-4Burnham, K.P., Anderson, D.R. 2002. Model Selection and Multimodel Inference - A Practical InformationTheoretic Approach. New York: Springer-Verlag.CASELLES, V., DELEGIDO, J., SOBRINO, J. A., & HURTADO, E. (1992). Evaluation of the maximum evapotranspiration over the La Mancha region, Spain, using NO A A AVHRR data. International Journal of Remote Sensing, 13(5), 939-946. doi:10.1080/01431169208904167Caselles, V., Artigao, M. M., Hurtado, E., Coll, C., & Brasa, A. (1998). Mapping Actual Evapotranspiration by Combining Landsat TM and NOAA-AVHRR Images: Application to the Barrax Area, Albacete, Spain. Remote Sensing of Environment, 63(1), 1-10. doi:10.1016/s0034-4257(97)00108-9Casta-eda-Ibá-ez, C., Martínez-Menes, M., PascualRamírez, F., Flores-Magdaleno, H., FernándezReynoso, D. y Esparza-Govea, S. 2015. Estimación de coeficientes de cultivo mediante sensores remotos en el distrito de riego río Yaqui, Sonora, México. Agrociencia, 49, 221-232.Castellví, F., & Snyder, R. L. (2010). A New Procedure Based on Surface Renewal Analysis to Estimate Sensible Heat Flux: A Case Study over Grapevines. Journal of Hydrometeorology, 11(2), 496-508. doi:10.1175/2009jhm1151.1Cihlar, J., St.-Laurent, L., & Dyer, J. A. (1991). Relation between the normalized difference vegetation index and ecological variables. Remote Sensing of Environment, 35(2-3), 279-298. doi:10.1016/0034-4257(91)90018-2Di Bella, C. M., Rebella, C. M., & Paruelo, J. M. (2000). Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina. International Journal of Remote Sensing, 21(4), 791-797. doi:10.1080/014311600210579Kustas, W. P., & Norman, J. M. (1997). A two-source approach for estimating turbulent fluxes using multiple angle thermal infrared observations. Water Resources Research, 33(6), 1495-1508. doi:10.1029/97wr00704Mora, F., & Iverson, L. R. (1998). On the sources of vegetation activity variation, and their relation with water balance in Mexico. International Journal of Remote Sensing, 19(10), 1843-1871. doi:10.1080/014311698215027Mulleady, C., Barrera, D. 2013. Estimación de la tasa de evapotranspiración a partir de datos satelitales. Meteorológica, 38(1), 21-39.Sánchez, M. 2000. Características y apreciaciones generales de los métodos de medida y estimación de evapotranspiración. Revista de Geografía Norte Grande, 27, 27-36.Tasumi, M., Bastiaanssen, W.G.M., Allen, R.G. 2000. Application of the SEBAL methodology for estimating consumptive use of water and stream flow depletion in the Bear River Basin of Idaho through remote sensing. Idaho Department of Water Resources, University of Idaho, Department of Biological and Agricultural Engineering.Thornthwaite C.W., Mather, J.R. 1955. The water balance. Publications in climatology. Centerton, NJ: Drexel Institute of Technology. Vol. VIII, Nº. 1.WAN, Z. (2008). New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sensing of Environment, 112(1), 59-74. doi:10.1016/j.rse.2006.06.026Wang, W., Liang, S., & Meyers, T. (2008). Validating MODIS land surface temperature products using long-term nighttime ground measurements. Remote Sensing of Environment, 112(3), 623-635. doi:10.1016/j.rse.2007.05.024Yang, W., Yang, L., & Merchant, J. W. (1997). An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A. International Journal of Remote Sensing, 18(10), 2161-2180. doi:10.1080/01431169721781

Observations of the 2019 April 4 Solar Energetic Particle Event at the Parker Solar Probe

A solar energetic particle event was detected by the Integrated Science Investigation of the Sun (IS⊙IS) instrument suite on Parker Solar Probe (PSP) on 2019 April 4 when the spacecraft was inside of 0.17 au and less than 1 day before its second perihelion, providing an opportunity to study solar particle acceleration and transport unprecedentedly close to the source. The event was very small, with peak 1 MeV proton intensities of ~0.3 particles (cm² sr s MeV)⁻¹, and was undetectable above background levels at energies above 10 MeV or in particle detectors at 1 au. It was strongly anisotropic, with intensities flowing outward from the Sun up to 30 times greater than those flowing inward persisting throughout the event. Temporal association between particle increases and small brightness surges in the extreme-ultraviolet observed by the Solar TErrestrial RElations Observatory, which were also accompanied by type III radio emission seen by the Electromagnetic Fields Investigation on PSP, indicates that the source of this event was an active region nearly 80° east of the nominal PSP magnetic footpoint. This suggests that the field lines expanded over a wide longitudinal range between the active region in the photosphere and the corona

Energy Spectra, Altitude Profiles and Charge Ratios of Atmospheric Muons

We present a new measurement of air shower muons made during atmospheric ascent of the High Energy Antimatter Telescope balloon experiment. The muon charge ratio mu+ / mu- is presented as a function of atmospheric depth in the momentum interval 0.3-0.9 GeV/c. The differential mu- momentum spectra are presented between 0.3 and about 50 GeV/c at atmospheric depths between 13 and 960 g/cm^2. We compare our measurements with other recent data and with Monte Carlo calculations of the same type as those used in predicting atmospheric neutrino fluxes. We find that our measured mu- fluxes are smaller than the predictions by as much as 70% at shallow atmospheric depths, by about 20% at the depth of shower maximum, and are in good agreement with the predictions at greater depths. We explore the consequences of this on the question of atmospheric neutrino production.Comment: 11 pages, 8 figures, to appear in Phys. Rev. D (2000