dataesr/scientific-paragraphs-categorization huggingface dataset:
The aim of this paper is to extend our analysis of the first torsionally excited state of methylamine using the "hybrid" approach. The paper is divided into five further sections. Section 2 describes the experimental details. Section 3 presents the spectral data used in the present fit. Section 4 describes the Hamiltonian theoretical model and BELGI-hybrid code that we used, including the nuclear quadrupole hyperfine structure and the intensity calculation. Section 5 presents the results of the search for t = 1 rotational transitions of methylamine in the ISM, and Sect. 6 gives a discussion of some spectroscopic issues connected to our current fitting results. The conclusions of this work are presented in Sect. 7.
2005) : 16 E hf (I, J, F) = 1 2 χ + J 2 b + J 2 c -2J 2 a - 1 2 χ -J 2 b -J 2 c + χ ab ⟨J a J b + J b J a ⟩ 2 f (I, J, F)/J(J + 1) (2) where χ + = -χ aa , χ -= χ cc -χ bb and f (I, J, F) is the Casimir function  (Gordy & Cook 1984). 25 ) Transitions (a)  1456 1132 rms (b) (MHz) 0. 0129 0. 0082 Notes. (a) Number of hyperfine transitions used in the fit. In this study, the number of transitions includes 324 t = 1-1 hyperfine components (b) Root-mean-square deviation of the hyperfine fit. in the rho axis system (a, b, c). To obtain hyperfine-free rotational transition frequencies, each hyperfine component was fitted to Eq. (  1 ). The linear least-squares Gauss-Newton algorithm was applied using a dedicated Python code. In the present study, we assigned 324 partially resolved hyperfine components of t = 1-1 rotational transitions.
Theoretical model and analysis The theoretical Hamiltonian model used in our fit is the so-called hybrid Hamiltonian, which allows us to deal with the two coupled large-amplitude motions present in methylamine, namely the internal rotation of the methyl group CH 3 and the backand-forth oscillatory inversion motion, or wagging, of the amine group NH 2. This approach has been tested in the ground torsional state of methyl-malonaldehyde  (Kleiner & Hougen 2015) , and also in our previous study of the ground and first excited state of methylamine  (Kleiner & Hougen 2020). As the hybrid model and the BELGI-hybrid code have been presented in detail elsewhere  (Kleiner & Hougen 2015 , 2020 ), here we only give a short summary of the approach. In the hybrid Hamiltonian, we mix two methods to deal with the coupling of two large-amplitude motions.
(4) The second method treats the back-and-forth (wagging) motion of the amino group NH 2 using the tunneling approach as described by  Ohashi & Hougen (1987). Those tunneling-type terms, which contain matrix elements associated with tunneling between the two positions of the amino group, do not contain any explicit form for the double-well tunneling potential. Instead H = T + V for the wagging motion is replaced with one tunneling splitting parameter plus higher-order torsion-rotation corrections. Here, we used a two-step diagonalization procedure similar to the one used in the one-top BELGI code  (Kleiner et al. 1992; Kleiner 2010 ). In the first step, we diagonalize the torsion-Krotation part of the matrix associated with the two first terms of the Hamiltonian given in Eq. (3). We separate the nondegenerate torsional-wagging-rotational (twr) states (of symmetry  The BELGI-hybrid code globally fits a large microwave, mil-45 limeter, and submillimeter dataset for 2790 t = 0-0, 2921 t = 46 1-1, and 40 t = 2 -2 pure rotational transitions, as well as the 47 far-infrared dataset involving 11643 t = 1 ← 0, 6155 t = 2 ← 48 1, and 5253 t = 2 ← 0 transitions up to J = 40.
The increase in the number of parameters is very reasonable considering that the number of lines increased by almost a factor of two. As detailed in  Kleiner & Hougen (2020) , the first column of Table  A. 1, labeled ntrw, contains four indices describing the order of the term with n = t + r + w, where t and r are the traditional torsional and rotational orders and w is the amino wagging order (see  Kleiner & Hougen 2020  for details). The second column gives the name of the molecular parameter as used in the BELGI-hybrid code and the operator that it multiplies. These operators must all be: (i) Hermitian, (ii) of species A 1 in the permutation-inversion group G 12  (Kleiner & Hougen 2015) , and (iii) invariant to time reversal. The third column gives numerical values for the molecular parameters obtained from the previous fit  (Kleiner & Hougen 2020) , and the fourth column gives the values of the parameters as obtained from the present fit described in Table  2 , together with their standard errors.
This difference in the labeling occurs dur-98 ing the procedure of assignment of the K a quantum number after 99 the diagonalization of the Hamiltonian matrix and reveals the 100 mixing of the eigenfunctions. The BELGI-hybrid code was modified to provide the line 103 strength calculations for methylamine transitions. The line 104 strength (S ) of the torsion-rotation transition between the level L ′ 105 containing all (2J ′ + 1) M ′ components and the level L contain-106 ing all (2J + 1) M components is given by the formula (Ilyushin 107 & Lovas 2007): 108 S (L ′ ; L) = 1 µ 2 M 3 g K ′ ,K=-J,J v ′′′ t ,v ′′ t =0,N t -1 C J ′ ,τ ′ ,v ′ t ,σ ′ ,w ′ K ′ ,v ′′′ t C J,τ, t ,σ,w K,v ′′ t × ⟨K ′ , v ′′′ t , σ ′ , w ′ | µ g |K, v ′′ t , σ, w⟩ × ⟨J ′ , K ′ , M| Φ Zg |J, K, M⟩ , 2 (6) where |K, v t , σ, w⟩ and |J, K, M⟩ are the torsion-rotation-inversion 109 and symmetric rotor basis functions used to set up the wagging-110 torsion-rotation Hamiltonian matrix for the second diagonal-111 ization step; the coefficients C J,τ,v t ,σ,w K,v ′′ t are the eigenvectors 112 coefficients obtained after diagonalization of the wagging-113 torsion-rotation Hamiltonian matrix; the Φ Zg represents direc-114 tion cosines, and µ g is the electric dipole moment along the 115 Fig.
Detection of CH 3 NH 2 in its first torsionally excited state One of us reported the detection of methylamine in its torsional ground state toward Sgr B2(N1S) in  Kisiel et al. (2022; see their Fig. A. 5 ). The analysis presented in this latter study used the spectroscopic entry 31008 (version 1) available in the Jet Propulsion Laboratory (JPL) spectroscopic database  (Pickett et al. 1998) , which is based on the combined fit by  Ilyushin et al. (2005). This entry does not account for the hyperfine structure of methylamine. However, we found that the spectral broadening due to the hyperfine structure is not negligible for some methylamine lines given the typical line width of 5 km s -1 (about 1. 7 MHz) toward Sgr B2(N1S). Therefore, here we used the spectroscopic entry 31802 (version 2021.
18 It is evident that we came up against problems in current analy-19 sis that prevented us from achieving a fit within the experimental 20 error for the available dataset. Indeed, as seen from Table  2 , even 21 for the torsional ground state, the weighted rms deviation for the 22 microwave data is 2. 38. The fact that we get greater rms deviation for the ground state in comparison with the previous fit with the tunneling approach is not surprising. This is because in the present study, we worked with a semi-global approach, which fits the data belonging to a number of torsional states with one set of parameters, and therefore the problems with fitting excited torsional states may also diminish the quality of fitting for the ground state. On the one hand, we are not able to rule out a situation where our current fit corresponds to some local minimum in vicinity of the global minimum in the functional space of Hamiltonian parameters.
Here, the fact that poorly fitted lines belong to high J and/or high K transitions -as mentioned above -may be considered as an argument in favor of this explanation. Although our search for the high-order Hamiltonian terms that could help to improve the situation was quite extensive, it was by no means exhaustive, because this search is very time consuming in view of the complexity of the Hamiltonian parameter space in the case of two coupled, large-amplitude motions. A further possible explanation for the remaining problems with fitting the available dataset is the strong influence of intervibrational interactions arising from low-lying nontorsion vibrations in the molecule. In methylamine, the third excited torsional state is already close in energy to the NH 2 wagging mode (see e. g. , Fig.
It may be that it will be impossible to get a fit within experimental error for the current dataset without building a new theoretical approach allowing us to explicitly take into account the intervibrational interactions arising from nontorsion vibrations. On the observational side, in Sect. 5. 2 we claim the detection of methylamine in its first torsionally excited state on the basis of five relatively uncontaminated lines. While this would be insufficient for the secure identification of a new molecule in the ISM, the situation here is different. We already presented a secure detection of methylamine in its torsional ground state toward Sgr B2(N1S), from which we derived LTE parameters 1  (Kisiel et al. 2022). The synthetic spectrum of methylamine in 2 its t =1 state computed with the same set of LTE parameters 3 was found here to be consistent with the observed spectrum 4 of Sgr B2(N1S).
For the intensity calculation we also had to take into 28 account the nuclear spin statistical weight (W st ), which is equal 29 to 1 for A 1 , A 2 , and E 2 species and to 3 for B 1 , B 2 , and E 1. 30 The calculated t = 1-1 rotational spectrum of methylamine 31 is available at the Lille Spectroscopic Database 4 (LSD) in two 32 versions: (1) pure rotational transition frequencies, and (2) a 33 rotational spectrum, taking the nuclear hyperfine structure into 34 4 https://lsd. univ-lille. fr/.
S1 ). We assume each step -i. e. SPE, NH + 4 oxidation by BrO -, and NO - 2 reduction by NaN 3 -to be total (i. e. all the NO - 2 is consumed): A measured × δ 15 N measured = A blank × δ 15 N blank + A azide × δ 15 N azide + A sample × δ 15 N sample , (4) where δ 15 N measured , δ 15 N blank , δ 15 N azide , and δ 15 N sample respectively represent the measured 15 N isotopic composition, that from the blank, that from the azide reagent, and that from the sample. In Eq. (  4 ), three unknowns stay: A blank , δ 15 N blank , and δ 15 N azide. The goal of the developed Python code is to determine this tuple, assuming it to be constant on all the samples of one batch. This code accounts for both a "size correction", i. e. the relative contribution of the blank to the sample size, and also for any isotopic fractionation that occurs during the N 2 O measurement through the "gold method" conversion of N 2 O into N 2  (Kaiser et al.
The best tuple is associated with the smallest residual error. δ 15 N scc = A measured × δ 15 N calibrated -δ 15 N azide 2 - A blank × δ 15 N blank A measured 2 -A blank (6) By comparing the calculated and measured blank isotopic compositions and sizes, one can check that the isotopic composition converges towards the blank with smaller sizes. Once the tuple is fixed, the mixing model is again reversed to correct and calibrate the samples. The Python scripts used for this correction are available as a separate link  (Lamothe et al. , 2023). This general correction, which considers blank size and calibration, will hereafter be referred to as the mixing model correction.
, 2021) , potentially explained by agricultural sources (livestock or fertilizers). However, this and other studies highlight the lack of broad understanding of isotopic variations over the rest of the year and difficulties in assigning diagnostic isotopic labelling to different emission pools  (Mariappan et al. , 2009; Felix et al. , 2017; Favez et al. , 2021). Individual climate or environmental events may also leave distinct signals in an ice core's NH + 4 record. In the Alps, for example, Saharan dust events are an important source of NH + 4 , and a single event can deliver high amounts of NH + 4 within a brief period. We captured one such dust event in the CDD ice core at 820 cm w. e. where the NH + 4 concentration reaches its maximum value of nearly 600 ppb (Fig. 7a ). Based on our chronology and supported by CFA data from terrigenous species (Mg 2+ , Ca 2+ , Al 3+ ) as well as dust concentration, this NH + 4 peak corresponds to a dust event on 30 April 2013.
Code availability. The scripts are available here: https://doi. org/10. 5281/zenodo. 7728983  (Lamothe et al. , 2023). Data availability. All the data mentioned in the article will be accessible in an open-access dataset still under construction, which will be named CDD16 after the Col du Dôme drilling campaign in 2016. When finalized, the link to this dataset will be found on the PANDA analytical platform website (https://panda. osug. fr/; PANDA, 2023). Until then, one can contact the authors, who will share the data. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-16-4015-2023-supplement. Author contributions. AL led the study, performed the laboratory measurements with the help of PG, LS, EG, and NC, and wrote the first draft with the help of PDA.
Acknowledgements. We acknowledge the technical support from the F2G ( French National Platform  for Coring and Drilling, handled by  INSU , the  MIUR -Ministry of Education, University and Research)  for the  CDD16  drilling. We also thank  Sarah Albertin  for scientific advice,  Selin Bagci  for lab handling, and  Sophie Darfeuil  for CFA colorimetric module development. Financial support. This research has been supported by  LabEx OSUG@2020  (" Investissements d'avenir"   -ANR10 LABX56 ), the  ANR  project  ANR-15-IDEX-02  ( IDEX-UGA ), the  Ice Memory programme , and the  Grenoble University Foundation (FUGA). Review statement. This paper was edited by  Pierre Herckes  and reviewed by two anonymous referees.
Marine imagery is a comparatively cost-effective way to collect data on seafloor organisms, biodiversity and habitat morphology. However, annotating these images to extract detailed biological information is time-consuming and expensive, and reference libraries of consistently annotated seafloor images are rarely publicly available. Here, we present the Antarctic Seafloor Annotated Imagery Database (AS-AID), a result of a multinational collaboration to collate and annotate regional seafloor imagery datasets from 19 Antarctic research cruises between 1985 and 2019. AS-AID comprises of 3,599 georeferenced downward facing seafloor images that have been labelled with a total of 615,051 expert annotations. Annotations are based on the CATAMI (Collaborative and Automated Tools for Analysis of Marine Imagery) classification scheme and have been reviewed by experts.
Methods The Antarctic Seafloor Annotated Image Database (AS-AID) includes downward facing images of the seafloor at a depth range between 100 -3,000 m from Antarctic scientific surveys for which we were able to obtain access to both images and detailed metadata records. We restricted the database to images deeper than 100 m because at these depths environmental conditions are more stable (e. g. , relatively less impact on the fauna from iceberg scour) and more comparable between regions, which suits the continental scale biodiversity analysis that this database has primarily been developed for. AS-AID focusses on downward facing seafloor imagery rather than oblique facing imagery (where cameras can be mounted at different angles) because initial work indicated that downward facing images are more comparable between different surveys, with greater uniformity of illumination and resolution.
Image sub-setting Annotating all images from all surveys is neither feasible nor desirable due to the large differences in sampling effort among surveys (i. e. , the density of images along the transect lines is highly variable). We therefore created a subset of the full dataset by selecting a spatially balanced random subset of the images using their coordinates and the distance along the transect line with the R package MBHdesign version 2. 2. 2. Where only one coordinate for the transect was available, or where recorded end-coordinates of the transects did not match with expected values given the duration of the deployment and the normal ship-speed during deployment (max 1 kn), we randomly selected 5 of the first 25 images in those transects and assigned these images to the starting position of the transect (Table  2 ).
Technical Validation Annotations in AS-AID have gone through a rigorous review process involving experienced scientists, trained students and taxonomists. During the annotation process, the annotation team (consisting of two scientists, one postgraduate student and one undergraduate student) met regularly (daily at the beginning, weekly at a later stage and every time a survey was fully annotated) to discuss uncertain annotations, ensure consistent annotation between the scorers and create new labels where necessary. All annotations were checked for correct labelling once a survey was fully annotated and the entire database was checked again after all surveys had been fully annotated. Further, expert taxonomists were consulted in particular for key taxa such as sponges, cnidarians and bryozoans.
The largest uncertainties in this database were distinguishing whether an object was an organism or substrate, and whether an organism was dead or alive, and we created a number of categories to label these cases accordingly, such as e. g. "Unknown Biological Matrix" or "Unidentifiable". In AS-AID, we have opted to use experts to consecutively perform tasks on the same set of images. In particular, this meant one expert labelled the images while a second expert checked the annotations in the review process and flagged any misclassifications for discussion. While this process didn't allow label accuracy to be quantified, it did allow organisms to be labelled more consistently throughout the entire database. Researchers are invited to assess the validity of AS-AID for their own research purposes.
Acknowledgements We thank the crews and onboard scientific parties of the expeditions which collected the data within this study for providing the images and metadata for analysis in this study, either directly or via online raw data archives. We thank  Amy Leventer  for helping with data acquisition and  Fanny Hermand  for her help during the early stages of this project. This research has been funded as part of  ARC Discovery  Project  190101858. C. G is supported by a  CSIRO- UTAS Quantitative Marine Science Scholarship. NZ involvement is supported by  Ministry of Business Innovation and Employment  programme  C01X1710 ,  Ross Sea Research and Monitoring Programme ; is the world's largest MPA effective?.
Mathematical tools for quantification of code uncertainties and sensitivities have been under development for many years, with a huge, accumulated experience in performing Uncertainty Quantifications (UQ) with numerical tools applied for the analysis of events of the safety level 1 to 3 (NEA/CSNI/R, 2016). So far, this is not the case for SA codes and only a few investigations have focused on SA and UQ  (Ghosh et al. , 2021; Chevalier-Jabet et al. , 2014). To address this gap, the Management and Uncertainties of Severe Accidents (MUSA) project was funded in HORIZON 2020 EURATOM NFRP-2018 call on "Safety assessments to improve accident management strategies for generation II and III reactor"  (Herranz et al. , 2021; Mascari, 2021). MUSA project aims at establishing a harmonized approach for the analysis of uncertainties and sensitivities associated with SA analysis among EU and non-EU entities.
Uncertainty analysis applications methodology and tools used The UaSA is carried out in the MUSA project by the probabilistic propagation of input uncertainties  (Ghosh et al. , 2021)  which is particularly suitable for code simulation applications. The method is based on the random sampling of different selected input uncertain parameters values, see Fig. 2. All UQ-tools used in MUSA have the capability for pure random (Monte Carlo) and Latin hypercube sampling (LHS). The set of sampled values is used for running several code calculations, in which all the input uncertain parameters are sampled based on their own PDF. After the simulations are performed with the SA code, the data of interest are extracted from the result files and written in files in the format required by the UQ-tool to be statistically post-processed.
Statistics indicators (e. g. minimum, maximum values, mean, standard deviation, cumulative density function, probability density functions, quantiles) and sensitivity functionalities (correlation coefficients, standard regression coefficients, coefficients of determination, scatter plots) are then provided by the UQ-tools. The UQ-tools used in MUSA-WP6 are listed in Table  2  as well as the SA codes. The coupling between the UQ-tool and the SA code as described in Table  II  was considered as functional at the beginning of the MUSA project since it had already been used. However, uncertainty applications carried out in MUSA-WP4 on the PHEBUS FPT1 test have put in light major challenges  (Mascari, et al. , 2022)  O. Coindreau et al. and characterization of the input uncertain parameters; 2) SA code and UQ-tool coupling in a powerful and flexible way (e.
In particular, the coupling by scripting between the SA code and UQ-tool and the automation of data extraction for the post-processing were very useful. The implementation in the cluster of the SA code and UQ-tool for those who have access to such a system has required some time but issues have been handled (for instance by adding libraries in the cluster). The first results of the UaSA have be obtained and led to the following main conclusions: • Concerning the uncertainty quantification, the results obtained must be interpreted with caution, due to the constrains for the application of order statistics that are not all met in our computations. In particular, only a limited number of u. p. have been considered. UQ has enabled us to quantify the spreading of the different SA codes when applied to a transient in a SFP.
3L ). The activation marker ICOS was significantly decreased in CD8 T cells (Supplementary Fig. 3M ). However, this decline only reflected the decrease in the frequency of CD8 TCM cells (Supplementary Fig. 3D ), as only the frequency of ICOS + CD45RO + , but not of ICOS + CD45RO -cells was lessened (Supplementary Fig. 3N ). We also observed a decreased expression of the amino acid transporter CD98 (Supplementary Fig. 3O ), nevertheless being independent of CD45RO expression levels (Supplementary Fig. 3P ). Together, these data support that CD8 T cells in early-to-mid stage iPD exhibit a terminally-differentiated but non-exhausted and non-senescent state. Effector CD8 T cells tend to migrate to the tissues where an active immune response occurs  38. Therefore, we also assessed the expression of several lymphocyte-trafficking relevant chemokine receptors, such as CXCR3, CCR4 and CCR6.
Since CD8 T cells and NKT are known to have cytotoxic potential and also secrete cytotoxic effector molecules, we asked whether a more-cytotoxic state was already reflected by serological parameters. To this end, we measured two of the most abundant extracellular cytotoxic effector molecules, granzyme A (GZMA) and granyzme B (GZMB), together with the membrane-pore forming molecule perforin. Highly encouragingly, circulating GZMA was significantly elevated in   iPD, especially in females (Fig. 2j ), while the levels of both GZMB and perforin were comparable between iPD and HC (Fig. 2k, l ). In short, our serological data also suggest a more-cytotoxic immune milieu in iPD. Although naive CD4 cells displayed no difference, the frequency of CD4 TCM cells was slightly, but significantly decreased (Supplementary Fig.