dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Cuprite dataset We used a subset of 250×191 pixels of the Cuprite dataset for real-world experiments. The minerals in that region are well-studied and are therefore suitable for evaluating the abundance maps qualitatively. Fig. 3  (a) depicts the geological ground reference for the dominant minerals. We use a library D ∈ R 188×498 , which is composed of 498 spectral pixels from the USGS library. The water absorption and noisy bands were removed, hence the final pixels are of dimension p = 188. Fig. 3  (b) demonstrates the abundance maps estimated by using different unmixing techniques applied to Cuprite. We showed three dominant minerals in the scene, i. e. Chalcedony, Alunite, and Kaolinite, corresponding to library endmembers 297, 420 and 465. For SUnAA, those abundances appear as 15, 13, and 11th.
Fig. 4  depicts the estimated endmembers corresponding to the three minerals (Chalcedony, Alunite, and Kaolinite). Comparing the estimated endmembers with the corresponding ones from the library, i. e. 297, 420 and 465, reveals that they are scaled versions. In real-world applications, the captured datasets are affected by noise, atmospheric effects, illumination variations, and the intrinsic variation of materials  [19]. Therefore, expecting the measured endmembers from a library to represent the materials in a real-world dataset is unrealistic. On the other hand, using archetypal analysis, we can achieve a linear combination of the endmembers from the dictionary to better represent the endmembers present in the scene.
IV. CONCLUSION We proposed a sparse unmixing technique using archetypal analysis called SUnAA. SUnAA models the endmembers of interest as a convex combination of endmembers from a library. We proposed a nonconvex optimization to simultaneously estimate the endmembers contributions and abundances. The proposed iterative algorithm is based on an active set method and is parameter-free. We evaluated SUnAA on two simulated and Cuprite datasets. The experimental results confirm that SUnAA leads to accurate abundances estimation and  significantly outperforms the conventional sparse unmixing techniques. Additionally, experiments on a real-world dataset suggest that SUnAA can better detect existing minerals in a given scene.
2 Specific properties for each of the EFOSC2 grisms can be found in the dedicated ESO instrument webpage, https://www. eso. org/sci/ facilities/lasilla/instruments/efosc/inst/Efosc2Grisms. html 3 https://github. com/svalenti/pessto 4 https://github. com/LCOGT 5 IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc. , under cooperative agreement with the U. S. National Science Foundation.
Their general properties are listed in Table  1. The SN redshifts (z) were obtained from underlying host H regions present in the SN spectrum, except in the case of SN 2018aql for which there is no other available information than that obtained by spectral matching performed using the Supernova Identification (SNID;  Blondin & Tonry 2007)  software. SNID was also used to find events among the template sample that could resemble those presented here. We note that although we found some matches (see below), the phases are not accurate as our SNe are often quite distinct (spectroscopically) from the template events in SNID. Absolute magnitudes were calculated by correcting for Milky Way extinction obtained from the NASA Extragalactic Database (NED) 7  assuming a reddening law with Rv 3. 1. The distance moduli (DM) were calculated using the estimated z and the Cosmology Calculator III 8  provided by NED, adopting NED's cosmological parameters (H 0 73 km s -1 Mpc -1 , Ω Matter 0.
Comparison with other LSNe II In order to better interpret our data, we compare our sample with other LSNe II in the literature. Our goal is to find events similar to those presented here. We considered only comparison events with publicly available V-band photometry and at least three publicly available good S/N (> 5) spectral observations at different phases. This is because we selected our sample based on the V peak brightness, and because we want to be able to perform meaningful spectral comparisons. Moreover, since we are interested in understanding the observed features of our sample and not in gathering a sample of LSNe II, the final requirement to consider an event as a comparison object is that it should be part of a study that provides some interpretation of the observed characteristics rather than a pure data release.
Note that we do not put any constrain on the shape of the Hα profile to consider an event as part as the comparison sample. We aim at discovering if any event that exists in the literature shows similar behaviour to those presented here. To gather the comparison sample we inspected the Open Supernova Catalog (OSC 12 ;  Guillochon et al. 2017 ) and the SAO/NASA Astrophysics Data System (ADS 13 ). We found six objects matching our criteria: SN 2013fc  (Kangas et al. 2016 ), SN 2016ĳa (Tartaglia et al. 2018) ,  ASASSN-15nx (Bose et al. 2018 ), SN 2016gsd (Reynolds et al. 2020) , SN 2016egz 14  (Hiramatsu et al. 2021 ), and SN 2018hfm (Zhang et al. 2022). The adopted explosion date, distance and extinction for each comparison event were obtained from the cited references. As far as we know, this comparison sample includes most of the LSNe II with publicly available good-coverage observations (see above) present in the literature.
12 Although the front end of the OSC is no longer accessible, the catalog is still available on GitHub containing all the transients uploaded through 8 April 2022. Note that there are no further updates after this date. 13 https://ui. adsabs. harvard. edu. 14 The data for SN 2016egz were obtained upon request to  Hiramatsu et al. (2021). Fassia et al. (2001)  propose that SN 1998S shows a blend of He and Na at this epoch. The Ca NIR3 present in the spectra of our sample of LSNe II remains broad, but it does not show multiple peaks as seen in SN 1998S. It is worth noting that we are trying to find LSNe II similar to the ones presented in this work; hence, although considered as comparison events, SN 2016gsd and SN 2016ĳa would not have been selected as part of our sample because of the clear Hα absorption features present at several epochs.
Light curves Here we inspect the V-band light-curve behaviour of our sample against that of the comparison sample defined above. We include SN 2004et  (Maguire et al. 2010 ) and SN 2014G  (Terreran et al. 2016)  as examples of events treated as regular SNe II. The only criterion to select SN 2004et is that it has been extensively studied in the literature. SN 2014G was selected as it is a well-studied fast decliner with a good dataset. All considered V light curves can be seen in Fig. 14. SN 2017cfo and SN 2017hbj present a decline rate similar to that of SN 2014G up to ∼ 75 days, when the light curve of SN 2014G transitions to the radioactive tail. At this point both SN 2017cfo and SN 2017hbj show a subtle flattening that for the latter continues up to ∼ 100 days, after which their light curves decline again.
ACKNOWLEDGEMENTS We thank the anonymous referee for the valuable revision. P. J. P. thanks  Joe Lyman  for guidance with some of the used software. P. J. P. thanks the useful input of Tuomas Kangas. P. J. P. acknowledges funding support by the  ESO   Ph. D. studentship program. Based on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere, Chile , as part of ePESSTO+ (the advanced  Public ESO Spectroscopic Survey for Transient Objects Survey ). ePESSTO+ observations were obtained under ESO program IDs  1103. D-0328 ,  106. 216C ,  108. 220C  (PI: Inserra). This work makes use of observations from the  Las Cumbres Observatory global telescope network (LCO). LCO data have been obtained via OPTICON. The  OPTICON  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant no.
730890. The LCO team is supported by  NSF  grants  AST-1911225  and  AST-1911151. This work was funded by  ANID ,  Millennium Science Initiative ,  ICN12_009. L. M. acknowledges support from a  CONICET  fellowship and  UNRN PI2018 40B885  grant. A. V. F. received generous financial support from the  Christopher R. Redlich Fund  and many individual donors. M. N. is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 948381 ) and by a Fellowship from the  Alan Turing Institute. T. E. M. B. acknowledges financial support from the  Spanish Ministerio de Ciencia e Innovación (MCIN) , the  Agencia Estatal de Investigación (AEI)   10. 13039/501100011033 , and the  European Union Next Generation EU/PRTR  funds under the  2021 Juan de la Cierva program   FJC2021-047124-I  and the  PID2020-115253GA-I00   HOSTFLOWS  project, from  Centro Superior de Investigaciones Científicas (CSIC)  under the  PIE  project  20215AT016 , and the program  Unidad de Excelencia María de Maeztu   CEX2020-001058-M.
Support for G. P. is provided by the  Ministry of Economy, Development, and Tourism's Millennium Science Initiative  through grant  IC120009 , awarded to  The Millennium Institute of Astrophysics (MAS). P. C. acknowledges support via an  Academy of Finland  grant (340613; P. I. R. Kotak). M. G. is supported by the  EU   Horizon 2020 research and innovation programme  under grant agreement No  101004719. L. G. acknowledges financial support from the  Spanish Ministerio de Ciencia e Innovación (MCIN) , the  Agencia Estatal de Investigación (AEI)   10. 13039/501100011033 , and the  European Social Fund (ESF)  " Investing in your future " under the  2019 Ramón y Cajal program   RYC2019-027683-I  and the  PID2020-115253GA-I00   HOSTFLOWS  project, from  Centro Superior de Investigaciones Científicas (CSIC)  under the  PIE  project  20215AT016 , and the program  Unidad de Excelencia María de Maeztu   CEX2020-001058-M.
X. W. is supported by he  National Natural Science Foundation of China (NSFC  grants  12288102 ,  12033003 , and  11633002 ), the  Scholar Program of Beĳing Academy of Science and Technology  (DZ: BS202002 ), and the  Tencent Xplorer Prize. T. M. R. acknowledges the financial support of the  Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish academy of Science and Letters. The identification of SN 2017gpp was done by volunteers of the Supernova Sighting project in the Zooniverse. orgplatform. We have used the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration (NASA). We have also used the "Aladin sky atlas" developed at  CDS, Strasbourg Observatory, France.
Experimental tasks All experiments were implemented using the Psychophysics Toolbox 3 (http://psychtoolbox. org) v3 in MATLAB (Mathworks, Natick, MA, USA). The sound files used in Experiments 1, 2, 4 were generated using the ttsreader. com speech generation software, those used in Experiment 3 through the text to speech functionality from Macintosh computer, all with a female voice; sounds for Experiment 5 were recorded, with a male speaker. At the end of each experiment, participants were asked whether they felt that TTS had provided support, hindrance, both, or none to task performance.
Statistical analysis We analyzed error rates and correct reaction times with generalized mixed-models (GMM) using the Jamovi software (v. 2. 3. 16. 0). We calculated effect sizes using Odds ratio (OR) for error rates, and partial eta squared (h p 2 ) for reaction times. In all experiments, participant and stimuli were entered as random effects, and group as a fixed effect. All other withinsubject factors were entered as fixed effects. The significance threshold was set to p <. 05. One control participant, who had not correctly understood the instructions, was excluded from the analysis of Experiment 4.
Letters counting Participants were presented with spoken words varying in orthographic length, and indicated their number of letters as fast as possible. We predicted that synesthetes should be faster and more accurate than controls (Fig. 1A ). We then ran reliability analyses using the splithalf Rpackage  (Parsons, 2021). Using 5000 random splits, as in all c o r t e x 1 6 8 ( 2 0 2 3 ) 2 2 6 e2 3 4 subsequent analyses, the SpearmaneBrown corrected reliability estimate was. 75, 95% CI [. 63e. 85] for accuracy, and. 9, 95% CI [. 85e. 94] for RTs. On debriefing, 18 (82%) synesthetes reported that TTS helped them perform the task, while only 4 (18%) reported neither an advantage nor a hindrance.
| Human adipose tissues Subcutaneous human AT was obtained from healthy adult women undergoing dermolipectomy (mean body mass index 27. 5 kg/ m 2 ± SD 3. 324, mean age 41. 9 years ± SD 9. 8). The protocol was approved by Ministère de la Recherche (DC2008-452). Paired subcutaneous abdominal and visceral (omental) AT was obtained from patients, candidates for bariatric surgery through a clinical study approved by the Institutional Review Boards registered at Clini calTr ials. gov (SENAPID NCT01525472). All donors gave their informed consent.
Cells were washed with PBS and analyzed using a FACS Canto™ II flow cytometer. Analyses were performed with Diva Pro software (BD Biosciences) or FlowJo (BD Biosciences). Scratch wound was performed according to the manufacturer's instructions using the 96-well WoundMaker (Incucyte, Sartorius). After PBS washes, media were changed to ECBM 1% FCS in the presence or not of LY411575 (10 μmol/L), or in the presence of TGFβ1 (5 ng/ml), or SB431542 (10 μmol/L) and recorded every 3 h using the Incucyte SX5 during 72 h. Wound closure was analyzed using the Incucyte Scratch Wound Analysis Software Module (Sartorius, France).
| Immunofluorescence staining Fixed cells were blocked and permeabilized in PBS 0. 1% Triton X-100 and 5% serum for 30 min at RT and then incubated overnight at 4°C with primary antibodies (mouse anti-αSMA, Dako 1A4, 1:100; rabbit anti-NOTCH3, Abcam ab23426, 1:200, mouse anti-γH2AX, EMD Millipore JBW301, 1:200). After washing steps, cells were incubated for 1 h at RT with appropriate Alexa Fluor-conjugated secondary antibodies (Invitrogen). Nuclei were stained with DAPI (0. 5 μg/ml, Invitrogen), and lipid accumulation was visualized with BODIPY 493/503 staining (10 μg/ml, Invitrogen) for 15 min at RT. Images were taken with a fluorescent (Nikon Eclipse TE300, software NIS-Elements 2. 5 BR, Nikon®) or confocal microscope (ZEISS LSM780, ZEN software). Image analysis was performed with ImageJ software.
| Transcriptional analysis Total RNA was isolated from human cells using Quick-RNA Microprep kit (Zymo Research) or from murine SVC using Direct-zol RNA Miniprep Plus kit (Zymo Research) according to the manufacturer's instructions. cDNA synthesis was performed on 100-200 ng of total RNAs with Superscript III (Thermofisher) and random hexamer primers. Relative gene expression levels were assessed using Taqman® Probes and TaqMan Fast Advanced Applied mastermix (Thermofisher) on a QuantStudio™ 5 (QS5; Thermofisher). Human and murine Assay-On-Demand are listed in Table  S1. Each of the samples was run in duplicate, and the relative amount was normalized to 18 S or PPIB housekeeping gene. Data were analyzed using QuantStudio Real-Time PCR software (Thermofisher).
For serial passaging secretome, the nanoAcquity UPLC (Waters) coupled to a Q Exactive Plus mass spectrometer (Thermo Scientific) equipped with a nanoelectrospray source was used. Protein digests were loaded onto a nanoAcquity UPLC Trap column (Waters) and washed with 0. 2% formic acid at 20 μl/min for 3 min. Peptides were then eluted on a C 18 reverse-phase nanoAcquity column (Waters) with a linear gradient of 8-31% solvent B (H 2 O/ CH 3 CN/HCOOH, 10/90/0. 2, by vol. ) for 120 min, 31-91% solvent B for 20 min, and 91% solvent B for 5 min, at a flow rate of 250 nl/min. The mass spectrometer was operated in the data-dependent mode XTandem parallel Database Search. Peptide identifications were accepted if they could be established at greater than 7. 0% probability to achieve an FDR less than 1.
0% by the Scaffold Local FDR algorithm. Protein identifications were accepted if they could be established at greater than 97. 0% probability to achieve an FDR less than 1. 0% and contained at least 2 identified peptides. Quantitative differential analysis of proteins was realized using a label-free analysis with an in-house DIFFTAL (DIFferential Fourier Transform Analysis) software algorithm as described previously  (Autelitano et al. , 2014).
Equimolar concentrations of libraries were pooled at 4 nmol/L and used for sequencing. Pooled library was denatured and diluted as described in Denature and Dilute Libraries Guide (Illumina) and adjusted to final concentration of 1. 4 pmol/L. Resulting library was sequenced on NextSeq 500 using NextSeq 500/550 High Output v2 kit with 2 × 151 bp cycle. Generated raw files were converted to FASTQ files and used for data analysis. AmpliSeq transcriptome FASTQ files were analyzed on Array studio V10. 0 (Omicsoft, Qiagen). Following raw read QC, first and last 10 bases were trimmed and mapped to reference genome Human B38. The read count data were generated using GeneModel RefGene20170606. Resulting data were normalized by DESeq package, transformed to log2 value, and used for statistical analysis using NetworkAnalyst  (Zhou et al.
| DISCUSS ION There has been an increasing amount of evidence, implicating that adipose tissue (AT) senescence plays a role in both obesity-and ageinduced processes responsible for metabolic abnormalities. We show that SAβ-Gal-positive cells accumulate in both ScAT and VsAT of obese subjects by colorimetric imaging approaches, as described previously  (Conley et al. , 2020; Gustafson et al. , 2019; Rouault et al. , 2021). Differences in whole tissue SAβ-Gal staining have been reported between ScAT and VsAT of obese patients  (Rouault et al. , 2021). Unsupervised analysis of flow cytometry datasets allows the identification of SAβ-Gal+ cells in the SVC, revealing that macrophages represent the largest proportion, followed by progenitors and endothelial cells. SAβ-Gal+ macrophages have been previously reported in AT, but their senescent phenotype remains debated  (Hall et al.
Moreover, the group of patients with the highest proportion of senescent progenitors in VsAT is characterized by an early obesity trajectory reflected by an increased BMI at the age of 20 years old. It is therefore tempting to speculate that high VsAT progenitor senescence is a consequence of the chronic replicative pressure exerted by a longer history of obesity. The proliferative rate of AT progenitor cells is distinct according to fat depot location, with less proliferation in visceral compared with subcutaneous AT  (Tchkonia et al. , 2006). The present data suggest that the depot-specific differences in senescence may be an additional determinant of the progenitor proliferation rate. Combined transcriptomics and proteomics datasets, obtained from both native and in vitro replicative senescent immuno-selected progenitors and comparison with other available SASP datasets, highlight common core SASP factors in both ScAT and VsAT including INHBA as well as fat depotand replicative senescence-specific SASP factors.
Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/acel. 13776 by NATHALIE BOULET -Université Toulouse Iii Paul Sabatier , Wiley Online Library on [09/01/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License to automatically switch between MS and MS/MS acquisition. Survey full-scan MS spectra (from m/z 325-1300) were acquired with a resolution of 70,000 at m/z 200. MSMS spectra were recorded in profile type with a resolution of 17′500. All samples were injected in triplicate. The LC-MS/MS data, acquired using the Xcalibur software (Thermo Fisher Scientific), were processed using a homemade Visual Basic program software developed using XRawfile libraries (Thermo Fisher Scientific) to generate an MS/MS peak list used for database searching and a file used for quantitative analysis.
Analysis of the secretome of native immuno-selected ScAT and VsAT progenitor cells from 17 patients by large-scale mass spectrometry identified 428 secreted proteins. Paired analysis of ScAT and VsAT progenitor secretomes highlighted 161 differentially expressed proteins (DEPs) upregulated in VsAT progenitors and 65 in ScAT progenitors; the DEPs with the highest fold changes are shown in Figure 1f. Comparison of ScAT and VsATDEPs with the freely available secretome database, the SASP atlas(Basisty et al. , 2020), the SenMayo geneset (Saul et al. , 2022)  and factors identified in human AT senescent progenitors (Xu, Palmer, et al. , 2015;  Xu, Tchkonia, et al. , 2015)  were performed. VsAT progenitor secretome was mainly enriched in common canonical SASP factors including IGFBP7, IL6, and CXCL8 (cellular senescenceenriched reactome pathway), the CXCR2 Ligands (CXCL1, CXCL2, CXCL3, and CXCL8), as well as inflammatory, growth, and remodeling factors (MIF, FGF7, and INHBA) and factors of the complement and coagulation pathways (PLAT, PLAU, SERPINB2, E2 and E1, and TF; Table.
activation by DLL4 further enhanced myofibrogenesis, as shown by the increase in αSMA-positive cells and the expression of myofibroblast genes (ACTA2, COL1A1, and INHBA; Figure 4h,j) with no impact on total cell number (Figure 4I). Analyses of RNAseq data retrieved from whole ScAT and VsAT from the GTEx database (https://www. gtexp ortal. org) showed that NOTCH3 transcript levels were positively correlated with the ones of myofibrogenic-and SASP-related genes such as ACTA2, COL1A1, COL3A1, INHBA, GREM1, and SFRP4 and with TGFβ and EMT-related transcription factor SNAI2 but not SNAI1. Conversely, NOTCH3 levels were negatively correlated with the expression of the adipogenic-related markers ADIPOQ, GPDD1, PLIN1, and PPARG. Similar trends were observed for both ScAT and VsAT, but correlations were weaker in VsAT.
Results are expressed as fold change over P1, means ± SEM of n = 15 independent experiments (one-way ANOVA, Dunn's post-test, ***p < 0. 001 compared with P1). (c) Heatmap of differentially expressed proteins in paired analysis between P1 and P3 and P1 and P6 secretomes from three donors, (deep blue corresponding to the smallest value and yellow to the highest value). (d) Volcano plot of the twofold and more differentially expressed genes between P1 and P6 RNA sequencing transcriptomes performed in three donors. (e) Statistically significantly enriched pathways in the 518 DEGs upregulated in P6 transcriptome in the Human Molecular Signatures Database (MSigDB) hallmark gene sets MSigDB Hallmark (GSEA). (f) Venn diagram of DEGs upregulated in the P6 transcriptome, P3 and P6P3 secretome and ScAT (left) and VsAT (right) native progenitor-specific secretomes.
14749726, 0 , 0 Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/acel. 13776 by NATHALIE BOULET -Université Toulouse Iii Paul Sabatier , Wiley Online Library on [09/01/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License to automatically switch between MS and MS/MS acquisition. Survey full-scan MS spectra (from m/z 325-1300) were acquired with a resolution of 70,000 at m/z 200. MSMS spectra were recorded in profile type with a resolution of 17′500. All samples were injected in triplicate. The LC-MS/MS data, acquired using the Xcalibur software (Thermo Fisher Scientific), were processed using a homemade Visual Basic program software developed using XRawfile libraries (Thermo Fisher Scientific) to generate an MS/MS peak list used for database searching and a file used for quantitative analysis.