dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Cheng et al. 2016 Cheng et al. , 2020;; Sun et al. 2018) , whose formalism assumes implicitly fixed line ratios. For [CI], we had fewer constraints and we propose new empirical recipes based on a recent observational compilation. The new SIDES code is modular and can be easily modified to include new observational results. The version presented in this paper is a compromise between simplicity and being realistic. It was not fine-tuned, since the observational constraints were well reproduced overall on the first try. Since the code is public, documented, and modular, different methods can easily be implemented by other users, based on their preferences and access to new observational data. The code has been optimized to be able to produce a catalog or data cube on a laptop in a few tens of minutes, which is ideal for performing tests and exploring the impact of the various parameters.
(2015)  showed that 5 < z < 6 galaxies still follow the L        L DL14 [CII] L        = 7. 06 + log 10 SFR M yr -1. (1) This result was confirmed by the ALPINE large program  (Le Fèvre et al. 2020; Béthermin et al. 2020; Faisst et al. 2020) , which targeted 118 normal galaxies at 4. 4 < z < 5. 9, and result after reanalyzing archival data and correcting for the flux loss caused by the excessively extended configuration used in the early observations. We thus used the DL14 relation (Eq. (  1 )) to derive [CII] luminosities from the SFR in the SIDES simulation. As pointed out by  Ferrara et al. (2019) , massive high-redshift galaxies correspond to much higher  [CII]  surface brightness regimes than the initial low-redshift DL14 observations and it is almost surprising that this relation remains valid under these physical conditions.
2 dex of scatter) presented in Fig. 1. In this section and contrary to Eq. (  4 ), all luminosities are expressed in normal power units (i. e. , in multiples of watts), such as solar luminosities 5. We found that the ratio between the [CI](1-0) line luminosity (L  [CI] (1-0) ) and the total infrared luminosity (L IR ), and the ratio between L CO(4-3) and L IR , are correlated with a dispersion of 0. 2 dex (see Fig. 1  upper panel): log 10 L [CI](1-0) L IR = 1. 07 log 10 L CO(4-3) L IR + 0. 14. ( 9 ) We used the CO(4-3) transition instead of a lower-energy one because of the larger high-z observational dataset available to calibrate our relation. For the typical range of values of L CO(4-3) /L IR in the  Valentino et al. (2020b)  study (10 -6 -10 -4 ), the mean L [CI](1-0) /L CO(4-3) is in the range between 0.
We then used the histogramdd 3D histogram routine of the numpy package  (Harris et al. 2020 ) that used the line fluxes as weights to generate the cube and normalize each of its frequency slices using 6 The conversion between the velocity width, the frequency width, and the redshift width (used hereafter) are obtained in the following way: δυ = c δν obs ν obs = c νrestδz (1+z) 2 ν obs = c δz (1+z). Eq. (  11 ). This operation was performed for each transition of each line. To produce the continuum cube, we computed the flux density of each galaxy at the central frequency of each spectral element using a parallelized code. For each frequency, we then produced a map using the histogram2d histogram routine, applying the flux density at this frequency as weight. The maps corresponding to all the frequency slices were then stacked to produce the cube.
A pilot deep survey has been performed by  Walter et al. (2014)  using the Plateau de Bure interferometer. This survey covered a single pointing (∼1 arcmin diameter) in the 79-115 GHz range. The ALMA spectroscopic survey (ASPECS) started by a first pilot survey with a similar size, covering band 3 (84-115 GHz) and band 6 (212-272 GHz) with an improved depth  (Walter et al. 2016; Decarli et al. 2016 ). Finally, a large program extended the coverage to a 4. 6 arcmin 2 region  (Decarli et al. 2019 (Decarli et al. , 2020)). In Fig. 6  we compare the luminosity function in our simulation with the measurements from these various surveys. The band 3 and the band 6 windows correspond to a different redshift range for each line. This offers a wide variety of constraints on the various transitions and redshifts.
The SIDES luminosity functions are derived from the simulated catalog using a volume corresponding to a [z cen -0. 1, z cen + 0. 1] redshift range, where z cen is the central redshift of the ASPECS measurement. We used the apparent luminosity in this computation for consistency, since the observations have not been corrected for lensing magnification. However, this effect is negligible in the luminosity regime probed by the observation. The SIDES  simulation is always in the 1-σ range of the observations. However, for the higher-J transitions (J upper ≥ 4), the simulation tends to be systematically on the lower end of the confidence interval. This could be due to a field-to-field variance effect, since the field covered by the ASPECS large program was only 4. 6 arcmin 2. A companion paper will demonstrate that the field-to-field variance caused by large-scale structures is non-negligible (Gkogkou et al.
Computation of power spectra per frequency slices and link with 3D power spectra In Sect. 4. 3, we compared the 3D power spectra of various models. In this section, we instead use angular power spectra of 1 GHz frequency slices. We simply calculated the angular power spectrum of each 1 GHz spectral cube slice using the powspec package 8. Working in angular wavenumber and frequency is closer to the data that the instruments produce. It also allows us to produce figures that are independent of the choice of a reference line for the projection into the 3D physical space. Finally, the low spectral resolution of CONCERTO will not allow us to probe the small scales in the radial direction. Projected to the physical space, the angular resolution (22 arcsec at 305 GHz) and the frequency resolution are very different.
Conclusion We have presented a new model and its associated public code to generate submillimeter and millimeter intensity mapping simulated cubes from dark-matter light cones. This new model produces realistic dust continuum SEDs that evolve with redshift and with an intrinsic scatter in radiation-field intensity, and thus temperature. The various CO transitions are generated using the combination of two SLEDs (a clump and a diffuse medium). The fraction of each component is also linked to the radiation field. The CO SLEDs of galaxies thus evolve with z and have an intrinsic scatter. [CI] is generated using empirical relations. Finally, [CII] is generated using several prescriptions based on recent [CII] studies and includes a scatter. Our model thus includes a certain level of complexity to test future analysis methods without being too computationally demanding.
With our simulation in input, we will be able to perform end-to-end simulations of the full process, and identify and correct the main biases of the analysis pipelines. The new version of the SIDES simulation is thus a powerful tool for preparing and interpreting future intensity mapping experiments. It can also be used for interferometric spectral scans and photometric surveys (e. g. , to forecast the number of detections, properties of the detections, and end-to-end simulations). A companion paper  (Gkogkou et al. , in prep. )  will present a larger simulation and discuss the field-to-field variance on these various probes of galaxy evolution. The code and its products are publicly available 9. The code has been developed to be flexible and easily modifiable to adapt to the needs of the community.
Acknowledgements. We thank the referee for their insightful comments. We warmly thank  Bin Yue ,  Dongwoo Chung ,  Marco Viero ,  Gergö Popping , and  Shengqi Yang  for sharing their model predictions and discussing them. We thank  Gareth Keating  for suggesting to test our simulation on the mmIME results and his explanations. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  788212 ) and from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d'Avenir" program. M. A. acknowledges support from  FONDECYT  grant  1211951 ,  ANID+PCI+INSTITUTO MAX PLANCK DE ASTRONOMIA MPG 190030 ,  ANID+PCI+REDES 190194  and  ANID   BASAL  project  FB210003.
Previously used to carry out absolute quantification in mass spectrometry, this technique yields highly accurate measurements of protein abundance. Unfortunately, it has some key drawbacks. It is time-consuming and costly, and, more importantly, it can only quantify a limited number of proteins at a time (in general, less than 100)  [10, 11]. The latter issue is quite limiting in the context of systems biology, given that absolute quantification is needed for large proteomics datasets (i. e. , covering 1000s of proteins) if they are to be properly integrated into genome-scale metabolic models, where protein abundances may serve as upper limits  [12, 13]. In addition, by making available more proteomics datasets that use absolute values, it would be easier to carry out comparative studies across laboratories even when data have been acquired with different devices.
Indeed, UPS2 is costly and is not available year-round. Furthermore, there is no consensus on either the ratio (protein standard/proteome background) or the amount of UPS2 required to develop a suitable standard of reference. However, there is a clear need to optimize this strategy by reducing the amount of UPS2 needed while maximizing the number of proteins that can be detected. This situation, therefore, calls for the development of a practical, robust label-free technique for carrying out semi-absolute protein quantification that will yield large proteomics datasets of sufficient accuracy to be integrated into metabolic models. To this end, we evaluated fourteen different label-free semi-absolute quantification techniques (seven quantification methods × two transformation strategies). Our study system was chemostat cultures of the budding yeast Saccharomyces cerevisiae (CEN.
5, 1 M KCl), and anaerobic. Each set of conditions was independently replicated three times, resulting in a total of 15 samples (5 conditions × 3 replicates). Sampling was carried out as described elsewhere  [32]. Here, we used five different stress conditions of the model yeast S. cerevisiae to assess the semi-absolute protein quantification in a realistic dataset, with multiple phenotypes. This motivation aimed to identify the impact of the different quantification techniques on the peptides and proteins abundance trends observed between the different stress responses.
Protein Identification To identify the proteins, we used a custom-made database (see the Supplementary Methods), which contained genomic information for S. cerevisiae CEN. PK113-7D (UNIPROT, accessed on 14 September 2017) and S. cerevisiae s288c (accessed on UNIPROT, 3 November 2017); the UPS protein database (Sigma); and a database of common contaminants. Database searches were performed using the X!Tandem algorithm (Alanine 1 February 2017) implemented in the open-source search engine X!TandemPipeline (v. 3. 4. 3; https: //forgemia. inra. fr/pappso/xtpcpp (accessed on 5 January 2022))  [33]. Enzymatic cleavage was defined as trypsin digestion with one possible miscleavage. The settings for the carboxyamidomethylation of cysteine residues and the oxidation of methionine residues were static modifications and potential modifications, respectively.
Precursor and fragment mass tolerance was 10 ppm. The data filtering standards were as follows: peptide E-value < 0. 01, protein log(E-value) < -3, and a minimum of one identified peptide per protein. Using such filtering criteria, the peptide and protein false discovery rates (FDR) were 0. 04% and 0. 68%, respectively. The mass spectrometry data were deposited online in the public database PRO-TICdb  [34] [35] [36]  (repository: http://moulon. inra. fr/protic/chassy_saccharomyces_absolute (accessed on 5 January 2022)) and in the Proteomics Identification Database  [37]  (PRIDE; dataset identifiers PXD014765 and PXD012836). PRIDE is a member of the ProteomeXchange Consortium.
Protein Quantification Protein abundance was quantified using SC-and XIC-based methods (Figure  1B ). The R code that we used is available at https://forgemia. inra. fr/aaron. millan-oropeza/ protquanter-saq/ (accessed on 5 January 2022). Four SC-based methods were employed. The first was the protein abundance index (PAI), in which the observed number of peptides is divided by the expected number of peptides  [3]. The second was the exponentially modified PAI (emPAI), which is equal to 10ˆPAI minus one  [4]. The third was the spectral abundance factor (SAF), in which the SC for a given protein is divided by the protein's length (L). The fourth was the normalized spectral abundance factor (NSAF), which is defined for a given protein i as follows: NSAF i = (SC/L) i ∑ N j=1 (SC/L) (1) where the number of SCs identified for protein i is divided by the protein's length (L) in amino acids, and the result is then divided by the sum of SC/L for all the proteins (N) in the experiment  [38].
Data Analysis We used R  [43]  (v. 3. 5. 1, R Core Team, Vienna, Austria) to quantify protein abundance and carry out the data analyses. The performance of the different techniques (the seven quantification methods × the two transformation strategies) was assessed with various metrics. These metrics were (i) accuracy intra-samples, as estimated by bootstrapping the results for the UPS2 proteins (see the Supplementary Methods for details); (ii) the coefficient of determination (r 2 ) obtained from the linear regression between the expected and observed abundances of UPS2 proteins (fmol) for a given quantification method; (iii) repeatability, as estimated among all the bulk samples using all the results for the UPS2 proteins (i. e. , the CV among replicates); (iv) the variability associated with the results for the UPS2 proteins, which was evaluated for each order of magnitude of concentration and reported as a median value (i.
Performance of the Quantification Methods with the UPS2 Proteins The abundances of the 28 UPS2 proteins detected in the bulk samples were quantified using seven different methods (based on SC: PAI, emPAI, SAF, and NSAF; based on XIC: SUMnorm, Top3, and iBAQ; Figure  1B ). Prior to carrying out the final regression models, a Cook's distance analysis  [45]  was performed to identify any outliers that could negatively affect the linear models. An outlier was defined as a point with a Cook's distance of more than three times the value of the mean (µ). Based on the results of this analysis, the proteins UBIQ (P62988), COS5 (P01031), and SYUG (O76070) were removed from the dataset. The short length of these proteins coupled with the proximity of their arginine and lysine residues resulted in small peptides that were poorly detected after mass fragmentation.
In similar studies performed at our proteomics facility using different yeast strains and bacteria (data not shown), COS5 and SYUG were also systematically removed from datasets, suggesting that these proteins cannot be properly quantified via label-free shotgun techniques. Linear regression analysis was thus carried out using the data for the remaining 25 UPS2 proteins. The relationships between the expected and observed protein abundances for the different quantification methods yielded various standards of reference (File S2). Overall, the regression parameters and statistical measures (i. e. , intercept, slope, and coefficient of determination) were reproducible across the bulk samples for each quantification method. The six independent bulk sample replicates were employed to establish a final standard of reference that was used to determine the linear regression parameters for estimating the absolute abundance (in fmol) of the yeast proteins.
This finding fits with the accuracy among samples observed in our study (Figure  3 ); it contrasts with what we observed for the absolute error of the external proteins (Figure  4B ). Indeed, TPA offers the advantage of not relying on external standards. Taking the results together, the NSAF-UPS2, PAI-UPS2, and SAF-UPS2 techniques displayed a good balance between performance (linearity, reproducibility, accuracy) and the number of proteins quantified. We performed a PCA on the protein abundances (fmol) estimated using the three semi-absolute quantification techniques with the lowest median absolute error with UPS2 approach (Figure  S4 ). In the PCA, the five sets of culture conditions were clearly differentiated (>33% of variance considering the PC1 and PC2), indicating that these quantification techniques could distinguish among the biological features of datasets obtained from different proteome backgrounds.
Conclusions In this study, using five different proteome backgrounds, we demonstrated the feasibility of utilizing two transformation strategies-TPA and a UPS2-based approach-as the foundation for label-free semi-absolute quantification. Based on the performance of the fourteen different techniques that we tested, we recommend employing an SC-based quantification method-PAI, SAF, or NSAF-in conjunction with the UPS2-based transformation strategy when calculating protein abundances (fmol) in complex mixtures. The techniques tested in this study remain imperfect, given that the CVs we obtained were inferior to those associated with methods based on isotope labeling (e. g. , AQUA, SILAC). However, they do represent a potentially helpful tool for performing semi-absolute quantification in studies where thousands of proteins are being examined and the goal is to generate datasets that can be integrated into metabolic models.
8 fmol and Ribonuclease A from bovine pancreas (RNAS1) at 342. 6 fmol (n = 4). Figure  S4. Principal component analysis of the absolute abundances (fmol) of yeast proteins from the selected SC-based (UP2 approach) quantification methods NSAF, SAF and PAI. As well as the XIC-based methods iBAQ, TOP3 and SUMnorm (TPA). File S1. Proteins identified using X!TandemPipeline. File S2. Results of the linear regressions for the seven different quantification methods performed using the data from the bulk samples spiked with UPS2 proteins. File S3. Protein abundances (fmol) for S. cervisiae CEN. PK113-7D grown under the five sets of culture conditions. Estimates were obtained using the 14 different techniques (7 quantification methods × 2 transformation strategies).
Acknowledgments: The proteomics analyses were performed at the PAPPSO platform (http:// pappso. inra. fr(accessed on  4 January 2022 )), which is funded by  INRAE  (http://www. inrae. fr(accessed on  4 January 2022 )), the  Ile-de-France regional council  (https://www. iledefrance. fr/educationrecherche(accessed on  4 January 2022 )),  IBiSA  (https://www. ibisa. net(accessed on  4 January 2022 )), and  CNRS  (http://www. cnrs. fr(accessed on  4 January 2022 )). We thank  Olivier Langella  for making bioinformatic improvements to the search engine tools, and we wish to acknowledge the contributions of our colleagues in the CHASSY consortium.
Estimation of host contamination We estimated the host contamination in the ovary and midgut quality ltered short reads using the software phyloFlash  [34]  v3. 4. We used the "phyloFlash. pl" script with the "--readlength 150" and "-almosteverything" ags that i) extracted the small-subunit rRNA (SSU rRNA) sequences from our data by mapping the SILVA SSU Ref database  [35] , ii) assembled the extracted reads into SSU rRNA using SPAdes [36] assembler, iii) estimated the proportion of assembled by re-mapping them to the full-length SSU sequences, iv) provided a taxonomic summary of the reads from the initial mapping, the full-length SSU rRNA sequences and the unassembled sequences. We plotted the taxonomic assignment of the mapped reads on the SILVA SSU databases using a custom R script (R version 3.
Metagenomic assembly and binning We performed metagenomic analyses using anvi'o v7  [38, 39]  and the metagenomic snakemake  [40]  work ow from the quality ltering to the merging of pro le databases generated for each organ separately using the "anvi-run-work ow" program and the "--work ow metagenomics" ag. All the parameters used to set the snakemake work ows are written in the 'con g. json' les (one le by organ) available in the Code Availability Section. Although the ovaries raw reads have already been analyzed in  [32] , we herein reanalyzed both ovaries and midguts raw reads following an exactly similar protocol in order to generate consistent analyses and comparable results for both organs. Brie y, during each work ow, we quality-ltered the raw reads from each sample using illumina-utils  [41]  v2.
10 and the "iulter-quality-minoche" anvi'o program with default parameters. We assembled quality-ltered reads into contigs using MEGAHIT  [42]  v1. 2. 9, keeping only contigs with a length > 1000 nt. We performed read recruitment analyses by mapping the quality-ltered reads from all ovary samples onto the contigs of each ovary sample with "all against all" ag in con g les using Bowtie2  [43]  v2. 3. 5. 1 and repeated the same procedure for midgut samples. We then used the "anvi-gen-contigs-database" program to generate anvi'o contigs databases for each individual assembly. This program computed k-mer frequencies for each contig, soft-splits contigs with longer > 20,000 pb into smaller ones and identi ed ORF in each contig using Prodigal  [44]  v2. 6. 3. We used the "anvi-run-hmms" program to identify HMM hits searching against the HMM sources included by default in anvi'o (Bacteria_71, Archaea_76 and Protista_83) and the "anvi-run-ncbi-cogs" program to assign functions to genes which search gene amino acid sequences against the COG20  [45]  database using blastp [46] v2.
10. 1. We used the "anvi-pro le" program to compute the coverage per nucleotide position and statistics for each metagenome assembly using the BAM les. We merged the resulting anvi'o pro les using the "anvi-merge" program. After the metagenomic snakemake work ow, we performed an automatic genome binning from assemblies using the "anvicluster-contigs" anvi'o program and the CONCOCT  [47]  algorithm ("--driver CONCOCT" ag) with a speci c number of cluster ("--clusters X" ag) by sample (See Supplementary Table  3 ) to perform an automatic binning with the best possible ratio of completion and redundancy for bacterial bins (checked with "anviestimate-genome-completeness" program). Finally, we manually re ned bacterial bins obtained in each sample using the interactive program "anvi-re ne" to discard redundancy (and contamination) from bins as much as possible.
Single Nucleotide Variants (SNVs), Single Codon Variants (SCVs) and Single Amino Acid Variants (SAAVs) We used the "anvi-gen-variability-pro le" program to generate a table that summarized the variability of Single Nucleotide Variants (SNVs, "--engine NT" parameter), Single Codon Variants (SCVs, "--engine CDN") and Single Amino Acid Variants (SAAVs, "--engine AA") stored in the anvi'o pro le databases of i) quality ltered reads from each sample mapped on each corresponding sample Wolbachia MAGs (for intra-MAG variability), ii) quality ltered reads from all samples mapped on each Wolbachia MAGs (for inter-MAG variability). We ltered SNVs, SCVs and SAAVs by removing the ones with entropy < 0. 2 (20%) and departure from consensus < 0. 2 (20%) in order to discard those that could be due to sequencing errors and therefore considered as noise (https://merenlab.