RESUMEN
This paper presents the utilization of Synthetic Aperture Radar (SAR) data for monitoring and detection of oil spills. In this work, a case study of an oil spill has been investigated using C-band Sentinel-1A SAR data to detect the oil spill that occurred on 28 January 2017, near Ennore port, Chennai, India. Oil spill damages marine ecosystems causing serious environmental effects. Quite often, oil spills on the sea/ocean surface are seen nowadays, mainly in major shipping routes. They are caused due to tanker collisions, illegal discharge from the ships, etc. An oil spill can be monitored and detected using various platforms such as vessel-based, airborne-based and satellite-based. Vessel based and airborne methods are expensive with less area coverage. This process also consumes more time. For ocean applications such as oil spill and Ship detection, optical sensors cannot image during bad weather. As SAR is an active sensor, weather independent, and has cloud penetrating capability, the images can be acquired during the day as well as at night. Radar Remote Sensing (RRS) has rapidly gained popularity for monitoring and detection of oil spills and ships for more than a decade. With the availability of the satellite images, detection of oil spill has improved due to its wide coverage and less revisit time. The present paper gives an overview of the methodologies used to detect oil spills on the SAR images using dual-pol Sentinel-1A Level 1 SLC data. This work clearly demonstrates the preprocessing steps of the Sentinel 1A data for oil spill detection. The oil spill was only visible in the VV channel, therefore, for ocean application VV channel image is preferred. SEASAT was the first space-borne SAR mission launched in 1978 by NASA to observe sea surface. The preprocessing was carried out at the European Space Agency (ESA), the Sentinel Application Platform (SNAP) toolbox and Envi 5.1 toolbox. Based on the Sigma naught values, oil spill can be discriminated with the ocean surface. The results obtained with the VV channel are satisfactory and one could map out the oil spill very well. Supervised classifiers SVM and NN were applied on the boxcar filtered 3 × 3 VV channel image to delineate the oil spill. The result of oil spill detection mapping is validated with Supervised SVM and Neural Network classifiers. The results show there is a good agreement between oil spill mapping and classified image using SVM and NN classified images. The Overall Accuracy (OA) obtained using SVM classifier is 98.13% with kappa coefficient as 0.95 and using NN classifier is 98.11% with kappa coefficients 0.95. This technique is considered to be a potential proxy for the detection and monitoring of Oil spills on water bodies. Application of SAR data for oil spill detection is considered to be first of its kind from Indian coasts. This study aims to detect the oil spill occurred due to collision of two LPG tankers with Sentinel-1A SLC data in Chennai coast area.
Asunto(s)
Contaminación por Petróleo , Petróleo , Contaminantes Químicos del Agua , Ecosistema , Monitoreo del Ambiente , India , Petróleo/análisis , Contaminación por Petróleo/análisis , Radar , Contaminantes Químicos del Agua/análisisRESUMEN
Characterizing the cell identity in a heterogeneous tissue is essential to the in-depth understanding of this sample. Existing single-cell techniques (e.g., flow cytometry or in situ cell florescent imaging) allow us to do so using the high/low signal of a combination of multiple signature molecules or even of a single marker. Recent advance of single-cell RNA-seq technology profiles the entire transcriptome of individual cells. Using a few marker genes to characterize cell type in this new technique is less reliable due to the high noise level and the dynamic transcription behavior. Nonetheless, the "noisy" but high-throughput transcriptome profiles provide adequate information to reveal the cellular identity and to understand the detail of the molecular characteristics. In this chapter, we will demonstrate a new method that is based on the supervised learning of the single-cell transcriptome profiles of many different known cell types. We will demonstrate how this technique solves the cellular identity problem.
Asunto(s)
Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Análisis de la Célula Individual/métodos , Regulación de la Expresión Génica , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Análisis de Secuencia de ARN , Programas Informáticos , Aprendizaje Automático SupervisadoRESUMEN
Predicting colorectal cancer (CRC) based on fecal microbiota presents a promising method for non-invasive screening of CRC, but the optimization of classification models remains an unaddressed question. The purpose of this study was to systematically evaluate the effectiveness of different supervised machine-learning models in predicting CRC in two independent eastern and western populations. The structures of intestinal microflora in feces in Chinese population (N = 141) were determined by 454 FLX pyrosequencing, and different supervised classifiers were employed to predict CRC based on fecal microbiota operational taxonomic unit (OTUs). As a result, Bayes Net and Random Forest displayed higher accuracies than other algorithms in both populations, although Bayes Net was found with a lower false negative rate than that of Random Forest. Gut microbiota-based prediction was more accurate than the standard fecal occult blood test (FOBT), and the combination of both approaches further improved the prediction accuracy. Moreover, when unclassified OTUs were used as input, the BayesDMNB text algorithm achieved higher accuracy in the Chinese population (AUC=0.994). Taken together, our results suggest that Bayes Net classification model combined with unclassified OTUs may present an accurate method for predicting CRC based on the compositions of gut microbiota.
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Bacterias/clasificación , Neoplasias Colorrectales/microbiología , Heces/microbiología , Microbioma Gastrointestinal , Tracto Gastrointestinal/microbiología , Anciano , Algoritmos , Bacterias/aislamiento & purificación , Técnicas Bacteriológicas , China , Neoplasias Colorrectales/diagnóstico , Femenino , Francia , Humanos , Masculino , Persona de Mediana Edad , Sangre Oculta , Valor Predictivo de las Pruebas , Pronóstico , Reproducibilidad de los Resultados , Medición de Riesgo , Factores de RiesgoRESUMEN
Resting state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive functions. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative.