dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUDING REMARKS The essential added-value of SulfAtlas is to propose a homology-based classification system for sulfatases (into families and subfamilies), allowing a better prediction of substrate specificities. To the best of our knowledge, there is no other database which maintains such a classification system for sulfatases. SulfAtlas mainly provides three types of service: (i) it centralizes the knowledge on these enzymes; (ii) it provides bioinformatic tools for mining sulfatases and (iii) it helps experimentalists in their choice of relevant candidate sulfatases to study. As we hoped in 2016, the SulfAtlas classification has been progressively adopted by the community, as a guide to indeed select candidates for biochemical characterization as shown by numerous recent studies  (18, (24) (25) (26) 28, 29, 32, 33) , but also as a useful tool for broader metagenomic analyses  (44) (45) (46) (47).
Particularly, the development of SulfAtlas HMM should facilitate and speed up such (meta)genomic studies, allowing a fast assignment of sulfatase sequences at the (sub)family level. Internally, this new tool has already proven to be invaluable to maintain the SulfAtlas database up-to-date. SulfAtlas HMM and our updated pipeline also pave the way for future expansion of Sul-fAtlas toward other families related to sulfate metabolism, most notably the sulfotransferases.
ACKNOWLEDGEMENTS We are grateful to  Dr Philippe Potin  for his support as  IDE-ALG program  coordinator. We thank  Dr Mirjam Czjzek  for her helpful discussions and  Dr Elizabeth Ficko-Blean  for her help with proof reading of the manuscript. FUNDING G. M. and M. S. acknowledge support from the  Agence National de la Recherche (ANR)  with regard to the ' Blue Enzymes'  project [ ANR-14-CE19-0020 ]; G. M. , P. L. , M. H. and E. C. are also grateful to  ANR  for its support with regards to the investment expenditure program  IDEALG  [ ANR-10-BTBR-04 ];  Roscoff Bioinformatics platform ABiMS  (http://abims. sb-roscoff. fr),part of the  Institut Francais de Bioinformatique  [ ANR-11-INBS-0013 ];  BioGenouest network. Funding for open access charge:  Agence Nationale de la Recherche.
The Massachusetts MPV E8L sequence was identical to the Zaire strain E8L sequence except for a single residue. Peptide Match software was used to identify pentapeptide subunits of E8L that are not found within the human proteome, thus addressing concerns about cross-reactivity with human proteins (such as CA3) and improving antigen speci city. Overall, 28 such pentapeptides were identi ed. The Immune Epitope Database was queried for experimentally-characterized epitopes/antigens containing these 28 pentapeptides, yielding 21 matching epitopes, demonstrating that some of these pentapeptides can evoke immune responses. This study identi ed human-foreign pentapeptides within E8L which could be explored to nd viable epitopes for MPV vaccines. While existing vaccines confer protection against MPV, the risk of vaccine escape (as seen with SARS-CoV-2) suggests that additional vaccines should be investigated.
Uniprot Sequence Retrieval The search query "monkeypox virus cell surface binding protein" was submitted to the online Uniprot database. The results were ltered to show reviewed entries only. Entry Q8V4Y0, cell surface-binding protein from gene E8L, was selected  [4]. The amino acid FASTA sequence was retrieved. The protein was isolated from strain Zaire-96-I-16 of the monkeypox virus.
BLAST and Multiple Sequence Alignment The Zaire strain's E8L amino acid sequence was submitted to NCBI BLAST with default settings to search for highly similar proteins  [8]. BLAST results were sorted by BLAST score. Percent identities for the selected BLAST hits ranged from 47. 4-95. 4%. The top twelve entries (including the Zaire strain's sequence) were submitted to Clustal Omega Multiple Sequence Alignment software to generate an alignment for the sequences  [9]. Perfectly conserved contiguous regions were identi ed, the longest being DDYGSNHL.
Draft Genome Sequence Translation And Analysis The complete draft genome of the monkeypox virus was sequenced and uploaded to the NCBI GenBank database on May 21, 2022, by Gigante et al  [10]. It was obtained from a con rmed monkeypox case in Massachusetts, USA. There are 197,124 base pairs and the genome was sequenced using the Oxford Nanopore device. The ID is ON563414. Link to the genome: https://www. ncbi. nlm. nih. gov/nuccore/ON563414 [10]  The FASTA sequence of the genome was obtained and submitted to the Expasy Translate tool to convert the DNA sequence to the protein sequence  [11]. Expasy Translate automatically identi ed and mapped all open reading frames. Due to it being extremely new, the FASTA sequence has not yet been annotated. In order to nd the region of DNA encoding the cell surface-binding protein E8L, the protein sequence was searched for DDYGSNHL, the oligopeptide found to be identical across all ten cell surface-binding proteins from the multiple sequence alignment.
Antigenicity Prediction The VaxiJen 2. 0 software was used to predict the antigenicity of the Massachusetts E8L based on the FASTA sequence  [12]. VaxiJen has over 80% accuracy at predicting antigenicity. The default threshold for antigenicity is set at 0. 4. VaxiJen predictions are based on auto-cross covariance transformation of input sequences into amino acid vectors.
Cross-Reactivity Search Against Human Proteome Peptide Match is a software from the Protein Information Resource that searches proteome databases for matches with provided protein or peptide sequences  [13]. First, Python code was written in Google Colab to split the cell surface-binding protein E8L sequence into overlapping k-mers of length ve, also known as pentapeptides. The list of k-mers was submitted to Peptide Match with the settings of (1) restricted to homo sapiens proteome (2) UniProt/SwissProt only (3) include isoforms (4) not only UniRef100 representative sequences only, and (5) treat leucine and isoleucine as different.
Results And Discussion The rst objective was to identify the amino acid sequence of ON563414's E8L protein which could be different from other monkeypox strains. We aimed to identify a highly conserved amino acid motif across monkeypox E8L proteins that could be used to locate ON563414 E8L. We started with an E8L amino acid sequence (Q8V4Y0) from the strain Zaire-96-I-16 which is well-annotated and documented on the Uniprot database. >sp|Q8V4Y0|CAHH_MONPZ Cell surface-binding protein OS=Monkeypox virus (strain Zaire-96-I-16) OX=619591 GN=E8L PE=2 SV=1 MPQQLSPINIETKKAISDTRLKTLDIHYNESKPTTIQNTGKLVRINFKGGYISGGFLPNEYVLSTIHI YWGKEDDYGSNHLIDVYKYSGEINLVHWNKKKYSSYEEAKKHDDGIIIIAIFLQVSDHKNVYFQ KIVNQLDSIRSANMSAPFDSVFYLDNLLPSTLDYFTYLGTTINHSADAAWIIFPTPINIHSDQLSKF RTLLSSSNHEGKPHYITENYRNPYKLNDDTQVYYSGEIIRAATTSPVRENYFMKWLSDLREACFS YYQKYIEGNKTFAIIAIVFVFILTAILFLMSQRYSREKQN Table  1 : Uniprot entry sequence for Q8V4Y0 cell surface-binding protein  [4]  There were 250 BLAST hits for Q8V4Y0, 19 reviewed and 231 unreviewed.
DDYGSNHL was successfully located within an open reading frame. The open reading frame (Fig. 4 ), highlighted in red, beginning with MPQQ and ending with EKQN) was compared to the Zaire strain cell surface-binding protein sequence. The sequences were perfectly identical except for a single residue. The nineteenth amino acid in the Zaire strain is threonine but it is alanine in the Massachusetts sequence. VaxiJen 2. 0 predicted Massachusetts E8L to be antigenic with a score of 0. 5316, well over the standard threshold of 0. 4. Next, the protein sequence was divided into all possible pentapeptides, or 5-mers, using Python. Three hundred pentapeptides were obtained. PeptideMatch software searched for these three hundred pentapeptides in the UniProt and SwissProt databases' human proteomes. For 272 pentapeptides, matches were found.
the IEDB found 21 epitopes containing at least one of the 28 pentapeptides. They came from ectromelia mousepox, vaccinia virus, Clostridium perfringens, and other pathogens. These epitopes are experimentally demonstrated to have immunogenic potential. Interestingly, two human epitopes appeared in the results. Supposedly, all human proteins should have been ltered out by the previous steps. It is possible that the human epitopes were not recorded in the UniProt and SwissProt databases.
Dynamo, a prototype of the "participatory" exhibition In 2014, based on a study of sixty-seven mobile applications from cultural institutions, Émilie Flon, Gaëlle Lesaffre and Anne Watremez 35 identified the major trends and "facilitation forms" that characterize them. These software tools were then grouped into families of functionality: "Display," "Audioguide," and "On-site facilitation. " Within this study set, only two mobile applications seemed to draw on "innovative" technologies in an experimental way: BlinksterCP (Musée National d'Art Moderne) and Dynamo. From April to July in 2013, the Dynamo exhibition allowed for experimentation through new digital media devices, provided by the telecom service provider Orange and the RMN -Grand Palais's digital department. The Dynamo exhibition made it possible to "develop a classic example of a participative application to be used before, during, and after the visit.
Following Dynamo, other mobile applications designed by the RMN -Grand Palais explicitly encouraged the public to share photographs. Several factors corroborate this. First, interface design: multiple mobile applications (Jean-Paul Gaultier, 2015; Hergé, 2016; Joyaux, 2017) included the presence of injunctive screen pages which encouraged sharing on social media platforms. Second, respondents' discourse: in these, the injunction to participate through photographic sharing circulated widely. In the following statement, the head of the digital division at the RMN -Grand Palais closely associates photography practice, the use of the participative module Photo bullée (the Hergé application), and the sharing of these photos on the Web and on social media: Concerning photography, through our application the public will be able to insert speech bubbles from famous comic books into their photos and bubble in, or "cartoonize" their photos, before sharing them.
This evolution goes hand in hand with a greater interest in the metrics and traces of participation than in the participation itself, concerning the same institutions. 47  According to Yves Citton, if the photographic policy within institutions has shifted from prohibition to active encouragement, it is also because institutions need these photographs to be taken, shared, and circulated. I argue that this need for "attentional traces" 48 contributes to designers' socio-technical representations of the visitors, whose reconfigured cultural practices push the institution to offer picture-sharing tools on social media platforms. These representations seem particularly applicable at the RMN -Grand Palais, even though the data collected on this subject by its Research and Marketing Team suggest the opposite.
We analyse Global Navigation Satellite System (GNSS) data from Svalbard to understand how uplift rates are controlled by the elastic and viscoelastic response of the solid Earth to changes in glacier mass on annual, interannual, decadal, centennial and millennial timescales. To reveal local patterns of deformation, we filter the GNSS time-series with an enhanced common-mode filtering technique where the non-tidal loading signal is incorporated. This technique reduces the estimated uncertainties for 5-yr time-series from 0. 8 to 0. 3 mm yr -1. Analysis of the GNSS data with different software-GAMIT, GipsyX, and GINS-produce consistent results that all indicate large temporal variations in uplift. For example, at the Ny-Ålesund GNSS station, uplift varies between 6 and 12 mm yr -1 for different 5-yr periods, and also shows a significant increase in the last 15 yr.
GNSS data and analysis We use data from five permanent GNSS stations in Svalbard (NYAL, NYA1, LYRS, SVES and HORN), and one station on Bear Island (BJOS) 240 km south of Svalbard (Fig. 1 ). All these stations are located close to existing settlements with power supply and communication infrastructure. We also use data from HAGN station, located at a nunatak in the middle of the glacier Kongsvegen, 30 km southeast of Ny-Ålesund. This station is powered by solar panels and batteries. In the dark season, data are recorded for 24 hr once a week to save power until the sun returns. Data are downloaded once a year in the spring during a glacier mass balance field campaign. Finally, we analyse data from three GNSS stations on the Norwegian mainland (TRO1, VARS and HONS) to help constrain the contributions from regional processes (e.
g. GIA). Data from the GNSS stations at Svalbard are analysed with the program packages GAMIT  (Herring et al. 2018) , GipsyX  (Bertiger et al. 2020)  and GINS  (Loyer et al. 2012). The GipsyX solution is directly realized in IGb14 (The International GNSS Service (IGS) realization of ITRF2014  (Altamimi et al. 2016 )) through the JPL GipsyX No-Net-Rotation (NNR) products. GINS uses the precise point positioning with integer ambiguity resolution mode (IPPP  Katsigianni et al. 2019 ) using MG3 orbit and clock products. MG3 is the IGS REPRO3 product computed by the Centre National d' Études Spatiales-Collecte Localization Satellites (CNES-CLS) team on behalf of the Géodésie Spatiale (GRGS). To ensure a good global realization in IGb14 of the GAMIT solution a global network of approximately 90 global IGS stations is analysed and combined with the Svalbard stations before transforming to IGb14.
Daily coordinate time-series are extracted from these solutions. For more details about the GAMIT and GipsyX analysis strategy, see  Kierulf et al. (2021b). More details on the MG3 products and the GINS software can be found in  Michel et al. (2021). The time-series are analysed with Hector  (Bos et al. 2008 ) software. We include the trend and annual and semi-annual harmonics in the time-series analysis. None of the stations used in this study had antenna changes during the study period, nor are there any known offsets. We assume that the temporal correlation in the time-series is a combination of white noise and power-law noise, and estimate the spectral index in the time-series analysis. The time-series for the GAMIT solutions of the stations used in this study are plotted in Fig. 2. The first data point is from January 1995 from the NYAL station.
Glacier elevation data We constrain recent ice melt on Svalbard from a series of digital elevation models (DEMs) representing Svalbard's ice surface between 1936 and 2018. The DEMs do cover all areas of Svalbard, which requires the usage of earlier time steps than 1995 to obtain a complete set of thickness changes to estimate PDIMs for all GNSS stations. All of the DEMs are constructed through stereo-photogrammetry using aerial photographs or satellite images  (Melvaer et al. 2014; Noh & Howat 2017; Geyman et al. 2022). If nothing else is explicitly stated, we refer to this ice model as our PDIM-model. In Section 3. 3 we also compare our measured uplift with elastic uplift estimated from the glacier surface elevation change model in  Hugonnet et al. (2021). For the seasonal variations in the glacier mass, we use the CMB model in  van Pelt et al.
2010 Our reference DEM for glacier change is constructed from aerial photographs acquired during a Norwegian Polar Institute (NPI) mapping campaign conducted from 2008 to 2012, with the bulk of the imagery obtained in the period 2009-2011. DEMs from these campaigns, which were created using the SOCET SET photogrammetry software, are complete for nearly all of Svalbard  (Melvaer et al. 2014) , with only a portion of southern Spitsbergen not yet available. In the latter area, we use elevation data from the ASTER GDEM product, a global compilation of stacked and filtered ASTER DEMs  (Nuth et al. 2013 ) made from images obtained in the period 2000-2013. The data are interpolated onto a 50-m grid (Fig. 3 ) in the UTM-33N coordinate system, and elevations are relative to the local geoid  (ArcGP-2006-sk)  defined by the NPI, as are the other DEMs in this paper.
1990 The 1990 ice elevation data are taken from the available archived 1990 NPI DEMs  (Melvaer et al. 2014) , and a 1996 DEM of the Austfonna ice cap made by combining differential SAR interferometry and ICESat laser altimetry  (Moholdt & Kääb 2013). In southern Spitsbergen, a hole in the archived 1990 NPI DEM is filled using 1961 and 1970 data, scaled using the assumption that the average mass loss rates from 1936 to 1961/1970 continued to 1990. In the remaining areas with no 1990 data, we add 2/3 of the 1936-2010 difference to the 1936 elevations, reflecting the fact that, elsewhere in Svalbard, roughly 2/3 of the 1936-2010 elevation change occurred in the interval 1936-1990, with the remaining 1/3 occurring from 1990-2010  (Geyman et al. 2022).
However, the combination of CM filtering and load removal reduces the amplitude of the annual signal from on average 5. 2 mm to 1. 6mm. For the GispyX and GINS software we see similar differences. However, for both solutions and especially the GINS solution the unfiltered time-series (H GNSS ) have a larger uncertainty. After filtering all the time-series are more or less at the same level for all solutions. Note the large improvements for the GINS solution after filtering of elastic loadings (H GNSS, L versus H GNSS ). This indicates that the larger uncertainties in the H GNSS reflect the true loading signal. By comparing different GNSS analysis strategies and loading models  Kierulf et al. (2021b)  concluded that there is 'an analysis strategy-dependent effect in the periodic signal'. This effect masks the loading signal in the unfiltered time-series.
Uplift in Svalbard filtered for PDIM Uplift values for PDIM in the different periods can be found in Table  A1. The measured uplift for the different software after removal of PDIM can be found in Table  A2. In Fig. 7  the differences between measured uplift and the uplift after correction of the elastic response due to PDIM are plotted. The results are differenced relative to the 2010-2018 rate in Table  4. In Fig. 7 (a) the residuals are not corrected for PDIM. If there were no time variations in the uplift, these should not significantly differ from zero. We see a clear increase during the 2010s in this figure. In Fig. 7 (b) the signals from our PDIM-model, are removed. We see that both the GAMIT solution and the GINS solution are not significantly different from zero in any time periods.
This confirms our hypothesis H, that the ongoing changes in glacier mass balance in Svalbard explain the observed varying interannual land uplift. The GipsyX solution for 2015-2018 (see Table  A2 ) shows a larger increase in uplift than can be explained by the PDIM, but for the other time periods the differences are within the uncertainty level. The reason for the discrepancy for GipsyX for this particular time period is unknown. In Fig. 7  The agreement between the uplift solutions from the three different GNSS software and the two PDIM-models is listed in Table  3. For each pair of GNSS solution and PDIM-model and for each time period we computed the root mean square (RMS) of the difference between measured uplift and the uplift due to PDIM. We do not have complete overlap in time of our PDIM-model and the PDIM-model of  Hugonnet et al.
Final results The uplift from the GAMIT solution and the PDIM-model for the 2010-2018 period is listed in Table  4  and ploted in Fig. A1. The horizontal velocities are included in Table  5. The period 2010-2018 is chosen because this is the longest period for which we have data from all the stations in the network coincident with an epoch of the PDIM-model. According to Table  3  all the GNSS solutions coincide with the variation present in the PDIM-model at the same level. The GipsyX solution gives too large uplift compared to the PDIM-model for 2015-2018. We therefore recommend the GAMIT or GINS solution for further studies. The differences between the GAMIT and the GINS solution is 0. 1 mm yr -1 for most stations and does not exceed 1σ (Table  A2 ). We therefore use the GAMIT solution in our analysis.
DATA AVA I L A B I L I T Y The GNSS data from Hornsund (HORN) is provided by the Institute of Geophysics of the Polish Academy of Sciences. GNSS time-series are available from the authors. The NTL time-series are available from http://loading. u-strasbg. fr/. Distributed time-series of climatic mass balance were extracted from the data set described in  van Pelt et al. (2019). The 1990 The and 2008 The -2012.
(Altamimi et al. 2017) are publicly available on the NPI website: https://publicdatasets. data. npolar. no/kartdata/S0Terrengmodell/Delmodell/. The 1936/1938 glacier reconstructions are available on the NPI website (https://ww w. doi. org/10. 21334/npolar. 2021. f6afca5c)and on Zenodo (https: //doi. org/10. 5281/zenodo. 5806388). Uplift and horizontal velocities for the GNSS stations in Svalbard. Horizontal velocities are relative to the ITRF2014 plate-motion model(Altamimi et al. 2017). Left-hand panel shows the GNSS observations. Middle panel is the residual velocity after removing the PDIM signal. Right-hand panel is the residual after removing PDIM, LIA and LGP. PDIM uplift signal for Svalbard stations for different time intervals. The PDIM signal is described in Section 2. 3. 1.
(2) On long time scales, CLIMP should enable building a merged CALIPSO+ATLID long-term lidar cloud product, in which the same clouds are detected despite the instrumental and orbital differences between ATLID and CALIOP. From this point of view, CLIMP should maximize consistency with GOCCP. The GOCCP+CLIMP long-term dataset should describe more than 20 years of cloud profiles at global scale, which will enable the study and evaluation in climate models of inter-annual variability in cloud profiles due to multi-annual climate variations (e. g. , El Nino, NAO, Madden-Julian oscillation). Its analysis will moreover make possible the detection of cloud changes because of human-induced climate warming, and their evaluation in climate model simulations. Therefore, CLIMP will be composed of two datasets named CLIMP-ST (short-term) and CLIMP-LT (long-term).
Both will mainly differ by their cloud detection threshold, as we will see later in the text. This threshold is parameterized in COSP/lidar and can be easily changed when comparing simulated data to CLIMP-ST and CLIMP-LT. The CLIMP product and algorithm inherit from the approach developed for CALIPSO-GOCCP. This algorithm processes L1 data in exactly the same way as the COSP lidar simulator does. GOCCP is part of the CFMIP-OBS database included in Obs4Mips  (Waliser et al. , 2020)  for model evaluation. Differences between GOCCP, NASA, and JAXA CALIOP cloud products were documented in  Chepfer et al. (2013)  and  Cesana et al. (2019). The three key elements of the GOCCP algorithm, which need to be kept when developing CLIMP, are: (i) lidar profiles are not averaged horizontally before cloud detection (1) to keep consistency with the subgridding module SCOPS  (Klein and Jacob, 1999)  included in COSP which is required to respect the Eulerian framework of climate model simulations, and (2) to avoid overestimate the cloud fraction in shallow clouds (e.
This threshold needs to be constant over a full dataset and cannot be scene-dependent. The structure of the article is as follows. In Section 2, we briefly describe the differences and similarities between ATLID and CALIOP, the formalism necessary to understand the analysis presented in the next sections, and the cloud variables used in this study. Section 3 describes the physical elements that matter for the development of CLIMP-ST. Using synthetic cloud scenes (3. 1) and a numerical chain which simulates lidar profiles observed by CALIPSO and ATLID over the cloud scenes at full spatial resolution and instantaneous time scales (3. 2), we define the cloud detection scheme of. In this part of the study, we try to answer whether ATLID might observe optically thinner cloud in day time than CALIOP at full horizontal resolution, a useful capability to evaluate the description of cirrus in climate models.