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search.filters.applied.f.access: openAccess × End date: 2023 × Start date: 2023 × Type: Article × Publisher: New York, NY [u.a.] : Elsevier ×

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    The HITRAN2020 molecular spectroscopic database
    (New York, NY [u.a.] : Elsevier, 2022) Gordon, I.E.; Rothman, L.S.; Hargreaves, R.J.; Hashemi, R.; Karlovets, E.V.; Skinner, F.M.; Conway, E.K.; Hill, C.; Kochanov, R.V.; Tan, Y.; Wcisło, P.; Finenko, A.A.; Nelson, K.; Bernath, P.F.; Birk, M.; Boudon, V.; Campargue, A.; Chance, K.V.; Coustenis, A.; Drouin, B.J.; Flaud, J.M.; Gamache, R.R.; Hodges, J.T.; Jacquemart, D.; Mlawer, E.J.; Nikitin, A.V.; Perevalov, V.I.; Rotger, M.; Tennyson, J.; Toon, G.C.; Tran, H.; Tyuterev, V.G.; Adkins, E.M.; Baker, A.; Barbe, A.; Canè, E.; Császár, A.G.; Dudaryonok, A.; Egorov, O.; Fleisher, A.J.; Fleurbaey, H.; Foltynowicz, A.; Furtenbacher, T.; Harrison, J.J.; Hartmann, J.M.; Horneman, V.M.; Huang, X.; Karman, T.; Karns, J.; Kassi, S.; Kleiner, I.; Kofman, V.; Kwabia-Tchana, F.; Lavrentieva, N.N.; Lee, T.J.; Long, D.A.; Lukashevskaya, A.A.; Lyulin, O.M.; Makhnev, V.Yu.; Matt, W.; Massie, S.T.; Melosso, M.; Mikhailenko, S.N.; Mondelain, D.; Müller, H.S.P.; Naumenko, O.V.; Perrin, A.; Polyansky, O.L.; Raddaoui, E.; Raston, P.L.; Reed, Z.D.; Rey, M.; Richard, C.; Tóbiás, R.; Sadiek, I.; Schwenke, D.W.; Starikova, E.; Sung, K.; Tamassia, F.; Tashkun, S.A.; Vander Auwera, J.; Vasilenko, I.A.; Vigasin, A.A.; Villanueva, G.L.; Vispoel, B.; Wagner, G.; Yachmenev, A.; Yurchenko, S.N.
    The HITRAN database is a compilation of molecular spectroscopic parameters. It was established in the early 1970s and is used by various computer codes to predict and simulate the transmission and emission of light in gaseous media (with an emphasis on terrestrial and planetary atmospheres). The HITRAN compilation is composed of five major components: the line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, experimental infrared absorption cross-sections (for molecules where it is not yet feasible for representation in a line-by-line form), collision-induced absorption data, aerosol indices of refraction, and general tables (including partition sums) that apply globally to the data. This paper describes the contents of the 2020 quadrennial edition of HITRAN. The HITRAN2020 edition takes advantage of recent experimental and theoretical data that were meticulously validated, in particular, against laboratory and atmospheric spectra. The new edition replaces the previous HITRAN edition of 2016 (including its updates during the intervening years). All five components of HITRAN have undergone major updates. In particular, the extent of the updates in the HITRAN2020 edition range from updating a few lines of specific molecules to complete replacements of the lists, and also the introduction of additional isotopologues and new (to HITRAN) molecules: SO, CH3F, GeH4, CS2, CH3I and NF3. Many new vibrational bands were added, extending the spectral coverage and completeness of the line lists. Also, the accuracy of the parameters for major atmospheric absorbers has been increased substantially, often featuring sub-percent uncertainties. Broadening parameters associated with the ambient pressure of water vapor were introduced to HITRAN for the first time and are now available for several molecules. The HITRAN2020 edition continues to take advantage of the relational structure and efficient interface available at www.hitran.org and the HITRAN Application Programming Interface (HAPI). The functionality of both tools has been extended for the new edition.
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    Comparison of bacteria in different metabolic states by micro-Raman spectroscopy
    (New York, NY [u.a.] : Elsevier, 2022) Shen, Haodong; Rösch, Petra; Thieme, Lara; Pletz, Mathias W.; Popp, Jürgen
    It was shown that several metabolic states of bacteria with various characteristics such as chemical composition participate in the formation of biofilms. To study the connections and differences among different bacterial metabolic states, five species of bacteria in exponential phase, stationary phase and biofilm have been compared and investigated by micro-Raman spectroscopy. The spectral differences between different metabolic states showed that the chemical composition varied among those metabolic states. Moreover, as can be shown by the spectral differences and principal components (PCs), different species and strains of bacteria behave differently. Furthermore, a principal component analysis (PCA) combined with support vector machines (SVM) was applied to distinguish species of bacteria within the same metabolic states. Our study provides valuable data for the comparison of bacteria between different metabolic states utilizing micro-Raman spectroscopy in combination with chemometrics models.
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    Trade-off for survival: Microbiome response to chemical exposure combines activation of intrinsic resistances and adapted metabolic activity
    (New York, NY [u.a.] : Elsevier, 2022) Adi Wicaksono, Wisnu; Braun, Maria; Bernhardt, Jörg; Riedel, Katharina; Cernava, Tomislav; Berg, Gabriele
    The environmental microbiota is increasingly exposed to chemical pollution. While the emergence of multi-resistant pathogens is recognized as a global challenge, our understanding of antimicrobial resistance (AMR) development from native microbiomes and the risks associated with chemical exposure is limited. By implementing a lichen as a bioindicator organism and model for a native microbiome, we systematically examined responses towards antimicrobials (colistin, tetracycline, glyphosate, and alkylpyrazine). Despite an unexpectedly high resilience, we identified potential evolutionary consequences of chemical exposure in terms of composition and functioning of native bacterial communities. Major shifts in bacterial composition were observed due to replacement of naturally abundant taxa; e.g. Chthoniobacterales by Pseudomonadales. A general response, which comprised activation of intrinsic resistance and parallel reduction of metabolic activity at RNA and protein levels was deciphered by a multi-omics approach. Targeted analyses of key taxa based on metagenome-assembled genomes reflected these responses but also revealed diversified strategies of their players. Chemical-specific responses were also observed, e.g., glyphosate enriched bacterial r-strategists and activated distinct ARGs. Our work demonstrates that the high resilience of the native microbiota toward antimicrobial exposure is not only explained by the presence of antibiotic resistance genes but also adapted metabolic activity as a trade-off for survival. Moreover, our results highlight the importance of native microbiomes as important but so far neglected AMR reservoirs. We expect that this phenomenon is representative for a wide range of environmental microbiota exposed to chemicals that potentially contribute to the emergence of antibiotic-resistant bacteria from natural environments.