Determinants of Metabolic Health Across Body Mass Index Categories in Central Europe: A Comparison Between Swiss and Czech Populations.

Comparisons among countries can help to identify opportunities for the reduction of inequalities in cardiometabolic health. The present cross-sectional analysis and meta-analysis aim to address to what extent obesity traits, socioeconomic, and behavioral factors determine poor metabolic health across body mass index (BMI) categories in two urban population-based samples from Central Europe. Data from the CoLaus (~6,000 participants; Lausanne, Switzerland) and the Kardiovize Brno 2030 (~2,000 participants; Brno, Czech Republic) cohorts. For each cohort, logistic regression analyses were performed to identify the main determinants of poor metabolic health overall and stratified by body mass index (BMI) categories. The results of each cohort were then combined in a meta-analysis. We first observed that waist circumference and body fat mass were associated with metabolic health, especially in non-obese individuals. Moreover, increasing age, being male, having low-medium educational level, abdominal obesity, and high body fat mass were the main determinants of the metabolically unhealthy profile in both cohorts. Meta-analysis stratified by BMI categories confirmed the previous results with slight differences across BMI categories. In fact, increasing age and being male were the main determinants of poor metabolic health independent of obesity status. In contrast, low educational level and current smoking were associated with poor metabolic health only in non-obese individuals. In line, public health strategies against obesity and related comorbidities should aim to improve social conditions and to promote healthy lifestyles before the progression of metabolic disorders

Communication abilities in the clinical interview

[Resumen] Las habilidades comunicacionales constituyen una parte importante de la entrevista clínica. Para una mejor comprensión pueden dividirse en los siguientes apartados: contexto, escucha, comprensión, estrategia y resumen general. Se detallan en cada uno de ellos (a excepción del contexto por haber sido bordado en otro artículo) una serie de técnicas o consideraciones de interés que facilitan una mejora en la interacción del profesional de la salud (fisioterapeuta) con el consultante. Esta mejora repercute tanto en una mayor calidad de los resultados como a nivel afectivo y emocional en ambos protagonistas de la relación.[Abstract] The communication skills are an important part of the clinical interview. To get a better comprehension they can be divided into the following steps: context, listening, acknowledgement, strategy and summary. Some techniques or interesting considerations which can improve health care provider (physiotherapist) patient relationship are explained in each one (but the context because it has been considered in another article). This improvement is involved with not only better clinical outcomes, but also affective and emotional level for both relationship's main character

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

Entrepreneurship, communication and ICT in secondary education

Education is essential to developing young people’s skills and culture. It is vital that entrepreneurship education is addressed from the secondary education. Entrepreneurship education is essential not only to shape the mindsets of young people but also to provide the skills that are important to developing an entrepreneurial culture. The entrepreneurship key competence refers to an individual’s ability to turn ideas into action. It includes creativity, innovation, and the ability to plan projects in order to achieve objectives. Besides, communication and ICT are relevant in innovation processes in organizations, especially in schools where people interact daily and it is intended to ensure a good future for the organization. In schools, the existence of good communication and the use of ICT is a relevant factor for the integration of teaching innovative projects related to entrepreneurship. A good communication ensures the dissemination of educational innovation processes adopted by the teachers. It´s relevant to improve teaching practice. Communication and the ICT play a key role in the processes of educational innovation. The incorporation of innovative materials, ICT, courses and communication activities, and the use of the media to extract information, are very important. Communication is essential in the secondary education. If the communication flows were eliminated in the school, we would not have school. Communication is introduced into all activities of the school, representing an important tool with which individuals understand their role in the school and integrates organizational departments. The secondary schools are organized through a model based on the participation and collaboration of its component, coordinating the actions of different people in order to achieve the proposed educational objectives. Therefore, the optimal operation of a secondary school is closely related to communication processes taking place in this organization. This paper studies the entrepreneurship, communication processes and the ICT in secondary education. Media and communication channels used in secondary schools to spread the teaching innovation projects related to the entrepreneurship are studied. Supports or conventional tools and new technologies for communication in educational organizations are also analysed when it is studied and worked the entrepreneurship in the classroom.info:eu-repo/semantics/publishedVersio

Broadband metamaterial for nonresonant matching of acoustic waves

Unity transmittance at an interface between bulk media is quite common for polarized electromagnetic waves incident at the Brewster angle, but it is rarely observed for sound waves at any angle of incidence. In the following, we theoretically and experimentally demonstrate an acoustic metamaterial possessing a Brewster-like angle that is completely transparent to sound waves over an ultra-broadband frequency range with >100% bandwidth. The metamaterial, consisting of a hard metal with subwavelength apertures, provides a surface impedance matching mechanism that can be arbitrarily tailored to specific media. The nonresonant nature of the impedance matching effectively decouples the front and back surfaces of the metamaterial allowing one to independently tailor the acoustic impedance at each interface. On the contrary, traditional methods for acoustic impedance matching, for example in medical imaging, rely on resonant tunneling through a thin antireflection layer, which is inherently narrowband and angle specific

Adaptación a la altura y a ambientes fríos en personas con lesión medular

Objetivo: estudiar las diferencias en la adaptación respiratoria al ejercicio en condiciones medioambientales similares a las de la práctica de los deportes de invierno, entre personas físicamente activas y sanas con lesión medular y sin lesión medular. Material y método: participaron 24 voluntarios sanos y físicamente activos, 12 presentaban en su historia una lesión medular y los otros 12 no. Cada sujeto realizó tres pruebas de esfuerzo máximas con monitorización ventilatoria, pero modificando las circunstancias ambientales: a nivel del mar y a 22 - 24º C; a 3.000 metros de altura simulada y a 22 - 24º C, y a 3.000 metros de altura simulada y a 5 - 6º C. Resultados: al comparar los valores observados en las pruebas de altura simulada respecto a la realizada a nivel del mar, se observó un aumento significativo de los valores del consumo de VO2 y de la producción del VCO2. Este aumento no se acompañó de modificaciones en la ventilación, la frecuencia respiratoria o el volumen corriente. Paralelamente, la fracción espirada para el O2 y el CO2, el equivalente respiratorio para el O2 y el CO2 o la presión al final de la espiración para el O2 y el CO2 mostraron cambios estadísticamente significativos. Conclusiones: la realización de un esfuerzo intenso produce importantes cambios ventilatorios con necesidades de oxígeno superiores para una altura simulada de 3.000 metros que no cambian sustancialmente con el frío

SMARCA4 deficient tumours are vulnerable to KDM6A/UTX and KDM6B/JMJD3 blockade

The authors thank Isabel Bartolessis (Cancer Genetics Group) at IJC for technical assistance. This work was supported by the Spanish Ministry of Economy and CompetitivityMINECO (grant number SAF-2017-82186R, to M.S.-C., and grant PI19/01320 to A. Villanueva) and from the Fundacion Cientifica of the Asociacion Espanola Contra el Cancer (AECC) (grant number GCB14142170MONT) to M.S.-C. A. Villanueva is also funded by the Department of Health of the Generalitat de Catalunya (2014SGR364). O.A. R. received a Juan de la Cierva postdoctoral contract (grant No. IJCI-2016-28201, until November 2019) and an AECC research contract (INVES19045ROME from December 2019). A. Vilarrubi, P.L. and A.A. are supported by pre-doctoral contracts from the Spanish MINECO (FPI-fellowship: PRE2018-084624, BES-2015-072204 and FPU17/00067). M.S. was supported by a Rio Hortega contract from the Instituto de Salud Carlos III (CM17/00180). L.F. received a European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions grant agreement, number 799850.Despite the genetic inactivation of SMARCA4, a core component of the SWI/SNF-complex commonly found in cancer, there are no therapies that effectively target SMARCA4-deficient tumours. Here, we show that, unlike the cells with activated MYC oncogene, cells with SMARCA4 inactivation are refractory to the histone deacetylase inhibitor, SAHA, leading to the aberrant accumulation of H3K27me3. SMARCA4-mutant cells also show an impaired transactivation and significantly reduced levels of the histone demethylases KDM6A/UTX and KDM6B/JMJD3, and a strong dependency on these histone demethylases, so that its inhibition compromises cell viability. Administering the KDM6 inhibitor GSK-J4 to mice orthotopically implanted with SMARCA4-mutant lung cancer cells or primary small cell carcinoma of the ovary, hypercalcaemic type (SCCOHT), had strong anti-tumour effects. In this work we highlight the vulnerability of KDM6 inhibitors as a characteristic that could be exploited for treating SMARCA4-mutant cancer patients.Spanish Ministry of Economy and Competitivity-MINECO SAF-2017-82186R PI19/01320Fundacion Cientifica of the Asociacion Espanola Contra el Cancer (AECC) GCB14142170MONTDepartment of Health of the Generalitat de Catalunya 2014SGR364Juan de la Cierva postdoctoral contract IJCI-2016-28201AECC research contract INVES19045ROMESpanish MINECO PRE2018-084624 BES-2015-072204 FPU17/00067Instituto de Salud Carlos III European Commission CM17/00180European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions grant agreement 79985

Rising nutrient-pulse frequency and high UVR strengthen microbial interactions

Solar radiation and nutrient pulses regulate the ecosystem’s functioning. However, little is known about how a greater frequency of pulsed nutrients under high ultraviolet radiation (UVR) levels, as expected in the near future, could alter the responses and interaction between primary producers and decomposers. In this report, we demonstrate through a mesocosm study in lake La Caldera (Spain) that a repeated (press) compared to a one-time (pulse) schedule under UVR prompted higher increases in primary (PP) than in bacterial production (BP) coupled with a replacement of photoautotrophs by mixotrophic nanoflagellates (MNFs). The mechanism underlying these amplified phytoplanktonic responses was a dual control by MNFs on bacteria through the excretion of organic carbon and an increased top-down control by bacterivory. We also show across a 6-year whole-lake study that the changes from photoautotrophs to MNFs were related mainly to the frequency of pulsed nutrients (e.g. desert dust inputs). Our results underscore how an improved understanding of the interaction between chronic and stochastic environmental factors is critical for predicting ongoing changes in ecosystem functioning and its responses to climatically driven changes.This study was supported by the Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional (FEDER) (CGL2011-23681 and CGL2015-67682-R to PC), Ministerio de Medio Ambiente, Rural, y Marino (PN2009/067 to PC) and Junta de Andalucía (Excelencia projects P09-RNM-5376 and P12-RNM-327 to PC and JMMS, respectively). M.J.C. was supported by the Spanish Government “Formación de Profesorado Universitario” PhD grant (FPU12/01243) and I.D.-G. by the Junta de Andalucía “Personal Investigador en Formación” PhD grant (FPI RNM-5376). This work is in partial fulfillment of the Ph. D. thesis of M.J.C

Divergência genética em uma população de melhoramento de cajueiro.

A seleção de genitores com ampla dissimilaridade genética é condição fundamental para obtenção de híbridos com maiores efeitos heteróticos. O objetivo deste trabalho foi avaliar a divergência genética de progênies de cajueiro-comum, pertencentes à população de melhoramento de meios-irmãos, e identificar grupos superiores e dissimilares para as variáveis agronômicas