Sublittoral soft bottom communities and diversity of Mejillones Bay in northern Chile (Humboldt Current upwelling system)

The macrozoobenthos of Mejillones Bay (23°S; Humboldt Current) was quantitatively investigated over a 7-year period from austral summer 1995/1996 to winter 2002. About 78 van Veen grab samples taken at six stations (5, 10, 20 m depth) provided the basis for the analysis of the distribution of 60 species and 28 families of benthic invertebrates, as well as of their abundance and biomass. Mean abundance (2,119 individuals m-2) was in the same order compared to a previous investigation; mean biomass (966 g formalin wet mass m-2), however, exceeded prior estimations mainly due to the dominance of the bivalve Aulacomya ater. About 43% of the taxa inhabited the complete depth range. Mean taxonomic Shannon diversity (H', Log e) was 1.54 ± 0.58 with a maximum at 20 m (1.95 ± 0.33); evenness increased with depth. The fauna was numerically dominated by carnivorous gastropods, polychaetes and crustaceans (48%). About 15% of the species were suspensivorous, 13% sedimentivorous, 11% detritivorous, 7% omnivorous and 6% herbivorous. Cluster analyses showed a significant difference between the shallow and the deeper stations. Gammarid amphipods and the polychaete family Nephtyidae characterized the 5-mzone, the molluscs Aulacomya ater, Mitrella unifasciata and gammarids the intermediate zone, while the gastropod Nassarius gayi and the polychaete family Nereidae were most prominent at the deeper stations. The communities of the three depth zones did not appear to be limited by hypoxia during non-El Niño conditions. Therefore, no typical change in community structure occurred during El Niño 1997–1998, in contrast to what was observed for deeper faunal assemblages and hypoxic bays elsewhere in the coastal Humboldt Current system

Evaluation of classification algorithms in the Google Earth Engine platform for the identification and change detection of rural and periurban buildings from very high-resolution images

[EN] Building change detection based on remote sensing imagery is a key task for land management and planning e.g., detection of illegal settlements, updating land records and disaster response. Under the post- classification comparison approach, this research aimed to evaluate the feasibility of several classification algorithms to identify and capture buildings and their change between two time steps using very-high resolution images (<1 m/pixel) across rural areas and urban/rural perimeter boundaries. Through an App implemented on the Google Earth Engine (GEE) platform, we selected two study areas in Colombia with different images and input data. In total, eight traditional classification algorithms, three unsupervised (K-means, X-Means y Cascade K-Means) and five supervised (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy and Minimum distance) available at GEE were trained. Additionally, a deep neural network named Feature Pyramid Networks (FPN) was added and trained using a pre-trained model, EfficientNetB3 model. Three evaluation zones per study area were proposed to quantify the performance of the algorithms through the Intersection over Union (IoU) metric. This metric, with a range between 0 and 1, represents the degree of overlapping between two regions, where the higher agreement the higher IoU values. The results indicate that the models configured with the FPN network have the best performance followed by the traditional supervised algorithms. The performance differences were specific to the study area. For the rural area, the best FPN configuration obtained an IoU averaged for both time steps of 0.4, being this four times higher than the best supervised model, Support Vector Machines using a linear kernel with an average IoU of 0.1. Regarding the setting of urban/rural perimeter boundaries, this difference was less marked, having an average IoU of 0.53 in comparison to 0.38 obtained by the best supervised classification model, in this case Random Forest. The results are relevant for institutions tracking the dynamics of building areas from cloud computing platfo future assessments of classifiers in likewise platforms in other contexts.[ES] La detección de cambios de áreas construidas basada en datos de teledetección es una importante herramienta para el ordenamiento y la administración del territorio p.e.: la identificación de construcciones ilegales, la actualización de registros catastrales y la atención de desastres. Bajo el enfoque de comparación post-clasificación, la presente investigación tuvo como objetivo evaluar la funcionalidad de varios algoritmos de clasificación para identificar y capturar las construcciones y su cambio entre dos fechas de análisis usando imágenes de alta resolución (<1 m/píxel) en ámbitos rurales y límites del perímetro urbano municipal. La anterior evaluación fue llevada a cabo a través de una aplicación desarrollada mediante la plataforma Google Earth Engine (GEE), donde se alojaron y analizaron diferentes imágenes y datos de entrada sobre dos áreas de estudio en Colombia. En total, ocho algoritmos de clasificación tradicional, tres no supervisados (K-means, X-Means y Cascade K-Means) y cinco supervisados (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy y Minimum distance) fueron entrenados empleando GEE. Adicionalmente, se entrenó una red neuronal profunda denominada Feature Pyramid Networks (FPN) sobre la cual se aplicó la estrategia de modelos preentrenados, usando pesos del modelo EfficientNetB3. Por cada una de las dos áreas de estudio, tres zonas de evaluación fueron propuestas para cuantificar la funcionalidad de los algoritmos mediante la métrica Intersection over Union (IoU). Esta métrica representa la evaluación de la superposición de dos regiones y tiene un rango de valores de 0 a 1, donde a mayor coincidencia de las imágenes mayor es el valor de IoU. Los resultados indican que los modelos configurados con la red FPN tienen la mejor funcionalidad, seguido de los algoritmos tradicionales supervisados. Las diferencias de la funcionalidad fueron específicas por área de estudio. Para el ámbito rural, la mejor configuración de FPN obtuvo un IoU promedio entre ambas fechas de 0,4, es decir, cuatro veces el mejor modelo supervisado, correspondiente al Support Vector Machine de kernel Lineal con un IoU de 0,1. Respecto al área de límites del perímetro urbano municipal, esta diferencia fue menos marcada, con un IoU promedio de 0,53 en comparación con el 0,38 derivado del mejor modelo de clasificación supervisada, que en este caso fue Random Forest. Los resultados de esta investigación son relevantes para entidades responsables del seguimiento de las dinámicas de las áreas construidas a partir de plataformas de procesamiento en la nube como GEE, estableciendo una línea base para futuros estudios evaluando la funcionalidad de los clasificadores disponibles en otros contextos.Los autores agradecen a las Subdirecciones de Catastro, y Geografía y Cartografía del IGAC. Esta investigación hace parte de la licencia del programa GEO-GEE administrada por la Subdirección de Geografía y Cartografía. Se agradece igualmente al equipo de EODataScience por su soporte constante en los desarrollos técnicos de esta investigación.Coca-Castro, A.; Zaraza-Aguilera, MA.; Benavides-Miranda, YT.; Montilla-Montilla, YM.; Posada-Fandiño, HB.; Avendaño-Gomez, AL.; Hernández-Hamon, HA.... (2021). Evaluación de algoritmos de clasificación en la plataforma Google Earth Engine para la identificación y detección de cambios de construcciones rurales y periurbanas a partir de imágenes de alta resolución. Revista de Teledetección. 0(58):71-88. http://hdl.handle.net/10251/169765OJS718805

Scalable Visible Light Indoor Positioning System Using RSS

This paper proposes a visible light positioning system that utilizes commercial Light-Emitting Diode (LED) lamps as transmitters and Silicon PIN photodiodes as receivers. The light signals are transmitted and received using Intensity Modulation and Direct Detection (IMDD). The lamps are modulated using On–Off Keying (OOK) with the Manchester code, and the medium access control is achieved by Time-Division Multiplexing (TDM). The position is estimated using trilateration based on the Received Signal Strength (RSS). The system’s scalability is accomplished by replicating primary localization cells composed of seven lamps and drawing on the neighborhood synchrony, exploiting the spatial multiplexing property of the light. A basic unit in the cell comprises three lamps forming a localization triangle; then, one primary localization cell shall consist of six triangles sharing lights among basic neighbor units. The cell prototype was implemented to prove the working principle of the system. Three estimation methods were used to compute the position: a deterministic approach based on least-squares regression, an Artificial Neural Network (ANN) per lamp, and an ANN for the complete system. The best per lamp estimator was the ANN, computing positions that reached an experimental accuracy of 2.5 cm under indoor conditions

Long-term clinical outcomes of Zika-associated Guillain-Barré syndrome

Zika virus infection has been associated with the development of a spectrum of neurologic disease including Guillain–Barré syndrome (GBS)1. GBS is an autoimmune disorder of the peripheral nervous system often triggered by a preceding infection. The mechanism of Zika-associated GBS (Z-GBS) and the long-term clinical course is unknown. The purpose of this study was to describe the 2-year clinical course of Z-GBS in order to provide further insights into disease pathogenesis and prognosis

Long-term clinical outcomes of Zikaassociated Guillain-Barré syndrome

Zika virus infection has been associated with the development of a spectrum of neurologic disease including Guillain–Barré syndrome (GBS)1. GBS is an autoimmune disorder of the peripheral nervous system often triggered by a preceding infection. The mechanism of Zika-associated GBS (Z-GBS) and the long-term clinical course is unknown. The purpose of this study was to describe the 2-year clinical course of Z-GBS in order to provide further insights into disease pathogenesis and prognosis