Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification.

Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments.

[Humanized prenatal care in Latin America: a stat of the art]. / Atención prenatal humanizada en américa latina: un estado del arte.

Objetive: To identify contributions of the scientific literature produced in the 2009-2019 period in Latin America on humanized prenatal care. Data sources:Articles produced in Latin America that addressed humanized prenatal care, published between 2009 and 2019 in indexed journals (reported in MIAR) and located in recognized databases, were selected. Study selection: A selection based on the source, quality and relevance of the studies was taken into account, excluding gray literature. After applying filters, 26 articles were obtained. Data extraction: The data was recorded, subsequently using an analytical matrix for its coding. Data: the studies, especially from Colombia and Brazil, were largely qualitative. It is established that the quality of care is reflected in the accessibility and satisfaction of the users with the services. Humanized prenatal care affects maternal-perinatal health. The recognition of the particular needs of pregnant women is relevant. The studies recommend qualifying health personnel to strengthen humanized prenatal care, assuming pregnant women as protagonists of the process, dispelling their doubts and concerns, and providing evidence on the relationship humanized prenatal care / reduction of maternal morbidity and mortality. Conclusions: The studies carried out recognize the pregnant woman as the protagonist and her needs as the starting point for humanized prenatal care, conceived as an essential reference for the quality of services with a positive impact on the satisfaction of pregnant women and maternal outcomes. Training of health personnel is required.

Objetivo: Identificar aportes de la literatura científica producida en el periodo 2009-2019 en América Latina sobre atención prenatal humanizada. Fuentes de datos: Se seleccionaron artículos producidos en América Latina que abordaran la atención prenatal humanizada, publicados entre 2009 y 2019 en revistas indexadas (reportadas en MIAR) y localizados en bases de datos reconocidas. Selección de los estudios: Se tuvo en cuenta una selección basada en la fuente, calidad y pertinencia de los estudios, excluyendo la literatura gris. Luego de aplicar filtros se obtuvieron 26 artículos. Extracción de datos: Los datos fueron fichados, utilizando posteriormente una matriz analítica para su codificación. Datos: los estudios, sobre todo provenientes de Colombia y Brasil, fueron en buena parte cualitativos. Se establece que la calidad de la atención se refleja en la accesibilidad y la satisfacción de las usuarias con los servicios. El cuidado prenatal humanizado incide en la salud materno-perinatal. Es relevante el reconocimiento de las necesidades particulares de las gestantes. Los estudios recomiendan cualificar el personal de salud para fortalecer el cuidado prenatal humanizado, asumir las gestantes como protagonistas del proceso, disipando sus dudas y preocupaciones y aportar evidencia sobre la relación atención prenatal humanizada/reducción de la morbimortalidad materna. Conclusiones: Los estudios realizados reconocen a la gestante como protagonista y sus necesidades como el punto de partida para la atención prenatal humanizada, concebida como referente esencial de la calidad de los servicios con impacto positivo sobre la satisfacción de las gestantes y los resultados maternos. Se requiere formar al personal de salud.

Network Analysis of Gene Transcriptions of Arabidopsis thaliana in Spaceflight Microgravity.

The transcriptomic datasets of the plant model organism Arabidopsis thaliana grown in the International Space Station provided by GeneLab have been mined to isolate the impact of spaceflight microgravity on gene expressions related to root growth. A set of computational tools is used to identify the hub genes that respond differently in spaceflight with controlled lighting compared to on the ground. These computational tools based on graph-theoretic approaches are used to infer gene regulatory networks from the transcriptomic datasets. The three main algorithms used for network analyses are LASSO, Pearson correlation, and the HITS algorithm. Graph-based spectral analyses reveal distinct properties of the spaceflight microgravity networks for the WS, Col-0, and mutant phyD ecotypes. The set of hub genes that are significantly altered in spaceflight microgravity are mainly involved in cell wall synthesis, protein transport, response to auxin, stress responses, and catabolic processes. Network analysis highlights five important root growth-regulating hub genes that have the highest outdegree distribution in spaceflight microgravity networks. These concerned genes coding for proteins are identified from the Gene Regulatory Networks (GRNs) corresponding to spaceflight total light environment. Furthermore, network analysis uncovers genes that encode nucleotide-diphospho-sugar interconversion enzymes that have higher transcriptional regulation in spaceflight microgravity and are involved in cell wall biosynthesis.