dataesr/scientific-paragraphs-categorization huggingface dataset:
Extragalactic redshifts The redshifts of extragalactic objects are produced by two modules, QSOC and UGC, which analyse BP/RP spectra of quasars and galaxies, respectively. The selection of the processed sources uses the Combmod class Fig. 24. HR diagrams colour-coded by FLAME parameters. Note that the colour-scale is linear and not a density plot. Top: lum_flame versus teff_gspphot colour-coded by evolstage_flame for stars with relative parallax errors better than 10%. We have applied the recommended FLAME filter for giants. Lower: lum_flame_spec versus teff_gspspec colour-coded by age_flame_spec for stars with flags_gspspec like '0000000000000%'. In the background the red clump can be seen in grey. These have no age values associated with them. Even though we have made selections on quality on certain parameters, there are still some artefacts that can be seen, such as the high-luminosity, low T eff giants in the upper panel colour-coded by yellow, or the high-luminosity low mass main sequence stars in the lower panel.
These artefacts can be removed by filtering on luminosity uncertainty, or requiring that the T eff from both GSP-Spec and GSP-Phot agree to e. g. 300 K, or filtering on spectra SNR. probabilities of DSC, classprob_dsc_combmod_quasar and classprob_dsc_combmod_galaxy. The CU8 extragalactic parameters are found in the qso_candidates and galaxy_candidates tables. More details on the quality and processing of these parameters are given in the accompanying Paper III  (Delchambre 2022). QSOC predicts quasar redshifts redshift_qsoc and associated confidence levels redshift_qsoc_lower and redshift_qsoc_upper in the range 0. 0826 < z < 6. 1295. Intentionally, the module chose to be complete and produced results on 6. 4 million sources, which is three times the expected number of quasars that Gaia should theoretically observe.
This repeated validation was done on a module-by-module basis for a limited number of mostly random sources  (10 million). The teams compiled many validation tests to ensure that the software performed as intended. Such tests comprise checking the astrophysical content of the data, e. g. HR diagrams such as Fig. 24 , which helped to point out weaknesses in the codes in certain parameter spaces. Comparisons with external data allowed the teams to check whether their results are consistent with what is already known in the literature. It is important to point out, however, that Apsis does not do any calibration of its APs to mimic external catalogues. The external catalogues were merely used as a consistency check. Once the final input data had been received (six months before operations), further test runs were performed to refine the codes and to adapt parameter settings to the final data.
A higher level of validation was performed using a validation database hosted at the ESAC operations center in Madrid. This allowed the CU8 team to perform many cross-checks between the individual parameters, e. g. Fig. 14 , and provided important feedback for necessary modifications to the code before the full operations sequence. It also allowed a statistical check on the full dataset, which then allowed the post-processing codes to be prepared for filtering results and setting archive flags. A third level of validation was then performed by the Coordination Unit 9 (CU9) archive team once the operational data had been delivered. In essence they were the first users of the data, and had an external view of the full results in the archive just as a user would, see  Babusiaux et al. (2022).
Caveats and known issues There are several caveats that the user should be aware of before using the data. Additionally, a number of issues have been found following extensive validation. We list both caveats and the main issues known to us at the time of writing, starting with general comments on variability and crowding, followed by a discussion on a module-by-module basis. The user should consider these issues when using the APs in Gaia DR3. Variability: Apsis processes the mean BP/RP and RVS spectra, astrometry, and mean magnitudes provided by upstream processing systems. Therefore, we advise users to consider the variability of their source before deciding whether the APs from Apsis are adapted to their specific science case. As a concrete example, RR Lyrae stars have large amplitude variability, and a mean spectrum for these stars will not necessarily represent the mean state of such a star.
Conclusions Gaia DR3 contains one of the most extensive catalogues of astrophysical parameters to be exploited by the community, and it is based on Gaia-only data. It contains valuable information on stellar and non-stellar sources, and these parameters appear in ten main archive tables. A minor subset of APs also appear in gaia_source to simplify querying for a new user of ADQL. There are up to 1. 6 billion classifications of objects (star, galaxy,. ), along with 470 million stellar-based APs and 6 million extragalactic redshifts of quasar (6M) and galaxy (2M) candidates, along with a self-organising map of outliers, total Galactic extinction maps and MCMC samples. All of these were produced by the Apsis analysis system (Sect. 3). Stellar-based analyses were performed using general methods by the Apsis modules GSP-Phot and GSP-Spec which derived the spectroscopic parameters (T eff , log g,.
Caveats and known issues with the catalogue are summarised in Sect. 8. Further information on the quality, validation and use of the data is provided in the accompanying Papers II and III which target the stellar parameters and the non-stellar content, respectively  (Fouesneau 2022; Delchambre 2022). More technical details are available in the online documentation. Dedicated papers focusing on specific Apsis modules are also available, for GSP-Phot (Andrae 2022), GSP-Spec (Recio-Blanco 2022b), ESP-CS (Lanzafame 2022) and QSOC  (Delchambre 2018). In addition to these data, several tools are made available to the community to aid their exploitation, see Appendix E. Examples of the use and performance of the astrophysical parameters are shown in several papers accompanying Gaia DR3. Bailer-Jones (2022) explores the extra-galactic content of Gaia, by combining results from Apsis with other Gaia results.
Chemin); -the National Natural Science Foundation of China (NSFC) through grants 11573054, 11703065, and 12173069, the China Scholarship Council through grant 201806040200, and the Natural Science Foundation of Shanghai through grant 21ZR1474100; -the Tenure Track Pilot Programme of the Croatian Science Foundation and the École Polytechnique Fédérale de Lausanne and the project TTP-2018-07-1171 'Mining the Variable Sky', with the funds of the Croatian-Swiss Research Programme; -the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010 and INTER-EXCELLENCE grant LTAUSA18093, and the Czech Space Office through ESA PECS contract 98058; -the Danish Ministry of Science; -the Estonian Ministry of Education and Research through grant IUT40-1; -the European Commission's Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission's Seventh Framework Programme through grant FP7-606740 (FP7-SPACE-2013-1) for the Gaia European Network for Improved data User Services (GENIUS) and through grant 264895 for the Gaia Research for European Astronomy Training (GREAT-ITN) network; -the European Cooperation in Science and Technology (COST) through COST Action CA18104 'Revealing the Milky Way with Gaia (MW-Gaia)'; -the European Research Council (ERC) through grants 320360, 647208, and 834148 and through the European Union's Horizon.
Acknowledgements. We thank the referee for their careful reading of the paper and for providing constructive feedback. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. Acknowledgments from the financial institutions are in Appendix F. We thank our DPAC colleague  Hector Canovas  for providing Python routines to download spectra and MCMC samples. The data analysis made use of  Vaex (Breddels & Veljanoski 2018 ), TOPCAT (Taylor  2005 ), and R (R Core Team 2013).
2020 research and innovation and excellent science programmes through  Marie Skłodowska-Curie  grant  745617  ( Our Galaxy  at full HD -Gal-HD) and 895174 (The build-up and fate of selfgravitating systems in the Universe) as well as grants  687378  ( Small Bodies :  Near and Far) ,  682115  ( Using the Magellanic Clouds to Understand the Interaction of Galaxies ), 695099 (A sub-percent distance scale from binaries and  Cepheids -CepBin) ,  716155  ( Structured ACCREtion Disks -SACCRED), 951549 (Sub-percent calibration of the extragalactic distance scale in the era of big surveys -UniverScale ), and  101004214  ( Innovative Scientific Data Exploration and Exploitation Applications for Space Sciences -EXPLORE ); -the  European Science Foundation (ESF) , in the framework of the  Gaia Research for European Astronomy Training Research Network Programme (GREAT-ESF ); -the  European Space Agency (ESA)  in the framework of the  Gaia  project, through the  Plan for European Cooperating States (PECS)  programme through contracts  C98090  and  4000106398/12/NL/KML  for Hungary, through contract  4000115263/15/NL/IB  for Germany, and through  PROgramme de Développement d'Expériences scientifiques (PRODEX)  grant  4000127986  for Slovenia; -the  Academy of Finland  through grants  299543 ,  307157 ,  325805 ,  328654 ,  336546 , and  345115  and the  Magnus Ehrnrooth Foundation ; -the  French Centre National d'Études Spatiales (CNES) , the  Agence Nationale de la Recherche (ANR)  through grant  ANR-10-IDEX-0001-02  for the ' Investissements d'avenir' programme , through grant  ANR-15-CE31-0007  for project ' Modelling the Milky Way in the Gaia era ' (MOD4Gaia), through grant  ANR-14-CE33-0014-01  for project 'The  Milky Way disc formation in the Gaia era ' (ARCHEOGAL), through grant  ANR-15-CE31-0012-01  for project ' Unlocking the potential of Cepheids as primary distance calibrators' (UnlockCepheids ), through grant  ANR-19-CE31-0017  for project ' Secular evolution of galxies ' (SEGAL), and through grant  ANR-18-CE31-0006  for project ' Galactic Dark Matter' (GaDaMa ), the  Centre National de la Recherche Scientifique (CNRS)  and its  SNO Gaia of the Institut des Sciences de l'Univers (INSU) , its  Programmes Nationaux: Cosmologie et Galaxies (PNCG) ,  Gravitation Références Astronomie Métrologie (PNGRAM) ,  Planétologie (PNP) ,  Physique et Chimie du Milieu Interstellaire (PCMI) , and  Physique Stellaire (PNPS) , the ' Action Fédératrice Gaia' of the Observatoire de Paris , the  Région de Franche-Comté , the  Institut National Polytechnique (INP)  and the  Institut National de Physique nucléaire et de Physique des Particules  ( IN2P3 ) co-funded by  CNES ; -the  German Aerospace Agency (Deutsches Zentrum für Luft-und Raumfahrt e.
Abbreviations ACN, acetonitrile; ADAM, A Disintegrin And Metalloprotease; CID, collision-induced dissociation; FDR, false discovery rate; FRET, fluorescence resonance energy transfer; GST, Glutathione S-Transferase; HRP, Horseradish Peroxidase; KO, Knockout; LC, liquid chromatography; MMP, matrix metalloproteinase; MS, mass spectrometry; PASEF, parallel accumulation-serial fragmentation; PDB, protein data bank; QTOF, Quadrupole Time-Of-Flight; SLE, systemic lupus erythematosus; TBST, Tris-buffered saline with 0. 1% Tween 20 Detergent; THD, TNF homology domain; TIMS, trapped ion mobility spectrometry; TM, transmembrane; TMB, 3,3 0 5,5 0 -tetramethylbenzidine; TNF, tumour necrosis factor; TOF, Time Of Flight; WT, wild-type. Over the past 20 years, several studies reported nonapoptotic signalling by the CD95L-CD95 system  [2, 3].
Cleavage site were detected by database driven peptide identification and chromatograms were extracted from identified ions (Fig. 5B , Fig. S2a, b ). All cleavage fragments detected by the QTOF (corresponding to sites 106 HL 107 , 113 EL 114 , 142 EL 143 , 148 HL 149 ) were confirmed by the TimsTOF Pro (Fig. 5C , Fig. S2a , Table  S2b ). In addition, analyses on the TimsTOF Pro allowed us to identify seven additional fragments, corresponding to sites 103 QL 104 , 104 LF 105 , 109 KE 110 , 110 EL 111 , 126 SL 127, 138 PE 139 , 139 EK 140 (Fig. 5C , Table  S2b ). Chromatographic profiles of the samples obtained from MMP-treated or untreated samples were compared, and the peaks exclusively detected in MMP-treated sample profiles were identified as resulting from MMP-mediated cleavage products.
P1 and P1 0 positions correspond to the pair of amino acids between which the cleavage occurs. For DeepCleave, on the other hand, the threshold was set at 90% confidence, since this in silico tool performs more sensitive predictions including 8 amino acids around the cleavage site (P4 to P4 0 ). The PROSPER instrument offers a general overview of the predicted cleavages for the metalloprotease family. Finally, Proteasix summarizes the cleavage sites described to date in the MEROPS peptidase database  [74]. All this information has been added and the selection of  13.
Sequence alignment Sequences alignment of CD95L (UniProt-P48023) and TNF (UniProt-P01375) ectodomains was performed using the CLUSTAL OMEGA 1. 2. 4 software. The similarity scores were calculated according to the Gonnet PAM 250 matrix. The "*" indicates fully conserved residues, the ":" indicates conservation between groups of strongly similar properties (scoring > 0. 5). The ". " indicates conservation between groups of weakly similar properties (scoring ≤ 0. 5).
TIMS, MS operation and PASEF were controlled and synchronized using the control instrument software OTOFCONTROL 5. 1 (Bruker Daltonik). LC-MS/MS data were acquired using the PASEF method with a total cycle time of 1. 31 s, including 1 TIMS MS scan and 10 PASEF MS/MA scans. The 10 PASEF scans (100 ms each) contain, on average, 12 MS/MS scans per PASEF scan. Ion mobility-resolved mass spectra, nested ion mobility versus m/z distributions, as well as summed fragment ion intensities were extracted from the raw data file with DataAnalysis 5. 1 (Bruker Daltonik GmbH, Bremen, Germany). Generated spectra were analysed with the Mascot database search engine (v2. 5. 1; http://www. matrixscience. com) for peptide identification, using its automatic decoy database search to estimate a false discovery rate (FDR) and calculate the threshold at which the e-values of the identified peptides were valid.
According to the samples, the spectra were queried against a database containing the 5 peptides sequences (MS1, MS2, MS3, MS4, MS5). Mass tolerance for MS and MS/MS was set at 15 ppm and 0. 05 Da respectively. No enzyme was selected. Identified peptides are validated with a score ≥ 30 at peptide spectrum match level, using PROLINE STUDIO v2. 0. 1 software  [75]  and chromatograms were extracted in Skyline-daily version 21. 1. 1. 223 (University of Washington). Samples intended for analysis by QTOF underwent first a lyophilization step for 48 h. Processed samples were quickly frozen using liquid nitrogen and placed in the Alpha 2-4 LSC basic freeze dryer (Martin Christ Gefriertrocknungsanlagen GmbH, Germany) with the condenser previously set at À85 °C. The drying program was then inserted for 48 h with the application of vacuum (1 mbar).
Raw data were first transformed in mgf files using PLGS 3. 0. 0 (Waters) and peptides were identified by database searching using SequestHT (Thermo Fisher Scientific) with PROTEOME DIS- COVERER 2. 4 software (Thermo Fisher Scientific). Precursor and fragment mass tolerances were set at 15 ppm and 0. 05 Da, respectively, and no enzyme specificity was selected. Oxidation (+15. 995, M) was set as variable modification. Peptides were filtered with a 1% FDR.
CD95L modelling The extracellular human CD95L structure was obtained by homology modelling using YASARA version 19. 9. 17  [76]  using default settings, in order to resolve the noncrystallized region and thus generate a complete protein model. The CD95L trimer (PDB: 4MSV) and the predicted structure from AlphaFold V2. 0 (P48023) were used as templates. The obtained structure was prepared using Protein preparation wizard implemented in SCHR € ODINGER (Schr€ odinger Release 2021-3: Maestro, Schr€ odinger, LLC, New York, NY, 2021) and the membrane model (POPC, phosphatidylcholine) was set up between the amino acid 81 and 103.
Acknowledgements This work was funded by grants from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  777995  to MLG,  INCa  ( PLBIO2019 ) to ECh. This work was also supported by grants from  Biogenouest ,  Infrastructures en Biologie Sant e et Agronomie (IBiSA)  and  Conseil R egional de Bretagne  awarded to C. P. the  Swedish Research Council  ( 2019-3684 ) to LAE and  Sven and Lilly Lawski Foundation  to MT. The  Swedish National Infrastructure  for Computing is acknowledged for generous allocations of computing time at supercomputing centres  C3SE  and  NSC , in part funded by the  Swedish Research Council  through grant agreement no. 2018-05973.
Data availability statement The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [1]  partner repository with the dataset identifiers PXD039211, PXD039129 and 10. 6019/ PXD039129. The coordinates of the models (pdb format) are available for free download from Zenodo archive with the DOI: 10. 5281/zenodo. 7544325.
The adjusted associations between phthalate concentration, fixation duration, and reaction time were modelled using adjusted linear regressions. Percentages (percent time spent looking at the eyes and at the novel face) were modelled with beta regression  (Ferrari and Cribari-Neto, 2004 ) (R package betareg (Cribari-Neto and Zeileis, 2010)). Regarding model interpretation, betas are provided for an increase by one in the ln-transformed concentrations, except for fixation duration for which betas represent the percent change in fixation duration for doubling in biomarker concentration. Since several studies  (Radke et al. , 2020) , including one conducted within the SEPAGES cohort  (Guilbert et al. , 2021) , reported effect modification by child sex for various neurodevelopmental outcomes, in an additional analysis, we added an interaction term between sex and phthalate metabolite concentration.
If the p-value for this interaction term was <0. 2, sex-specific exposure effects were computed. To test the robustness of our results, analyses were run with phthalate metabolite concentrations that were not standardized for sampling and analytical conditions. Additional adjustment for specific gravity (assessed in each pool using a handheld Atago PAL 10-S refractometer (Atago)) was also performed. Finally, models were re-run without outlier observations identified using Cook's distance above the threshold of 0. 026 (4/sample size)  (Bollen and Jackman, 1985). All tasks and eye tracking data postprocessing were performed using MatLab (R2007b) and the Eyelink® toolbox  (Cornelissen et al. , 2002)  while statistical analyses were performed using R version 4. 2. 0 (R Core Team, 2021). The R codes are available at https://gricad-gitlab.
Acknowledgments We thank the SEPAGES study group:  E. Eyriey ,  A. Licinia ,  A. Vellement  ( Groupe Hospitalier Mutualiste, Grenoble ),  I. Pin ,  S. Bayat ,  P. Hoffmann ,  E. Hullo ,  C. Llerena  ( Grenoble Alpes University Hospital, La Tronche ),  X. Morin  ( Clinique des Cèdres, Echirolles ),  A. Morlot  ( Clinique Belledonne ,  Saint-Martin  d'Hères),  J. Lepeule ,  S. Lyon-Caen ,  C. Philippat ,  I. Pin ,  J. Quentin ,  V. Siroux  and  R. Slama  ( Grenoble Alpes University, Inserm, CNRS, IAB ). We thank the research team of the  Grenoble Laboratoire de Psychologie et Neurocognition (LPNC) , for the eye tracking assessments done in its Babylab. SEPAGES biospecimens are stored at  Grenoble University Hospital (CHU-GA ) biobank (bb- 0033-00069 ); we would like to thank the entire CRB team and in particular the technicians for the huge work of biospecimens processing and pooling.
Funding This work was supported by the  French Agency for Food, Environmental and Occupational Health & Safety -ANSES  ( CNAP  project,  EST-2016-121 ) and the  French Research Agency -ANR  ( EDeN  project  ANR-19-CE36-0003-01 ). The SEPAGES cohort was supported by the  European Research Council  (N •  311765-E-DOHaD ), the  European Commun-.
Participants We used the data from five longitudinal multicenter cohorts: the ADNI  [25] [26] [27] [28] [29] [30] [31]  (N = 1652), the Australian imaging, biomarker, and lifestyle flagship study of aging (AIBL)  32, 33  (N = 460), the JJ-ADNI  34, 35  (N = 470), the PharmaCog cohort  36, 37  (N = 111) and the MEMENTO cohort  38  (N = 1994). The study protocols were approved by the ethical committees of the university of southern California (ADNI), Austin Health, St Vincent's Health, Hollywook Private Hospital and Edith Cowan University (AIBL), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli (Pharma-Cog), Comité de protection des personnes sud-ouest et outre-mer III (MEMENTO), the National Bioscience Database Center Human Database (J-ADNI). Informed consent forms were obtained from research Participants are selected first using standard inclusion criteria and undergo a series of exams.
A disease progression model, such as AD Course Map, then forecasts the progression of each participant's data and predicts if the participant is likely to progress significantly during the trial, as measured by the predicted outcome change, which is the mini-mental state examination (MMSE) in this example. The treatment effect (e. g. , a 25% reduction of the change of the MMSE during trial) leads to a greater effect size, and therefore a smaller sample size, on the group of predicted fast progressors compared to the group of predicted slow progressors or the two groups combined. As a result, one may demonstrate the treatment efficacy with fewer participants by monitoring only the group of predicted fast progressors. participants. The research has been performed in accordance with the Declaration of Helsinki and relevant guidelines and regulations.
Participants were not compensated for the current study. The five cohorts are longitudinal observational studies with an average observation period ranging from 2. 0 years for PHARMACOG to 4. 8 years for ADNI, with an average number of visits ranging from 3. 7 in AIBL to 6. 9 in MEMENTO. We considered all participants with at least one year of follow-up. The sociodemographic, genetic, biological and clinical characteristics of the selected participants are reported in Tables  1  and 2 , as well as the proportion of available data in each cohort.
For all studies, cortical reconstruction and volumetric segmentation were performed with the Freesurfer image analysis suite (http:// surfer. nmr. mgh. harvard. edu/). Version 5. 3 was used for J-ADNI, MEM-ENTO, and PharmaCog, and version 6. 0 for ADNI and AIBL, operated within Clinica for reproducibility purposes  54. The cohort effect in the following analyses accounts for possible differences due to different versions of the software. We calculated the mean volume of the left and right hippocampus, and the total volume of the lateral ventricles (including inferior lateral volume). Hippocampus segmentation with Freesurfer was previously reported to have good reproducibility  55, 56. Both volumes were normalized by estimated total intracranial volume (ICV).