Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery.

Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.

Valores de hormona antimülleriana (HAM) y recuento de folículos antrales (RFA) en casos con fallo reproductivo previo / Anti-Müllerian hormone values and antral follicle count in patients with prior reproductive failure

Objetivo. Determinar si la HAM y el RFA pueden emplearse para predecir reserva ovárica. Sujetos y métodos. Se compara el RFA y los valores de la HAM en FIV/ICSI: bajas, normo y altas respondedoras, las primeras con fallos de TRA previos (un FIV/ICSI). El RFA se llevó a cabo con US 3D con modos superficie, inverso y AVC. Resultados. Existió correlación entre HAM y RFA. Ambos reflejan la reserva ovárica. Las diferencias entre normo y bajas respondedoras fueron significativas. RFA, HAM y edad mostraron ser los mejores parámetros. Las diferencias entre normo y altas respondedoras fueron igualmente significativas, mostrando ser predictivos de hiperestimulación. El grupo de número de ovocitos recuperados (<=5 vs >5) mostró ser predictivo de maduración, no de calidad embrionaria ni de pronóstico reproductivo. Conclusiones. La determinación de HAM y RFA no permite negar un nuevo intento FIV, pero obligan a informar a la pareja de las bajas probabilidades de éxito. Valores de HAM y RFA superiores a 27,21pmol/L y 15 folículos son riesgo de hiperestimulación. El pronóstico reproductivo muestra alto índice de cancelaciones (22% de ciclos iniciados) muy bajo de gestaciones (16,7%), con solo un 6,2% de embarazos evolutivos. Valores bajos de HAM (<9,28pmol/L) predicen mal pronóstico con un 72,7 y 64,5% de sensibilidad y especificidad. La mejor aproximación predictiva es la combinación HAM más RFA, con un área bajo la curva de 82% (AU)

Objective. To determine whether anti-Müllerian hormone (AMH) and antral follicle count (AFC) could be used as predictors of ovarian reserve. Subjects and methods. We compared AFC and AMH values in IVF/ICSI in low, normal and high responders. Low responders had undergone previous assisted reproduction technologies (ART) and had experienced at least one IVF failure.AFC was carried out with US 3D surface, inverse mode, and automatic volume calculation. Results. AMH and AFC were correlated and reflected the actual ovarian reserve. Differences between normal and low responders were significant. The best predictors of ovarian reserve were AMH, AFC and age. Differences between normal and high responders were also significant and were predictors of hyperstimulation. Grouping patients according to oocyte recovery (<= vs. >5) was predictive of maturation but was not predictive of embryo quality or reproductive outcome. Conclusions. Low AMH values (<9.28pmol/L) predicted poor outcomes with a sensitivity and specificity of 72.7% and 64.5%. The best predictive approach was the combination of AMH plus AFC, resulting in an area under the curve of 82.2%. AMH and AFC above 27.21pmol/L and 15 follicles indicated a high risk of hyperstimulation. The reproductive outcome showed a very high cancelation rate (22% of initiated cycles), very low pregnancy rates (16.7%) and only 6.2% of ongoing pregnancies. Determination of AMH and AFC levels should not be used to reject candidates for IVF treatment but to inform patients with low levels of the very poor probability of success and the use of other alternatives (AU)

Ultrasonidos. ¿A dónde vamos? Un esfuerzo por observar la vida intraútero más claramente / Ultrasonography: where do we go? An effort to see intrauterus life more clearly

Objetivo: Ésta es una somera revisión de las posibilidades en ultrasonografía 3D y 4D disponibles actualmente, que creemos de interés para los obstetras y ginecólogos. Método: Se describen los fundamentos de las técnicas actuales y las técnicas complementarias, así como las aplicaciones en obstetricia, ginecología y reproducción humana. Conclusión: El actual desarrollo de los modos de adquisición y software permite el manejo de las imágenes para optimizar el análisis de los datos y, de este modo, mejorar la certeza diagnóstica en diferentes áreas de la ginecología y la obstetricia

Objective: To provide a brief review of the currently available possibilities of 3D-4D ultrasonography, which we believe are of interest to obstetricians and gynecologists. Methods: We describe the basic principles of current techniques, complementary techniques, and their applications in obstetrics, gynecology and human reproduction. Conclusion: The current development of acquisition modes and software allows image manipulation to optimize data analysis and improves diagnostic accuracy in obstetrics and gynecology