Simulation and analysis of hot plume infrared signature based on SNB model

Numerical calculation of infrared emission from hot plume is of great significance for flight monitoring and detections. In this paper, the SNB (statistical narrow band) model established with parameters derived from the high-resolution spectral database HITEMP 2010 is used to perform the hot plume infrared signature simulations. Accuracy of the model is examined by the exact LBL (line by line) method, which proves the model’s reliability to predict radiative properties of combustion gases. In the application part, the SNB model is used to analyze infrared signatures of aircraft plumes cruising at different flight altitudes. The results show that cruising at a higher-altitude will obviously reduce the plume infrared emission. Besides, the plume infrared emissive energy mainly concentrates in a special wavenumber interval and can be strongly absorbed by atmosphere.

Analogue Rock Characterization with MicrOmega, within the H2020/PTAL project.

<p>The PTAL project [1] aims to build an Earth analogues database, the Planetary Terrestrial Analogues Library, to help characterizing the mineralogical evolution of terrestrial bodies, with a focus on Martian analogues (www.PTAL.eu). A set of natural Earth rock samples have been collected, compelling a variety of igneous and sedimentary rocks with variable compositions and levels of alteration. Those samples are characterized with thin section observations and XRD analysis, NIR spectroscopy, Raman spectroscopy and LIBS.</p><p>This abstract focuses on the NIR (Near Infrared) spectroscopy analysis performed using the MicrOmega instrument, a NIR hyperspectral microscope (e.g. [2]). The MicrOmega instrument used within the PTAL project is the spare model of the ExoMars rover laboratory. It has a total field of view of 5 mm x 5 mm, with resolution of 20 µm/pixel in the focal plane. It covers the spectral domain from 0.98 µm to ~3.6 µm. Its capabilities enable the identification of grains of different mineralogy in the samples [2].</p><p>Each MicrOmega observation produces >65,000 spectra, hence automatic analysis is needed as a first step. After data calibration, a quick-look data analysis based on a set of ~16 spectral parameters based on the detection of single or multiple absorption bands was performed to produce spectral indices maps and average spectra, then guiding the manual analysis in a second step. After spectral endmembers are identified, they are compared to reference spectral libraries to identify the presence of minerals species in the sample. Spectral parameter maps can then be used to map the extent of the identified mineral species on the surface of the sample. Final products of the analyses will feed the online PTAL spectral database, and a paper describing these analyses has recently been submitted to Astrobiology.</p><p>Mineral species detected with MicrOmega in the PTAL samples include: Olivine, High Calcium Pyroxene, Low Calcium Pyroxene, Amphiboles, Epidotes, Zeolites, Opals, Phyllosilicates, Oxides and Hydroxides, Carbonates, and Sulfates.</p><p>Preliminary<strong> </strong>comparisons with XRD and Raman analyses show general consistency in the identification of olivine, pyroxene and hydrated phases. As expected, quartz and plagioclase for example are challenging to be identified in NIR, but MicrOmega shows well the capacity in hydrated minerals identification and qualitative estimation of major and minor mineral species thanks to its spectral-imaging capabilities.</p><p>The PTAL spectral database will assist in particular in interpreting in situ data from the next Mars surface missions. The target-rocks in Oxia Planum and Jezero Crater, the landing sites of the next surface missions, have compositional similarities with some samples of the PTAL collection, in particular with the orbital identification of clay minerals and serpentine. The NIR spectrometers on board the rovers will be involved at multiple stages of the surface operations and will be crucial to understand the geologic history of each landing site, and in particular the context of the water alteration of the rocks.</p><p><strong>References:</strong> [1] Werner et al. (2018) Second International Mars Sample Return, No. 2071, 6060. [2] Pilorget and Bibring (2014) PSS 99, 7-18.</p><p><strong>Acknowledgements:</strong> This project is financed through the European Research Council in the H2020-COMPET-2015 program (grant 687302).</p>

MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. This is a challenging task as currently it is not clear how small molecules are fragmented in mass spectrometry. The existing approaches use the domain knowledge from chemistry to predict fragmentation of molecules. However, these rule-based methods fail to explain many of the peaks in mass spectra of small molecules. Recently, spectral libraries with tens of thousands of labelled mass spectra of small molecules have emerged, paving the path for learning more accurate fragmentation models for mass spectral database search. We present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by (i) utilizing an efficient algorithm to generate mass spectrometry fragmentations, and (ii) learning a probabilistic model to match small molecules with their mass spectra. We show our database search is an order of magnitude more efficient than the state-of-the-art methods, which enables searching against databases with millions of molecules. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that our probabilistic model can correctly identify nearly six times more unique small molecules than previous methods. Moreover, by applying molDiscovery on microbial datasets with both mass spectral and genomics data we successfully discovered the novel biosynthetic gene clusters of three families of small molecules.AvailabilityThe command-line version of molDiscovery and its online web service through the GNPS infrastructure are available at https://github.com/mohimanilab/molDiscovery.

MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. This is a challenging task as currently it is not clear how small molecules are fragmented in mass spectrometry. The existing approaches use the domain knowledge from chemistry to predict fragmentation of molecules. However, these rule-based methods fail to explain many of the peaks in mass spectra of small molecules. Recently, spectral libraries with tens of thousands of labelled mass spectra of small molecules have emerged, paving the path for learning more accurate fragmentation models for mass spectral database search. We present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by (i) utilizing an efficient algorithm to generate mass spectrometry fragmentations, and (ii) learning a probabilistic model to match small molecules with their mass spectra. We show our database search is an order of magnitude more efficient than the state-of-the-art methods, which enables searching against databases with millions of molecules. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that our probabilistic model can correctly identify nearly six times more unique small molecules than previous methods. Moreover, by applying molDiscovery on microbial datasets with both mass spectral and genomics data we successfully discovered the novel biosynthetic gene clusters of three families of small molecules. Availability: The command-line version of molDiscovery and its online web service through the GNPS infrastructure are available at https://github.com/mohimanilab/molDiscovery.