Search results for "IgE"
showing 10 items of 17476 documents
Room-Temperature Phosphorescence and Efficient Singlet Oxygen Production by Cyclometalated Pt(II) Complexes with Aromatic Alkynyl Ligands
2020
The synthesis of five novel cyclometalated platinum(II) compounds containing five different alkynyl-chromophores was achieved by the reaction of the previously synthesized Pt–Cl cyclometalated compound (1) with the corresponding RC≡CH by a Sonogashira reaction. It was observed that the spectral and photophysical characteristics of the cyclometalated platinum(II) complexes (Pt–Ar) are essentially associated with the platinum-cyclometalated unit. Room-temperature emission of the Pt–Ar complexes was attributed to phosphorescence in agreement with DFT calculations. Broad nanosecond (ns)-transient absorption spectra were observed with decays approximately identical to those obtained from the emi…
Analysis of salivary detection of P16INK4A and RASSF1A promoter gene methylation and its association with oral squamous cell carcinoma in a Colombian…
2019
Background Epigenetic factors play a fundamental role in the etiopathogenesis of oral squamous cell carcinoma (OSCC). This study evaluated if salivary detection of P16INK4A/RASSF1A gene promoter methylation might be linked to the clinical/histological features of OSCC in a Colombian population. Material and Methods Methylation-specific polymerase chain reaction (MSP-PCR) was used to detect the methylation frequency of P16INK4A/RASSF1A genes in DNA obtained from whole saliva collected of 40 healthy controls (HC) and 43 OSCC patients. Determination of the clinical performance of MSP-PCR assay was based on standard algorithms derived from two-way contingency table analysis. The association of …
A novel comprehensive procedure for estimating greenhouse gas emissions from water resource recovery facilities
2017
The emissions of the major greenhouse gases (GHGs), i.e. carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from water resource recovery facilities (WRRFs) are of increasing concern in the water industry. In order to produce useful and comparable information for monitoring, assessing, and reporting GHG emissions from WRRFs, there is a need for a generally accepted methodology for their quantification. This paper aims at proposing the first protocol for monitoring and accounting for GHG emissions from WRRFs, taking into account both direct and indirect internal emissions and focusing the attention on plant sections known to be primarily responsible for GHG emissions (i.e. oxidation…
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
2021
Funding Information: We are thankful to the GlobBiomass project team and Frank Martin Seifert (ESA) for valuable suggestions and stimulating scientific discussions. We are thankful to Takeo Tadono (JAXA EORC), Masato Hayashi, (JAXA EORC), Kazufumi Kobayashi (RESTEC), Åke Rosenqvist (soloEO), and Josef Kellndorfer (EBD) for support with the use and interpretation of the ALOS PALSAR mosaics. Support by the CCI Land Cover project team, in particular Sophie Bontemps (UCL), is greatly acknowledged. The help from Martin Jung (MPI-BGC) in feature selection and Ulrich Weber (MPI-BGC) for data processing for the GSV-to-AGB conversions is greatly acknowledged. Forest inventory data for the validation…
Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3
2012
Abstract ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from …
Optimized Class-Separability in Hyperspectral Images
2016
International audience; Image visualization techniques are mostly based on three bands as RGB color composite channels for human eye to characterize the scene. This, however, is not effective in case of hyper-spectral images (HSI) because they contain dozens of informative spectral bands. To eliminate redundancy of spectral information among these bands, dimensionality reduction (DR) is applied while at the same trying to retain maximum information. In this paper, we propose a new method of information-preserved hyper-spectral satellite image visualization that is based on fusion of unsupervised band selection techniques and color matching function (CMF) stretching. The results show consist…
Recent Advances in Techniques for Hyperspectral Image Processing
2009
International audience; Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in …
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
2019
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
Edge-Based Missing Data Imputation in Large-Scale Environments
2021
Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis
Multioutput Automatic Emulator for Radiative Transfer Models
2018
This paper introduces a methodology to construct emulators of costly radiative transfer models (RTMs). The proposed methodology is sequential and adaptive, and it is based on the notion of acquisition functions in Bayesian optimization. Here, instead of optimizing the unknown underlying RTM function, one aims to achieve accurate approximations. The Automatic Multi-Output Gaussian Process Emulator (AMO-GAPE) methodology combines the interpolation capabilities of Gaussian processes (GPs) with the accurate design of an acquisition function that favors sampling in low density regions and flatness of the interpolation function. We illustrate the promising capabilities of the method for the const…