Preparación y caracterización del compuesto superconductor [Y0.8Ca0.2]SrBaCu2.8(BO3)0.2O6.4

Luego del descubrimiento del YBa2 CU3 01-0 [1] en 1987 científicos diversos han venido preparando y estudiando diferentes variantes de este compuesto en las que se realizaron sustituciones catiónicas y/o aniónicas con la finalidad de estudiar las transformaciones estructurales y la temperatura critica superconductora. El presente trabajo nace a partir de reportes, en revistas especializadas, las cuales indican que el (YO.8SCaO.1S)SrO.5BaI.5CU2.5(B03)o.s0a es superconductor con una temperatura crítica igual a 55 K [2] Y que el compuesto (Yo.s CaO.5 )srBaCu 2.15 (B03 )0.25 ° a es superconductor con una temperatura crítica igual a 50 K [3]. Se hace una revisión de los conceptos teóricos de la superconductividad y se preparó la muestra [Yo.gCaO.2 }srBaCu2.8 (B03 )0.2 06.4 a través del método de la reacción del estado sólido. Se analizó la muestra usando la difracción de rayos X con posterior refinamiento de los datos mediante el método de Rietveld y la simulación de estos en el programa CaRine con el fin de determinar el tipo de estructura que posee además de saber a que grupo espacial pertenece.Tesi

Aplicación del algoritmo de redes elásticas en imágenes satelitales

This investigation was carried out in the Santuario Nacional Los Manglares de Tumbes (SNLMT), located in the district of Zarumilla department of Tumbes, is oriented to implement a methodology that allows to characterize the mangrove cover. For this, the image of the TM sensor of the LandSat 5 satellites was analyzed and processed evaluating a series of parameters related to the soil surface, such as SAVI (Index of vegetation adjusted to the ground), NDVI (Index of vegetation of dierence normalized) and NDWI (Normalized dierence water index) with a view to establishing the optimum index that allows to discriminate the dierent components of the Sanctuary's soil cover. The optimal index (SAVI) described above was introduced in the Elastic Net Algorithm (ENA) for the classication of the SNLMT ground cover. The images constructed from the ENA results were subjected to the validation process using conventional methods such as the maximum likelihood algorithm (MLA). This validation process consisted of performing the analyzes and comparisons of the average spectral signature graphs of each informational class obtained with both ENA and MLA, resulting in similar graphs where the RMSE was below 0.052 (dimensionless) and the Correlation factor on r=0.886. This indicates that the ENA method proves to be an eective tool for the subdivision of mangrove coverage classes.Esta investigación se realizó en el Santuario Nacional Los Manglares de Tumbes (SNLMT), ubicado en el distrito de Zarumilla departamento de Tumbes, está orientada a implementar una metodología que permita caracterizar la cobertura de manglar. Para ello, se analizó y procesó la imagen del sensor TM del satélites LandSat 5 evaluando una serie de parámetros relacionados a la supercie del suelo, tales como SAVI (Índice de vegetación ajustado al suelo), NDVI (Índice de vegetación de diferencia normalizada) y NDWI (Índice de agua de diferencia normalizada) con miras a establecer el índice óptimo que permita discriminar las diferentes componentes de cobertura de suelo del Santuario. El indice óptimo (SAVI) antes descrito fue introducido en el Algoritmo de las Redes Elásticas (ENA, por sus siglas en ingles) para la clasicación de la cobertura de suelo del SNLMT. Las imágenes construidas a partir de los resultados ENA, fueron sometidos al proceso de validación empleando métodos convencionales como el algoritmo de máxima verosimilitud (AMV). Tal proceso de validación consistió en realizar los análisis y comparaciones de las grácas de rmas espectrales promedio de cada clase informacional obtenidos tanto con ENA y AMV, dando como resultados similares grácas donde el RMSE fue por debajo de 0.052 (adimensional) y el factor de correlación sobre r=0.886. Esto indica que el método ENA resulta ser una herramienta ecaz para la subdivisión de clases de cobertura manglar

Estimation of seasonal stages based on climate parameters measured at the Apacheta micro-basin weather station, Ayacucho Region, 2000 to 2018

La estacionalidad de la precipitación, temperatura máxima y mínima del aire, humedad relativa, velocidad del viento y radiación solar, impactan en el estado ecológico de la microcuenca Apacheta. El objetivo es estimar los estadíos estacionales a partir de parámetros climáticos medidos en la estación meteorológica de la microcuenca Apacheta, en base a la selección de intervalos de valores de precipitación. El estadío lluvioso comprende los meses de enero, febrero, marzo y diciembre, su precipitación es de 145.96 mm con temperatura máxima del aire de 12.3 °C, temperatura mínima del aire de 1.55°C, humedad relativa de 74.04 %, velocidad del viento de 2.6 m/s y radiación solar de 527.13 Ly. El estadío intermedio en abril, setiembre, octubre y noviembre, con precipitación de 51.89 mm con temperatura máxima del aire de 13.38 °C, temperatura mínima del aire de -0.2 °C, humedad relativa de 69.76 %, velocidad del viento de 2.96 m/s y radiación solar de 552.37 Ly. El estadío seco en mayo, junio, julio y agosto, con precipitación de 15.41 mm, temperatura máxima del aire de 12.51 °C, temperatura mínima del aire de -2.4 °C, humedad relativa de 67.49 %, velocidad del viento de 3.16 m/s y radiación solar de 463.79 Ly.The seasonality of rainfall, maximum and minimum air temperature, relative humidity, wind speed and solar radiation impact the ecological status of the Apacheta micro-basin. The objective is to estimate the seasonal stages from climatic parameters measured at the Apacheta micro-basin weather station, based on the selection of intervals of precipitation values. The rainy season includes the months of January, February, March and December, its precipitation is 145.96 mm with maximum air temperature of 12.3 °C, minimum air temperature of 1.55 °C, relative humidity of 74.04 %, wind speed of 2.6 m/s and solar radiation of 527.13 Ly. The intermediate stage in April, September, October and November, with precipitation of 51.89 mm with maximum air temperature of 13.38 °C, minimum air temperature of -0.2 °C, relative humidity of 69.76 %, wind speed of 2.96 m/s and solar radiation of 552.37 Ly. The dry stage in May, June, July and August, with precipitation of 15.41 mm, maximum air temperature of 12.51 °C, minimum air temperature of -2.4 °C, relative humidity of 67.49 %, wind speed of 3.16 m/s and solar radiation of 463.79 Ly

The pitfalls and promise of liquid biopsies for diagnosing and treating solid tumors in children : a review

Cell-free DNA profiling using patient blood is emerging as a non-invasive complementary technique for cancer genomic characterization. Since these liquid biopsies will soon be integrated into clinical trial protocols for pediatric cancer treatment, clinicians should be informed about potential applications and advantages but also weaknesses and potential pitfalls. Small retrospective studies comparing genetic alterations detected in liquid biopsies with tumor biopsies for pediatric solid tumor types are encouraging. Molecular detection of tumor markers in cell-free DNA could be used for earlier therapy response monitoring and residual disease detection as well as enabling detection of pathognomonic and therapeutically relevant genomic alterations. Conclusion: Existing analyses of liquid biopsies from children with solid tumors increasingly suggest a potential relevance for molecular diagnostics, prognostic assessment, and therapeutic decision-making. Gaps remain in the types of tumors studied and value of detection methods applied. Here we review the current stand of liquid biopsy studies for pediatric solid tumors with a dedicated focus on cell-free DNA analysis. There is legitimate hope that integrating fully validated liquid biopsy-based innovations into the standard of care will advance patient monitoring and personalized treatment of children battling solid cancers

Selection for improved energy use efficiency and drought tolerance in canola results in distinct transcriptome and epigenome changes

To increase both the yield potential and stability of crops, integrated breeding strategies are used that have mostly a direct genetic basis, but the utility of epigenetics to improve complex traits is unclear. A better understanding of the status of the epigenome and its contribution to agronomic performance would help in developing approaches to incorporate the epigenetic component of complex traits into breeding programs. Starting from isogenic canola (Brassica napus) lines, epilines were generated by selecting, repeatedly for three generations, for increased energy use efficiency and drought tolerance. These epilines had an enhanced energy use efficiency, drought tolerance, and nitrogen use efficiency. Transcriptome analysis of the epilines and a line selected for its energy use efficiency solely revealed common differentially expressed genes related to the onset of stress tolerance-regulating signaling events. Genes related to responses to salt, osmotic, abscisic acid, and drought treatments were specifically differentially expressed in the drought-tolerant epilines. The status of the epigenome, scored as differential trimethylation of lysine-4 of histone 3, further supported the phenotype by targeting drought-responsive genes and facilitating the transcription of the differentially expressed genes. From these results, we conclude that the canola epigenome can be shaped by selection to increase energy use efficiency and stress tolerance. Hence, these findings warrant the further development of strategies to incorporate epigenetics into breeding

Annual trend, anomalies and prediction of vegetation cover behavior with Landsat and MOD13Q1 images, Apacheta micro-basin, Ayacucho Region

[EN] Climate variability in the Apacheta micro-basin has an impact on vegetation behavior. The objective is to analyze the annual trend, anomalies and predict the behavior of vegetation cover (CV) with Landsat images and the MOD13Q1 product in the Apacheta micro-basin of the Ayacucho Region. For this purpose, the CV was classified and validated with the Kappa index (p-value=0,032; <0.05), obtaining a good agreement between the values observed in situ and the estimated in the Landsat images. The CV data were subjected to the Lilliefors normality test (p-value=0,0014; <0,05) indicating that they do not come from a normal distribution. CV forecasting was performed with the auto.arima, forecast and prophet packages, in R, according to the Box-Jenkins and ARIMA approaches, whose two-year future scenario is acceptable, but with higher bias. The results show an anual increasing CV trend of 3,378.96 ha with Landsat imagery and 3,451.95 ha with the MOD13Q1 product, by the end of 2020. The anomalies and the CV forecast also show a significant increase in the last 9 years, becoming higher in the forecasted years, 2021 and 2022.[ES] La variabilidad climática en la microcuenca Apacheta impacta en el comportamiento de la vegetación. El objetivo es analizar la tendencia anual, anomalías y predecir el comportamiento de cobertura de vegetación (CV) con imágenes Landsat y el producto MOD13Q1 en la microcuenca Apacheta de la Región Ayacucho. Para ello, se clasificó la CV, que se validó con el índice Kappa (p-valor=0,032; <0,05) obteniéndose una buena concordancia entre los valores observados in situ y los estimados en las imágenes Landsat. Los datos de CV se sometieron a la prueba de normalidad Lilliefors (p-valor=0,0014; <0,05) indicando que no provienen de una distribución normal. El pronóstico de CV se realizó con los paquetes auto.arima, forecast y prophet, en R, según el enfoque de Box-Jenkins y ARIMA, cuyo escenario futuro de dos años es aceptable, pero con mayor sesgo. Los resultados muestran una tendencia anual de CV creciente de 3.378,96 ha con imágenes Landsat y de 3.451,95 ha con el producto MOD13Q1, para finales del 2020. Las anomalías y el pronóstico de CV también evidencian un significativo incremento en los últimos 9 años, llegando a ser superiores en los años pronosticados, 2021 y 2022. Este trabajo ha sido posible gracias a los proyectos “Strengthening resilience of Andean river basin headwaters facing global change” (PGA_084063), financiado por el Programa PEER de USAID y “Modelado de aguas subterráneas en los ecosistemas de humedales de la microcuenca Apacheta”, financiado por FOCAM de la Universidad Nacional de San Cristóbal de Huamanga. Los autores también agradecen a la Universidad Nacional de Frontera por su apoyo incondicional.Moncada, W.; Willems, B.; Pereda, A.; Aldana, C.; Gonzales, J. (2022). Tendencia anual, anomalías y predicción del comportamiento de cobertura de vegetación con imágenes Landsat y MOD13Q1, microcuenca Apacheta, Región Ayacucho. Revista de Teledetección. 0(59):73-86. https://doi.org/10.4995/raet.2022.15672OJS7386059Abujayyab, S. K., Karaş, İ. R. 2019. Automated Prediction System for Vegetation Cover Based on MODIS-NDVI Satellite Data and Neural Networks. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W19, 9-15. https://doi.org/10.5194/isprs-archives-XLII-4-W19-9-2019Aguilar, H., Mora, R., Vargas, C. 2014. Metodología para la corrección atmosférica de imágenes Aster, Rapideye, Spot 2 y Landsat 8 con el módulo Flaash del software Envi. Revista Geográfica de América Central, 2(53), 39-59. https://doi.org/10.15359/rgac.2-53.2Cairns, D. M. 2001. A Comparison of Methods for Predicting Vegetation Type. Plant Ecology, 156(1), 3-18. https://doi.org/10.1023/A:1011975321668Ceroni, M., Achkar, M., Gazzano, I., Burgeño, J. 2015. Estudio del NDVI mediante análisis multiescalar y series temporales utilizando imágenes SPOT, durante el período 1998-2012 en el Uruguay. Revista de Teledetección. Asociación Española de Teledetección, 43, 31-42. https://doi.org/10.4995/raet.2015.3683Dallal, G. E., Wilkinson, L. 1986. An Analytic Approximation to the Distribution of Lilliefors's Test Statistic for Normality. The American Statistician, 40(4), 294-296. https://doi.org/10.1080/00031305.1986.10475419Forzieri, G., Castelli, F., Vivoni, E. R. 2010. A Predictive Multidimensional Model for Vegetation Anomalies Derived From Remote-Sensing Observations. IEEE Transactions on Geoscience and Remote Sensing, 48(4), 1729-1741. https://doi.org/10.1109/TGRS.2009.2035110Han, J., Huang, Y., Zhang, H., Wu, X. 2019. Characterization of elevation and land cover dependent trends of NDVI variations in the Hexi region, northwest China. Journal of Environmental Management, 232, 1037-1048. https://doi.org/10.1016/j.jenvman.2018.11.069Hoek van Dijke, A. J., Mallick, K., Teuling, A. J., Schlerf, M., Machwitz, M., Hassler, S.K., Blume, T., Herold, M. 2019. Does the Normalized Difference Vegetation Index explain spatial and temporal variability in sap velocity in temperate forest ecosystems? Hydrology and Earth System Sciences, 23(4), 2077-2091. https://doi.org/10.5194/hess-23-2077-2019Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., Ferreira, L. G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Petropoulos, F., Chhay, L., O'Hara- Wild, M., Yasmeen, F. 2020. Package «Forecast». Forecasting functions for time series and linear models. https://pkg.robjhyndman.com/forecast/Hyndman, R. J., Khandakar, Y. 2008. Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(1), 1-22. https://doi.org/10.18637/jss.v027.i03ITT Visual Information Solutions. 2009. ENVI Atmospheric Correction Module: QUAC and FLAASH User's Guide, Version 4.7, pp. 44. http://www.harrisgeospatial.com/portals/0/pdfs/envi/ Flaash_Module.pdfKatchanov, Y. L., Markova, Y. V., Shmatko, N. A. 2019. The distinction machine: Physics journals from the perspective of the Kolmogorov-Smirnov statistic. Journal of Informetrics, 13(4), 100982. https://doi.org/10.1016/j.joi.2019.100982Lilliefors, H. W. 1967. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399-402. https://doi.org/10.1080/01621459.1967.10482916Moncada, W., Willems, B. 2020a. Spatial and temporal analysis of surface temperature in the Apacheta micro-basin using Landsat thermal data. Revista de Teledetección, 0(57), 51-63. https://doi.org/10.4995/raet.2020.13855Moncada, W, Willems, B. 2020b. Tendencia anual del caudal de salida, en referencia al caudal ecológico en la microcuenca Apacheta / Ayacucho / Perú, del 2000 al 2018. Ecología Aplicada, 19(2), 93-102. https://doi.org/10.21704/rea.v19i2.1560Moncada, W, Willems, B., Rojas, J. 2020. Estimación de estadíos estacionales a partir de parámetros climáticos medidos en la estación meteorológica de la microcuenca Apacheta, Región Ayacucho, 2000 al 2018. Revista de Investigación de Física. UNMSM, 23(2), 17-25. https://doi.org/10.15381/rif.v23i2.20296Nanzad, L., Zhang, J., Tuvdendorj, B., Nabil, M., Zhang, S., Bai, Y. 2019. NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016. Journal of Arid Environments, 164, 69-77. https://doi.org/10.1016/j.jaridenv.2019.01.019Neinavaz, E., Skidmore, A. K., Darvishzadeh, R. 2020. Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method. International Journal of Applied Earth Observation and Geoinformation, 85, 101984. https://doi.org/10.1016/j.jag.2019.101984Olivo, A. 2017. Clasificación de la vegetación del Karst de Sierra de las Nieves, utilizando imágenes Landsat (Sierra de las Nieves, Málaga, Andalucía, España) [Masters, E.T.S.I de Minas y Energía]. http://oa.upm.es/48286/Pereda, A., Moncada, W., Verde, L. 2018. Respuesta nival de la cabecera de cuenca Cachi-Apacheta de Ayacucho: Vol. I. Editorial Académica Española. https://www.morebooks.shop/store/es/book/ respuesta-nival-de-la-cabecera-de-cuenca-cachi- apacheta-de-ayacucho/isbn/978-620-2-12620-5Qiu, B., Zeng, C., Cheng, C., Tang, Z., Gao, J., Sui, Y. 2014. Characterizing landscape spatial heterogeneity in multisensor images with variogram models. Chinese Geographical Science, 24(3), 317-327. https://doi.org/10.1007/s11769-013-0649-yRashmi, M. K., Lele, N. 2010. Spatial modeling and validation of forest cover change in Kanakapura region using GEOMOD. Journal of the Indian Society of Remote Sensing, 38(1), 45-54. https://doi.org/10.1007/s12524-010-0011-0Sánchez, J. M. 2016. Análisis de Calidad Cartográfica mediante el estudio de la Matriz de Confusión. Pensamiento Matemático, 6(2), 9-26. Disponible en https://dialnet.unirioja.es/servlet/ articulo?codigo=5998855Spiegel, M., Stephens, L. 2009. Estadística (4a. edición). McGraw-Hill. Interamericana Editores, S.A. https://www.academia.edu/36241872/Estad%C3%ADstica_Serie_Schaum_4ta_edici%C3%B3n_Murray_R_Spiegel_pdf_1_1_Taylor, S., Letham, B. 2017. Forecasting at scale. PeerJ Preprints 5, 25. https://doi.org/10.7287/peerj.preprints.3190v2Tornero, J. 2017. Introducción al Forecasting con R Statistics [Estadística]. Doctor Metrics. https://www. doctormetrics.com/introduccion-al-forecasting-con-r-statistics/Zaraza, M. A., Manrique, L. M. 2019. Generation of change data of land cover in the Bogotá savannah using time series with Landsat images and MODIS-Landsat synthetic images between 2007 and 2013. Revista de Teledetección, 0(54), 41-58. https://doi.org/10.4995/raet.2019.1228

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care