dataesr/scientific-paragraphs-categorization huggingface dataset:
V. ),  U01HG007690  (M. V. ),  R01GM109199  (M. A. C. ), a  Canadian Institute for Health Research (CIHR) Foundation  Grant (F. P. R. ), the  Canada Excellence Research Chairs Program  (F. P. R. ) and an  American Heart Association  grant  15CVGPS23430000  (M. V. ). D. K. was supported by a  Banting Postdoctoral Fellowship  through the  Natural Sciences and Engineering Research Council (NSERC)  of  Canada  and by the  Basic Science Research Program  through the  National Research Foundation (NRF)  of  Korea  funded by the  Ministry of Education  ( 2017R1A6A3A03004385 ). G. M. S. was supported by  NIH   Training Grant   T32CA009361. M. V. is a Chercheur Qualifié.
August 1st 2018. Subcellular compartment membership of proteins was obtained from the Human Protein Atlas downloaded on August 1st 2018. Annotations of 'Uncertain' reliability were not used. Protein complex membership of proteins was obtained from BioPlex 8 2. 0 supplementary table 7. Entrez gene IDs were mapped to Ensembl gene IDs and restricted to protein-coding genes. database on June 13th 2018 and restricted to protein-coding genes. Pathway membership of proteins was obtained from Reactome, using a file mapping Ensembl gene IDs to lowest level pathways, downloaded on.
In total, 1,271 putative O-GlcNAcylated proteins and six modification sequences were identified by mass spectrometry (data available via ProteomeXchange, ID PXD010285). Most of these proteins have structural and metabolic functions that are essential for parasite survival and evolution. Furthermore, O-GlcNAcylation pattern variations were observed by antibody detection under glucose deprivation and heat stress conditions, supporting their possible role in the adaptive response. Given the numerous biological processes in which O-GlcNAcylated proteins participate, its identification in T. cruzi proteins opens a new research field in the biology of Trypanosomatids, improve our understanding of infection processes and may allow us to identify new therapeutic targets.
Immunofluorescence Microscopy For immunolabeling, purified T. cruzi epimastigotes were fixed in 4% (m/v) paraformaldehyde in PBS for 1 h at room temperature and washed with PBS. Parasite cells were permeabilized with 0. 1% Tween 20 for 90 min. Non-specific sites were blocked with 1% BSA. Anti O-GlcNAc antibodies RL2 diluted 1:50 (in PBS) were added and incubated overnight. After three washes with PBS, the parasites were incubated with anti-mouse FITC antibodies (1:100 in PBS) and then fixed on glass slides with Fluoro Shield DAPI (Sigma). A Leica DM2000 microscope with a Leica DFC310 FX camera was used for visualization. The images were processed with the software Image J. One representative figure is shown of three independent experiments.
Protein Identification and Abundance Quantification The acquired raw MS data were processed with the software Proteome Discoverer v. 1. 4. 1. 14 (Thermo Fisher Scientific). Data were searched via SEQUEST HT against the Uniprot T. cruzi reference proteome database (retrieved on February 14, 2018, 44 286 entries). The following parameters were used for searches: (i) trypsin; (ii) two missed cleavages were allowed; (ii) monoisotopic precursor tolerance of 10 ppm, followed by 0. 6 Da for fragment ions from MS/MS; and (iii) cysteine carbamidomethylation (+57. 0215) and methionine oxidation (+15. 995) as variable modifications. False discovery rate (FDR) was processed using Percolator a semi-supervised machine learning to discriminate correct from incorrect peptide-spectrum matches  (22) , and was set to a q-value of 1 and 5% for, respectively, define high and low confident peptides.
In addition, only peptide spectrum matches with a delta Cn Sequest HT parameters better than 0. 15 were considered and proteins were identified with at least two peptides per protein. Additionally, a threshold was established based on the MS area, which meant that the proteins with the lowest intensities which conformed the summed intensity of 7 × 10 8 were considered potential background  (21). The abundance of the different identified proteins were determined by label-free quantitative proteomics using the TOP 3 method  (23, 24). Peptides from β-elimination experiments were identified as described above, except that dehydration of Ser and Thr (-18. 011 a. m. u. ) and β-elimination of Cys (-33. 988 a. m. u. ) were added as variable modifications. Mass spectrometry proteomics data were deposited in the ProteomeXchange Consortium via the PRIDE (25) partner repository with the dataset identifier PXD010285.
These proteins belong to a broad range of biological functions and participating in various cellular pathways. Of the 10 most abundant putative O-GlcNAcylated proteins, three are constitutive of the cytoskeleton, three participate in oxidation reduction processes and the others include kinases, and proteins that participate in biosynthesis and stress response. The classification by function of the 100 most abundant based on Top3 quantification, are shown in Figure  5 , and full data are available via ProteomeXchange, ID PXD010285 and Supplementary Table  1. Subsequent elution of on-resin O-GlcNAcylated peptides by β-elimination led to the identification of 6 peptides corresponding to O-GlcNAc modification sites at 5% FDR (Supplementary Figure  1 ).
This low detection of modified peptides varies from our previous similar study  (28). The explanation could be associated to the lower amount of proteins initially used and to the higher number of O-GlcNAc proteins. Each O-GlcNAc modified peptide is related to one of the following protein groups: clathrins, RNA helicases, DNA polymerases, trans-sialidases and two uncharacterized proteins as seen in Supplementary Table  2. Several methods have been reported to detect O-GlcNAcylation. First, the sugar nucleotide UDP-GlcNAc is known to be a substrate for this PTM. UDP-GlcNAc is relatively abundant in T. cruzi, being produced by conventional HBP as  previously reported by  Turnock and Ferguson (29). Second, a search for the sequences of the regulatory enzymes OGT and OGA in T. cruzi genome databases (for instance, by Banerje et al.
However, it must be noted that there is a potential interference in the enrichment procedure with non-reducing GlcNAc residues on O-linked and N-linked glycoproteins as have been reported for ovalbumin  (39) ; when in a similar approach only 1% of the proteins were unspecifically bound, as the summed intensity of the labeled sample were 60-fold higher than the negative control  (21) , in the present work an additional threshold was set based on the MS area as described in the material and methods section. This combination of methods allowed us to identify 1,271 proteins displaying a wide variety of biological functions, including nucleic acid synthesis, transcription, protein synthesis, structural constitution of cytoskeleton, mitochondrial function, stress response, ATP cycling, peptidase activity and noteworthy retro-transposon hot spot (RHS) that are kinetoplastids exclusive proteins whose functions remain unveiled (data are available via ProteomeXchange, ID PXD010285 and Supplementary Table  1 ).
ACKNOWLEDGMENTS To  Karen Julissa Loaeza-Reyes ,  Yolanda Guevara Gomez , and  Mariana De Alba Alvarado  for their technical support. MA-G hold a fellowship of  CONACyT  from the government of Mexico. The authors thank the  UNAM DGAPA-PAPIIT  project  IN227816  and  IN216118 ,  Red Temática de Glicociencia en Salud CONACyT, Cuerpo Académico Investigación en Salud UABJO CA-63  for their financial support. Proteomics analysis was supported by the  Institut Paoli-Calmettes  and the  Centre de Recherche en Cancérologie de Marseille. Proteomic analyses were done using the mass spectrometry facility of  Marseille Proteomics  (marseille-proteomique. univamu. fr)supported by  IBISA  ( Infrastructures Biologie Santé et Agronomie ),  Plateforme Technologique Aix-Marseille , the  Cancéropôle PACA , the  Provence-Alpes-Côte d'Azur Région , the  Institut Paoli-Calmettes , and the  Centre de Recherche en Cancérologie de Marseille.
Wide-Angle-X-ray Scattering (WAXS) experiments were carried out on a Genix microsource (XENOCS) equipment using the Cu K α radiation (λ = 1. 54 Å). Primary beam was collimated and monochromatized by a FOX2D-12Inf optic (Xenocs, France), and the WAXS patterns were collected on a CCD VHR detector (Photonic Sciences). The experiments were conducted at room temperature in transmission mode on the compression molded films. Standard corrections were applied to the WAXS patterns before their treatments. The intensity profiles were obtained by 360° azimuthal integration of the 2D WAXS patterns using the fit2D software ®. Transmission Electron Microscopy (TEM) was performed on a Philips CM120 operated at 120 kV (CTM, Lyon, France). Ultrathin slices (~70 nm) were cut using an Ultra-cut cryo-microtome.
■ ACKNOWLEDGMENTS This work was supported by the  Max Planck Society , the  EC  through the  Marie Sklodowska-Curie ITN  project  iSwitch  ( GA-642196 ). The activity in Strasbourg was also supported by the  Agence Nationale de la Recherche  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ) and the  International Center for Frontier Research in Chemistry  (icFRC). We thank  Steven D. Aird  for editing the manuscript.
Acknowledgments: The authors would like to thank first the  Fonds Européen de Développement Régional (FEDER) ,  CNRS ,  Région Nord Pas-de-Calais, and Ministère de l'Education Nationale de l'Enseignement Supérieur et de la Recherche  for funding of X-ray diffractometers, and secondly,  Marc Bria , CCM, RMN,  Université Lille.
We present the optimal reconstruction of the thermal Sunyaev-Zel'dovich (tSZ) effect signal based on the combination of a heterogeneous dataset consisting of ACT and Planck data, with different numbers of channels, angular resolutions and noise levels. We combine both datasets using two different approaches, a Matched Multi-Filter (MMF) technique and an optimised Internal Linear Combination (ILC). We show that when applying the MMF to the combination of ACT and Planck data, the size-flux degeneracy is reduced and the signal-to-noise of clusters detected with their SZ signal improves by up to a factor of three. In the case of the optimised ILC method, we show that the tSZ map is reconstructed with a resolution of ∼ 1. 5 arcmin. This is more than a factor two improvement compared with the Planck resolution, and with a very good control of noise, i.
Data We focus on two CMB surveys, ACT and Planck, which differ by their angular resolutions, by their available frequency channels, and by their sky coverage. In the following, both datasets are considered as a single composite dataset of intensity and associated noise maps. Characteristics of the maps from both experiments are summarized in Table  1. We perform our analysis using this composite multi-instrument data that will be referred to as the PACT dataset in the rest of the paper. We use the publicly available total intensity dataset of the High Frequency Instrument (HFI) taken during the full mission of the Planck survey, i. e. the six highest-frequency Planck channel maps, from 100 to 857 GHz at native resolution  (Planck Collaboration VIII 2016). The full Planck dataset is available at the Planck Legacy Archive (PLA 1 ).
A noise map is associated with each channel map and is constructed from the difference of the first half and second half of the Planck rings for a given pointing position of the satellite spin axis. These maps are mainly free from astrophysical emission and they are considered a good representation of the statistical instrumental noise (Planck Collaboration For the ACT dataset, we use the publicly available 148 and 220 GHz data in the Southern and Equatorial areas at native resolution 1-1. 4 arcmin. The data was collected from 2008-2010 using the Millimeter Bolometric Array Camera  (Swetz et al. 2011). The ACT data reduction is described in  Dünner et al. (2013). We also use the associated weight maps and point-source masks 2. The latter are defined to be zero in a 5 arcmin circle at the location of sources detected with flux density greater than 15 mJy at 148 GHz.
3  Finally, we use the beam characteristics and tSZ transmission band centers in  Hasselfield et al. (2013b)  and  Swetz et al. (2011) , respectively. In the following, we combine the full ACT dataset with the overlapping Planck coverage. However, we identify two deeper regions that will be used to illustrate the results, called S deep and E deep for the Southern and Equatorial regions, respectively. These selected areas are displayed as black rectangles in Fig. 3. The area S deep is defined such that 56. 3 • < R. A. < 97. 9 • and -55. 3 • < Dec. < -51. 2 •. The area E deep is such that 4. 1 • < R. A. < 53. 6 • and -1. 06 • < Dec. < 1. 40 •.
Combining ACT and Planck data In this section, we present two different approaches for the combination of the ACT and Planck datasets, both optimised for the tSZ studies. The first one consists in combining the ACT and Planck maps through a Matched Multi-Filter (MMF) technique. This method is particularly optimised for the detection of tSZ sources and the measurement of their significance and photometry. The second approach consists in reconstructing a tSZ y-map using an ILC method to combine the frequency maps of the PACT dataset. We show, in Sect. 4, some of the advantages of the PACT dataset over the individual Planck and ACT data. Full analyses of the tSZ source catalog and y-map derived from the PACT dataset are beyond the scope of the present study. Hence as a proof of concept, we focus mostly on the deepest region of the ACT data, S deep , and we illustrate our results and compare them with those obtained, for the same region, by Planck and ACT separately.
2. The PACT dataset, i. e. all Planck-HFI and ACT channel maps, is then processed with an MMF filter. For this, the size of the spatial template is varied between 0. 1 and 50 arcmin in 50 logarithmically spaced bins. A detected tSZ source is defined as the one with the highest S/N when varying the template size. The associated size and flux measured from the filter are defined as the ones of detected source. It is beyond the scope of the present study to construct and deliver a catalog of clusters and tSZ sources from the PACT dataset. Our aim is to show the complementarity and added value of combining ACT and Planck data. We thus illustrate the application of the MMF to the detection of the tSZ sources in the combined PACT dataset by focusing on results of the deep region S deep. Furthermore, we have checked neither S/N nor tSZ fluxes derived from the MMF method are improved when we use both the 148 and the 220 GHz ACT maps or only the 148 GHz map.
We therefore choose to show the results of the analysis using all eight frequency channels of the PACT dataset (the two from ACT and six from Planck). In this configuration, we detect in total 58 tSZ sources above a threshold of S/N=4. 3. This threshold corresponds to the lowest signal-to-noise of the tSZ sources identified with actual clusters (i. e. below S/N=4. 3 the tSZ sources are either candidate tSZ clusters or spurious detections). The sample of 58 tSZ PACT sources contains 22 actual clusters. The identification of counterparts is performed by searching for already-known clusters from tSZ, X-ray and optical catalogs in a 5 arcmin radius around the position of the PACT detected tSZ sources. We use the tSZ compilation of 2690 tSZ sources 5. We also use the MCXC meta-catalog  (Piffaretti et al.
2011 ). In the optical, we use catalogs from the SDSS survey  (Rykoff et al. 2014; Wen & Han 2015). Finally, we query the NED database for the PACT sources that are not associated with known clusters. Thanks to the optimal combination of the ACT and Planck data with the MMF, we are able to recover all the clusters detected by ACT and Planck not only those common to both experiments but also those detected independently by ACT or by Planck. This amounts to 14 clusters of galaxies in total. Furthermore, all Planck clusters recovered from the PACT data have S/N > 4. 5, the Planck detection threshold. We discuss later the improvement in S/N due to the PACT combination of data. The improvement brought by the PACT dataset can also be seen in the detection of eight new tSZ sources in the S deep region associated with actual clusters from optical, X-ray or tSZ catalogs.
PACT y-map construction We now combine the Planck and ACT data to build a joint tSZ y-map, called the PACT y-map. As described in Remazeilles et al. (  2013 ), such a combination can be performed using component separation techniques which aggregate the data. We choose here to illustrate the results for the MILCA method (see  Hurier et al. (2013)  for details). The MILCA method allows us to perform, for the whole region Equatorial and South regions covered both by ACT and Planck, a scale dependent optimization of the reconstructed tSZ signal with respect to noise and foreground residuals. Figure  1  shows the resulting reconstructed y-map in a selected area of the PACT footprint. MILCA is a semiblind component separation method that requires as input the tSZ spectral dependence. Additional constraints can be considered to optimize the separation.
The weights show that ACT and Planck experiments nicely complement each other and that their combination allows us to take advantage of Planck data at scales below ∼ 0. 1 arcmin -1 and ACT data above, as will be discussed below. The 148 GHz map from ACT (thick solid grey line) domi-nates the contribution to the PACT y-map at scales above ∼ 0. 1 arcmin -1 due to the higher ACT resolution. We note that most of the contribution to the tSZ signal at large angular scales in the PACT y-map reconstruction actually comes from the Planck frequency channels with large beams. Since they dominate the cleaning from foregrounds in the MILCA method (and more generally in Internal Linear Combination approaches). Figure  2  highlights the dominant contributions to the PACT y-map from the 143 and 353 GHz channels at large scales.
If we now turn to the PACT y-map (Fig. 1  bottom panel), we see the advantages of combining the two experiments into a single dataset with MILCA. As seen in the area of the the pair of clusters A3395-A3391, we do recover in the PACT y-map the large angular scales thanks to Planck. We also clearly see the improvement brought by the higher resolution of ACT both in terms of spatial structure and in terms of significance. This is seen on the two Planck clusters in the field PSZ1 G258. 33-38. 54 and PSZ2 G262. 72-40. 92. Finally, the advantage of the combination of ACT and Planck is also illustrated by the detection of a higher number of tSZ sources seen as positive sources in the y-map (including actual clusters not detected independently by ACT or by Planck). The PACT reconstructed y-map also shows contamination by radio-sources which can be easily mitigated thanks to their negative signal.
The noise map of the PACT shown in Fig. 3  is obtained from the half-dataset difference maps and applying the same linear combination (and weights) as the one used to compute the reconstructed PACT y-map. The PACT noise maps in Fig. 3  are shown for the ACT equatorial footprint (top panel) and southern footpring (bottom panel). The rectangular regions represent selected deep regions defined in Sect. 2. By construction of the PACT dataset from the combination of ACT and Planck frequency channels, the associated noise structures are scale dependent. This is exhibited in Fig. 1 , bottom panel, where we note that the noise spatial distribution is correlated and inhomogeneous. Considering that large angular scales are dominated by Planck data and small angular scales by ACT data. To illustrate the quality of the PACT MILCA tSZ ymap, we present on Fig.
Properties of the tSZ clusters from the PACT dataset The combination of the ACT and Planck data into the PACT dataset improves the yield of detected tSZ sources as discussed in Sect. 3. 1. It also improves the quality of the reconstructed y-map as seen in Sect. 3. 2. We now show the improvements brought by the ACT and Planck combination focusing on the significance of the tSZ clusters detected by ACT or Planck, namely their signal to noise, on their estimated flux and sizes, and finally on the Compton parameter profile. Fig. 4. Distribution of pixels for the MILCA PACT y-map, in cyan for the tSZ signal, and in orange for the noise, the solid black line shows a Gaussian fit adjusted to the noise distribution.
Significance of tSZ detections For each cluster of the union sample displayed in Fig. 7  as a dot, we estimate with the MMF technique the size, the flux, and the S/N at the reported positions. We leave the discussion on the size-flux degeneracy to the next section and focus only on the S/N. The cluster properties are computed with the same MMF from the ACT data (orange dots) and Planck data (blue dots), i. e. two and six frequency channels respectively, and from the PACT dataset, i. e. the ensemble of eight frequency channels. We show in Fig. 7  the ratio of the S/N obtained from PACT to the S/N obtained from the native dataset (ACT or Planck) with the same MMF as a function of the cluster size. Figure  7  highlights the improvement on the detection signal-to-noise ratio both for extended clusters and more compact clusters, although a few clusters have a lower S/N when combining ACT and Planck data.
We first notice that for most of the ACT and Planck clusters the S/N improves when we use the PACT combined data rather than the native ACT(Planck) data to estimate the signal-to-noise ratio. The improvement can reach up to a factor three higher than the ACT(Planck) native S/N. In the case of ACT (orange dots), the highest improvements correspond to clusters with the largest sizes. In these cases, the PACT combination provides the large angular scales of Planck, missing from the ACT data, which also filter out the CMB and astrophysical contamination. In the case of Planck (blue dots), the highest improvements in S/N are reached for clusters with small angular sizes. The PACT dataset provides the small scales of ACT that are missing in Planck low-resolution data. For clusters with intermediate sizes, the average improvement in S/N induced by the use of the  PACT dataset is of order 50%.
We also note from Fig. 7  a few clusters (less than 10% of the full sample) exhibit smaller S/N values when the PACT dataset is used. Such a downward fluctuation of the S/N despite the use of more data was already noted by Planck Collaboration I (2017) (while the S/N improved for most SZ sources about 23% had their S/N reduced below the detection limit of 4. 5 σ). These downward-fluctuated S/N were associated with weak detections likely to be Eddington biased above the thresh- old. We note in the present analysis of PACT dataset that the clusters with reduced S/N are low significance ones. The modification of the background noise in the aggregated PACT dataset and the low significance of the tSZ detections result in downward-fluctuated detections. For the most nearby and thus extended clusters (such as A3395 at redshift z = 0.
Size and tSZ flux relation When we use the combined PACT dataset for our MMF analysis (red dots) this induces an improvement over both ACT and Planck alone. This is triggered by the improved measurement of the sizes. Extended clusters in ACT benefit from the Planck large scale signal and multiple frequency which reduces the contamination and hence the Y 500 estimate (orange crosses and arrows pointing to the left in Fig. 8 ). Unresolved clusters in Planck benefit from the small scales of ACT which permits to actually measure the cluster size and in turn the integrated Compton parameter Y 500 (blue crosses and arrows pointing to the right). Finally, for the smallest sizes the dominant contribution of the ACT data in the PACT combination reduces the noise level and further improves the tSZ estimates.