dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of other systems on Spike protein-based comparisons A small number of the associated proteins were tracked into other resources for our reference PDB ID and those were mainly proteins of the same viral family, during the time of writing this article:  SARS-CoV Spike protein is included in the results of the new structural similarity search in RCSB PDB along with SARS-CoV Spike protein -Escherichia T4 viral protein chimeric entries.  SCOP and CATH databases also enlist only the associated Coronavirus proteins.  Murine Coronavirus Spike protein is listed together with other Coronavirus family proteins in the matching structures displayed for Spike protein entry in the Aquaria-COVID resource  52.  DALI server's results encompass Human Coronavirus family proteins and ACE2 receptor.
g. data irregularities). To our knowledge, Machaon is the first structural comparison methodology that combines these three features under two different settings: whole structure and mixed-alignment-informed segment scanning. This is linked with meta-analysis that can offer hints for biological properties via multiple secondary structure alignments. This module also interfaces with various wellestablished data sources and methods, enriching further the resulting output with extended comparisons to genomic, proteomic and chemical levels. The results are derived from the structural parts recorded into the PDB file. Thus, it is possible that many compared structures were incomplete and partially represented, a common fundamental obstacle found in these datasets. Data preparation is a resource and timeconsuming process that could insert biases.
Through Machaon, many of the above-mentioned limitations are overcome due to the fundamental principles of the methodology. More specifically, Machaon analyzes the experimental data in their raw form without any sort of preprocessing or refinement, such as treating missing residues. Also, each protein may participate with several structures in the dataset due to the incompleteness of the available data, as two different PDBs of the same protein might contain different parts of the structure in different conformation and resolution. This is desired since the ideal dataset for this kind of task would be to have information on different resolutions, conformations, protonation states, isoforms etc. The metrics appear to be robust against a degree of noise as demonstrated by the high overlap observed between the results for the raw and preprocessed version of Spike.
We thoroughly tested Machaon on public datasets and three individual tasks. The median TM-Score for whole structure comparisons in all tasks was well above 0. 17, accounting for a meaningful relationship between a pair of compared structures  28. The presented results confirm that the method operates beyond domains or folding classes and retrieves similar structures in a wider scope, balancing between secondary and tertiary structure similarities. Machaon's constrained searches offer a fine-grained scanning with a manageable performance cost, identifying alternative conformations of the same protein that have very low TM-Scores. This particular case attests the difficulty of structural search since there is no silver-bullet solution or indisputable ground truth. An alternative conformation of the same protein having a TM-Score lower than 0.
3433463411143332034145213302044, 3433423441242151343433312144  Protein secondary structure sequences: TT. HHHHHHHGGGGGGTHHHHHHHHHHHHHH, TT. HHHHHHHHHHHTTTTSS. HHHHHHH  The alignment of mixed representations (ASCII), mapping hydropathy cluster labels to secondary structure states: NO}$%'$%""7:9998K$%"%&#"-------$$!#!%% |||||. || ||. || ||. |. || NO}$%#$%----------%"#%#"PLNOUV}$$"#"%% Selecting the structurally similar candidates The candidates are filtered, discarding samples by PDB metadata section ('organism scientific', 'gene') and user-specified constraints. Depending on the dataset's total size, the 1% of the top rows is sampled for each metric and combined into a new set with duplicates removed. The samples are ordered by Borda Count rank aggregation algorithm and are clustered by HDBSCAN clustering algorithm  71  (min_samples = 5 and Euclidean distance metric) (Supplementary Fig.
11 ). The ordered sample set is traversed until a non-noisy sample is encountered, whose label determines the preferred cluster, preserving the maximum set between the top 15% of the previously ranked samples or the previously traversed samples. On a failed attempt the minimum cluster size is lowered until a pre-defined number of attempts is exceeded and the clustering result is discarded if the cluster size is below a specified threshold. Samples clustered with probability below 0. 1 are discarded as noise and the results are re-ranked and truncated either to maximum 800 top-ordered entries or to 250 entries if the dataset is non-redundant (option is configurable). All intermediate results are stored. Visualizations are generated using Uniform Manifold Approximation and Projection (UMAP) method displaying the clustered space of the results.
Enrichment and assessment of the selected candidate entries The resulting list of the selected proteins is enriched with information from UniProt's ID mapping offline resource and from RCSB PDB GraphQL, UniProt, NCBI Entrez and EBI QuickGo online web services. Duplicate protein or gene entries are discarded, keeping the entry with the highest order by the metrics. An evaluation module performs global sequence alignments, determines the sequence identity, computes the 3D similarity with a modified version of TM-Align and calculates chemical similarity for each of the proteins in the final sample. The alignments (match score=5/mismatch score=-4 similar to EDNAMAT/NCBI NUC 4. 2 scoring matrix, open gap penalty=-10, gap extension penalty=-0,5, no end-gap penalty) take place between 1D/2D partial data derived from PDBs (leaving a gap between non-consecutive residues), full 1D sequences from UniProt (matches/mismatching scoring by BLOSUM62 matrix  72  ) and RNA sequences from NCBI RefSeq.
Prediction of Spike protein's binding sites The SARS-CoV-2 S protein structure (PDB ID: 6VXX) was manually processed via the Schrodinger Maestro Suite with the Protein Preparation Wizard  73  removing ligands and water molecules, filling missing residues with Prime  74  , determining protonation states in physiological pH (7. 4) with PropKa  75  and minimized with OPLS force field  76  (Ramachandran and Janin plots: Supplementary Figures  13 14 ). The binding sites were predicted using the SiteMap module  77  (Supplementary Table  3 ).
Figures Figure  1:  The workflow of Machaon and its perspective on the protein structure. a)A protein of choice is assigned as a reference for the comparisons with a dataset. The metrics are computed independently between the features extracted for each protein in the dataset and the reference ones. For segment comparisons, there is an additional initial step where the segments are defined based on mixed sequence alignments, which represent similarity in secondary structure and protein sequence-derived hydrophobicity level. The final set of the candidate proteins is determined by ranking aggregation and clustering. There is an intermediate pruning step that samples top 1% entries per metric order in case of a large dataset. The final cluster is enriched with Uniprot/Gene Ontology data and evaluated in protein, transcript and chemical levels.
Code Availability Machaon's implementation and evaluation scripts are available to the community at: https://github. com/anastasiadoulab/machaon. The computed features on the PDB files in Spike protein structural comparisons are available at https://zenodo. org/record/6654658. Machaon was implemented in Python v. 3. 8 (https://www. python. org/) and uses the following packages: NumPy (https://github. com/numpy/numpy), pandas (https://github. com/pandasdev/pandas), SciPy (https://www. scipy. org/), scikit-learn (https://github. com/scikit-learn/scikitlearn), Matplotlib (https://github. com/matplotlib/matplotlib), seaborn (https://github. com/mwaskom/seaborn), BioPython (https://github. com/biopython/biopython), RDKit (https://github. com/rdkit/rdkit), Open3D (https://github. com/isl-org/Open3D), HDBScan (https://github.
com/scikit-learncontrib/hdbscan), ranky (https://github. com/Didayolo/ranky), DSSPParser (https://github. com/neolei/DSSPparser), Pebble (https://github. com/noxdafox/pebble), lxml (https://github. com/lxml/lxml), networkx (https://github. com/networkx/networkx), Plotly (https://github. com/plotly/plotly. py), UMAP (https://github. com/lmcinnes/umap), word_cloud (https://github. com/amueller/word_cloud), tqdm (https://github. com/tqdm/tqdm), beautiful soup (https://code. launchpad. net/beautifulsoup). A modified version of TM-align (https://zhanglab. dcmb. med. umich. edu/TM-align/, source code retrieved at 10/2/2021 ) is used by the evaluation model for 3D structure similarity computation. Secondary structures are determined by DSSP (https://github. com/PDB-REDO/dssp).
Data Availability The utilized UniProt ID mapping resources were retrieved in 21/9/2021 at: https://ftp. uniprot. org/pub/databases/uniprot/current_release/knowledgebase/idmapping and RefSeq resources were retrieved in 6/11/2021 at: ftp://ftp. ncbi. nlm. nih. gov/refseq/. RCSB PDB GraphQL (https://data. rcsb. org/graphql), UniProt (https://www. uniprot. org/uploadlists/, https://www. uniprot. org/uniprot/ACCESSION. xml/<ACCESSION-NUMBER>) and EBI QuickGo (https://www. ebi. ac. uk/QuickGO/services/ontology/go/terms/GO:<termid>) online services are used as a fallback method to retrieve required data which are not present in the local static data sources. The PDB dataset for the comparisons to Spike was retrieved in December 2020 and it was the query result for viral proteins in RCSB (https://www.
rcsb. org/docs/programmatic-access/batchdownloads-with-shell-script). The dataset containing PDB files of the Spike protein was obtained from the same source (RCSB). Benchmark datasets were obtained from https://github. com/rcsb/biozernike-validation for Task 1, http://shrec2018. drugdesign. fr/shape_retrieval_shrec2018_pdb_files. tar. gz , http://shrec2018. drugdesign. fr/SHREC2018_ref. cla for Task 2 and http://ekhidna2. biocenter. helsinki. fi/dali/pdb_and_scope. tar for Task 3.
Finite rotations What is missing in completing our formulation is the definition of the orthogonal matrix Λ and the computation of the bending strains ω. To replace the costly nine-parameter representation of a three-dimensional finite rotation by the orthogonal tensor in component form Λ i j = g i • a j , the four-parameter description by quaternions has gained wide acceptance, e. g. , see Ibrahimbegovic and Taylor  [37]. There is another reason for using it here. Given an iterative contribution to the incremental nodal rotations ∆w (i)  a , as in the commercial software package ANSYS, quaternions are convenient, as shown in Table  2. The bending strains also require a special computational procedure summarized in Table  3. We note again that we have applied the results of Ibrahimbegovic and Taylor  [37].
1. Retrieve ω (i) 2. ( ∆w (i) ) ′ = ∑ 2 a=1 N ′ a ∆w (i) a 3. T (i) = sin ∆w (i) ∆w (i) I + 1+cos ∆w (i) ∆w (i) 2 [ ∆w (i) ] × + ∆w (i) -sin ∆w (i) ∆w (i) 3 ∆w (i) ⨂ ∆w (i) ; ∆w (i) =   ∆w (i)   4. Λ (i) w = cos ∆w (i) I + sin ∆w (i) ∆w (i) [ ∆w (i) ] × + 1-cos ∆w (i) ∆w (i) 2 ∆w (i) ⨂ ∆w (i) 5. ω (i+1) = T (i) ( ∆w (i) ) ′ + Λ (i) w ω (i) 6. Store ω (i+1) Table  4  Global solution procedure in the commercial finite element software ANSYS through the user element (UEL) interface. 1. CALL subroutine Table 2 → E (i+1) = ( Λ (i+1) tot ) T ε (i+1) 2. CALL subroutine Table 3→ m (i+1) = Λ (i+1) tot C k ( Λ (i+1) tot ) T ω (i+1) 3. CALL subroutine Table 1→ n (i+1) = Λ (i+1) tot N (i+1) ; α (i+1) = Λ (i+1) tot A (i+1).
Observations SPIRou (SPectromètre InfraROUge,  Donati et al. 2020 ) is a fiber-fed near-infrared (0. 98-2. 51 µm) echelle spectropolarimeter installed on the 3. 6 m Canada France Hawaii Telescope in Maunakea (CFHT). It has a high spectral resolution of 70 000 with 1. 9 pixels per resolution element and a pixel sampling of 2. 3 km•s -1. SPIRou is fed by three fibers: fibers A and B for science with orthogonal polarization and fiber C for reference. SPIRou was already used for atmospheric studies (e. g. ,  Boucher et al. 2021; Pelletier et al. 2021) , which reported the presence of non white noise instrumental systematics that might be associated to modal noise for example  (Oliva et al. 2019). Data reduction and analysis are described in Sect. 3. Transit datasets of 13 exoplanets have been collected with SPIRou as part of the SPIRou Legacy Survey (SLS, PI: Donati) and various programs obtained through Canadian open time or collaborations (namely AU Mic b, GJ1214 b, GJ3470 b, HAT-P-11 b, HD1897333 b, K2-25 b, TOI-1728 b WASP-11 b, WASP-39 b, WASP-52 b, WASP-69 b, WASP-80 b, and WASP-127 b).
The polarimetry mode was used for the datasets collected on 2019 June 17 for AU Mic b, 2019 February 18 for GJ 3470 b, and 2020 July 03, 2020 July 05, 2020 July 25, and 2021 July 24 for HD 189733 b. However, we used the extracted data in the AB mode such that we do not differentiate between polarization (see  Sect. 3. 1 and Cook et al. 2022). We discard  PI: Donati)  due to the very low signal-to-noise ratio (S/N) obtained for each exposure and TOI-1728b (21BC14, PI: Allart) due to a mismatch of the transit window. Figure  1  highlights the targets observed with SPIRou in a planetary mass-irradiation diagram. The sample is mainly composed of hot to warm Jupiters (7) with some warm Neptunes (2), and mini-Neptunes (2) spanning a broad range of stellar ages and orbiting mainly K and M-type stars.
Data reduction, spectral extraction, and telluric correction All data were reduced using A PipelinE to Reduce  Observations (APERO; version 0. 7. 179; Cook et al. 2022) , the standard SPIRou data reduction software. APERO performs all calibrations and pre-processing to remove detector effects including dark, bad pixel, background, and performs detector nonlinearity corrections  (Artigau et al. 2018) , localization of the orders, geometric changes in the image plane, correction of the flat and blaze, hot pixel and cosmic ray correction, wavelength calibration (using both a hollow-cathode UNe lamp and the Fabry-Pérot étalon;  Hobson et al. 2021) , and removal of diffuse light from the reference fiber leaking into the science channels (when a Fabry-Pérot is used simultaneously in the reference fiber).
Target Program  is done using a combination of daily calibrations and reference calibrations. The result is an optimally extracted spectrum of dimensions 4088 pixels (4096 minus 8 reference pixels) with 49 orders, referred to as extracted 2D spectra; E2DS. While the E2DS are produced for the two science fibers (A and B) and the combined flux in the science fibers (AB), we only used the AB extraction as this is the relevant data product for nonpolarimetric observations. APERO also provides telluric-corrected versions of the spectra  (Artigau et al. , in prep. )  from both the absorption and emission of the Earth's atmosphere. The telluric absorption line correction done in APERO is a two-step process and is briefly outlined here. First, the extracted spectra of both science targets and a large set of rapidly rotating hot stars are fitted with an Earth's transmittance model from TAPAS  (Bertaux et al.
The measured excess absorption on the transmission spectrum is -0. 09 ± 0. 52%, consistent with no absorption. The 3-σ upper limit on the presence of helium is set at <1. 56% due to the observed systematics. At the difference of the our other datasets, the noise structures disappear and tend toward white noise for larger bin ( WASP-39 b is an inflated warm Neptune orbiting a late G-type star  (Faedi et al. 2011). It is one of the archetype exoplanets for atmospheric characterization and comparison due to its cloud-free high metallicity atmosphere  (Wakeford et al. 2018; Sing et al. 2016). Detections of water, carbon monoxide, carbon dioxide, and hydrogen sulfide have been reported with HST, Spitzer, and the newly launched JWST  (Wakeford et al. 2018 ; JWST Transiting Exoplanet Community Early Release Science Team 2023;  Rustamkulov et al.
Helium triplet The two time series cover the full transit with a baseline before and after transit. The strong component of the telluric OH line overlaps partially and completely with the helium triplet for the two nights (Fig. A. 1). We note that the telluric-corrected masterout of each night is not perfectly identical. This is likely due to the low S/N of the datasets. This also impacts the shape of the stellar helium line where it is shallower and broader for the first night. Nonetheless, the transmission spectra and helium light curves of both nights are in good agreement with each other. From the transmission spectroscopic map (Fig. 2 ), we can see that the before-transit and in-transit spectra have features in excess absorption across the spectral range. This is also captured by the helium light curve (Fig.
However, these regions are split between models with low and high mass-loss rate that both are within the data error bars. This contradiction can be explained by our choice of homogeneous Mass loss [g. Mass loss [g. analysis and fixed thermosphere radius to the Roche lobe. The metastable helium profiles for the models at high mass-loss rates have a large fraction of helium particles at altitudes higher than the Roche Lobe. Consequently, the quantity of metastable helium below this radius is sufficient to reproduce the nondetections observed. As described before, we do not consider these models as realistic and therefore used the 3-σ contour of the regions of the parameter space at a low mass-loss rate to set its upper limit (Table  4 ). However, it was not possible to derive upper limits for GJ 1214 b, and WASP-127 b as there is always one model at a given temperature that fit the data independently of the mass-loss rate.
However, these flux values are model-dependent and are subject to various assumptions such as the age of the system (see Sect. 3. 3. 2). We limited the search for correlation to the sample studied here because the data were obtained with the same instrument and reduced in a homogeneous way including the report of detections and upper limits, which differs in the literature from one paper to another. We note that a trend is noticeable with stellar age (not shown), but due to the lack of precision on the stellar age and the small number of targets in our sample, it is not possible to draw more conclusions. The top panels of Fig. 9  show the correlations for δR p /H with the stellar mass and the XUV flux, the two parameters showing the strongest trends. We note that the upper limit set for WASP-11 b is not constraining enough to be useful.
They can be caused by the low data quality (e. g. , close to readout regime), instrumental effects, reduction pipeline errors, or stellar variability like in the case of AU Mic b. Nonetheless, we set 3-σ upper limits which are as representatives A164, page 19 of 26 as possible of these systematics and assuming a given helium line width. We estimated the impact of stellar-pseudo signal on the observed helium features with a simple toy model. We concluded that none of the detections could solely be explained by such an effect, but that could contribute to some of the variability observed between transits and instruments. A more complex model would be needed to take into account the complexity of stellar surfaces and their occultation by planets combined with the intrinsic variability of the stellar flux in various wavelength domains.
To better understand the impact of stellar variability on the presence of metastable helium in exoplanet atmospheres, applying homogeneous analysis of multiple stellar and planetary tracers (e. g. , Na, H-α, He) as presented in  Guilluy et al. (2020) ;  Czesla et al. (2022)  will be necessary for the future. Instruments like CARMENES, GIANO simultaneously to HARPS-N, SPIRou simultaneously to ESPaDOnS (in a near future), and NIRPS simultaneously to HARPS will have an edge to disentangle multisource effects. Among the three detections, HD 189733 b is probably the one most impacted by stellar variability and requires specific analysis to properly extract the true planetary signature. However, HAT-P-11 b emerges as the best candidate to search for temporal planetary variability signature as it is completely free of stellar contamination, and variability in the CARMENES data  (Allart et al.
This could be improved by gathering higher quality datasets even for nondetections. Future studies and proposals could use as guidelines the δR p /H versus M ⋆ correlation to build robust science cases as it is the less modeldependent correlation but one should not forget that studying planets outside this soft spot can turn out to be as important. Finally, we want to draw attention to the necessity of building reproducible observations from the proposal phase taking into account weather losses to get robust results. This lack of reproducibility for helium studies is frequent and calls for more than one transit observation per target. In this context, the NIRPS consortium will observe over 5 yr more than 75 gasdominated planets with at least two transits for each of them as part of its Guaranteed Time Observations (GTO) program.
Acknowledgements. Based on observations obtained at the  Canada-France-Hawaii Telescope (CFHT)  which is operated from the summit of Maunakea by the  National Research Council of Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France , and the  University of Hawaii. The observations at the  Canada-France-Hawaii Telescope  were performed with care and respect from the summit of Maunakea which is a significant cultural and historic site. We thank the anonymous referee for their comments that improved the overall quality of this work. R. A. is a Trottier Postdoctoral Fellow and acknowledges support from the  Trottier Family Foundation. This work was supported in part through a grant from the  Fonds de Recherche du Québec -Nature et Technologies (FRQNT).
J. F. D. acknowledges funding from the  European Research Council (ERC)  under the  H2020 research & innovation programme  (grant agreement # 740651  NewWorlds). We acknowledge funding from the  French ANR  under contract number  ANR18CE310019  (SPlaSH). A. C. and X. D. acknowledge funding from the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Grenoble-Alpes University. A164 , page 23 of 26 Allart, R. , et al. : A&A, 677, A164 (2023).
Jungfraujoch architecture The key element of Jungfraujoch is an IBM IC922 server equipped with FPGAs and inference-grade GPGPUs, which receives, sorts, reduces and buffers data from the JUNG-FRAU detector. The task is achieved with a combination of FPGA design and software algorithms, with a minor part accelerated on a GPGPU. The IBM architecture was chosen for its superior memory bandwidth and the coherent Open-CAPI interconnect, which significantly reduces the complexity of integrating FPGA design with the software. While memory-bandwidth-demanding tasks of Jungfraujoch are handled on the IBM server (see Fig. 2 ), the system is designed with auxiliary services, like writer and broker, which can run either on the IBM server or on another machine connected via a fast network. For example, to ensure the compatibility of servers constituting a high-performance file-system cluster, file writing can be performed on an external node from the facility's preferred vendor.