dataesr/scientific-paragraphs-categorization huggingface dataset:
Support of Astropy The Astropy project, as of the version 2. 0 release, does not receive any direct financial support for the development of astropy. Development of the software, all supporting materials, and community support are provided by individuals who work on the Astropy project in their own personal time, by individuals or groups contributing to Astropy as part of a research project, or contributions from institutions that allocate people to work on Astropy. A list of organizations that have contributed to Astropy in this manner can be found in the Acknowledgments. Different funding models have been proposed for support of Astropy (e. g. ,  Muna et al. 2016 ), but a long-term plan for sustainability has not yet been established. The Astropy project has the ability to accept financial contributions from institutions or individuals through the NumFOCUS 10  organization.
Each Frame class defines its own component names and preferred Representation class. The frame-specific component names map to corresponding components on the underlying Representation object that internally stores the coordinate data. For most frames, the preferred representation is spherical, although this is determined primarily by the common use in the astronomical community. Many of the Frame classes also have attributes specific to the corresponding reference system that allow the user to specify the frame. For example, the Fifth Fundamental Catalogue (FK5) reference system requires specifying an equinox to determine the reference frame. If required, these additional frame attributes must be specified along with the coordinate data when a Frame object is created. Figure  2  shows the network of possible reference frame transformations as currently implemented in astropy.
The Astropy Collaboration The typical user does not usually have to interact with the Frame or Representation classes directly. Instead, astropy. coordinates provides a highlevel interface to representing astronomical coordinates through the SkyCoord class, which was designed to provide a single class that accepts a wide range of possible inputs. It supports coordinate data in any coordinate frame in any representation by internally using the Frame and Representation classes. In what follows, we briefly highlight key new features in astropy. coordinates.
Proper motion and velocity transformations In addition to positional coordinate data, the Frame classes now also support velocity data. As the default representation for most frames is spherical, most of the Frame classes expect proper motion and radial velocity components to specify the velocity information. The names of the proper motion components all start with pm and adopt the same longitude and latitude names as the positional components. Transforming coordinates with velocity data is also supported, but in some cases the transformed velocity components have limited accuracy because the transformations are done numerically instead of analytically. The low-level interface for specifying and transforming velocity data is currently experimental. As such, in version 2. 0, only the Frame classes (and not the SkyCoord class) support handling velocities.
nddata The astropy. nddata subpackage provides three types of functionality: an abstract interface for representing generic arbitrary-dimensional datasets intended primarily for subclassing by developers of other packages, concrete classes building on this interface, and utilities for manipulating these kind of datasets. The NDDataBase class provides the abstract interface for gridded data with attributes for accessing metadata, the world coordinate system (WCS), uncertainty arrays matched to the shape of the data, and other traits. Building on this interface, the NDData class provides a minimal working implementation for storing numpy arrays. These classes serve as useful base classes for package authors wishing to develop their own classes for specific use cases and as containers for exchanging gridded data.
The classes NDDataRef, NDDataArray, and CCDData extend the base storage functionality with options to do basic arithmetic (addition, subtraction, multiplication, and division), including error propagation in limited cases, and slicing of the dataset based on grid coordinates that appropriately handles masking, errors, and units (if present). Additionally, the CCDData class also provides reading and writing from and to FITS files and uses data structures from astropy, like WCS, to represent the file contents abstractly. The astropy. nddata. utils module provides utilities that can operate on either plain numpy arrays or any of the classes in the astropy. nddata subpackage. It features a class for representing two-dimensional image cutouts, allowing one to easily link pixels in the cutout to those in the original image or vice versa, to convert between world and pixel coordinates in the cutout, and to overlay the cutout on images.
FITS The astropy. io. fits subpackage started as a direct port of the PyFITS project  (Barrett & Bridgman 1999). Therefore, it is pretty stable, with mostly bug fixes but also a few new features and performance improvements. The API remains mostly compatible with PyFITS, which is now deprecated in favor of astropy. Command-line scripts are now available for printing a summary of the HDUs in FITS file(s) (fitsinfo) and for printing the header information to the screen in a human-readable format (fitsheader). FITS files are now loaded lazily by default, i. e. , an object representing the list of HDUs is created but the data are not loaded into memory until requested. This approach should provide substantial speed-ups when using the convenience functions (e. g. , getheader() or getdata()) to get an HDU that is near the beginning in a file with many HDUs.
Lomb-Scargle Periodograms Periodic analysis of unevenly-spaced time series is common across many sub-fields of astronomy. The astropy. stats package now includes several efficient implementations of the Lomb-Scargle periodogram  (Lomb 1976; Scargle 1982)  and several generalizations, including floating mean models  (Zechmeister & Kürster 2009) , truncated Fourier models  (Bretthorst 2003) , and appropriate handling of heteroscedastic uncertainties. Importantly, the implementations make use of several fast and scalable computational approaches (e. g. ,  Press & Rybicki 1989; Palmer 2009) , and thus can be applied to much larger datasets than Lomb-Scargle algorithms available in, e. g. , scipy. stats (Jones et al. 2001-). Much of the Lomb-Scargle code in astropy has been adapted from previously-published open-source code  (VanderPlas et al.
The Astropy community is supported by and makes use of a number of organizations and services outside the traditional academic community. We thank Google for financing and organizing the Google Summer of Code (GSoC) program, that has funded severals students per year to work on Astropy related projects over the summer. These students often turn into long-term contributors. We also thank NumFOCUS and the Python Software Foundation for financial support. Within the academic community, we thank institutions that make it possible that astronomers and other developers on their staff can contribute their time to the development of Astropy projects. We would like acknowledge the support of the Space Telescope Science Institute, Harvard-Smithsonian Center for Astrophysics, and the South African Astronomical Observatory.
Figure7. Three approaches to a 1D histogram: left: a standard histogram using matplotlib's default of 10 bins. center: a histogram with the number of equal-width bins determined automatically using numpy's bins='auto'. right: a histogram created with astropy, with irregularly-spaced bins computed via the Bayesian Blocks algorithm. Compared to regularly-spaced bins, the irregular bin widths give a more accurate visual representation of features in the dataset at various scales.
The AGP subset of healthy individuals was determined by filtering by the metadata columns "subset_antibiotic," "subset_ibd," and "subset_diabetes" and, for individuals over the age of 16 years, "subset_bmi. " All data sets were processed using the Deblur pipeline as noted above, with the exception that all reads in the meta-analysis, including AGP data, were trimmed to 100 nt to accommodate the read length in the work of Yatsunenko et al. (6). Bloom reads as described above were removed from all samples. We used Striped UniFrac as noted above to estimate beta-diversity (unweighted UniFrac) and EMPeror software (66) version 0. 9 to visualize principal coordinates. We used a nonparametric PERMANOVA with 999 permutations to test for significant differences in fecal microbiomes associated with industrialized, remote agrarian, and hunter-gatherer lifestyles.
0 to 4. 0 min and an m/z range of 595. 345 to 595. 355. The stercobilin reference compound was used to assess the variability of chromatographic retention times to ensure that the retention times of compounds of interest (LA and CLA in particular) were correctly identified. After isotope peak removal, the peak lists of all samples were aligned within the corresponding retention time and mass tolerances. Gap filling was performed on the aligned peak list using the peak finder module with 1% intensity, 10-ppm m/z tolerance, and 0. 05-min RT tolerance, respectively. After the creation and export of a feature matrix containing the feature retention times, exact mass, and peak areas of the corresponding extracted ion chromatograms, we added sample metadata to the feature matrix metadata of the samples.
We then computed Pearson and Spearman correlations of the effect sizes, per metric, between the bloom and bloom-removed result (Fig. 2C  and D ). In addition to the effect size calculations, we also tested whether the bloom fraction was correlated with any metadata category and did not observe significant correlations. We then tested the removal of blooms from other studies in which room-temperature shipping was not performed by retrieving a wide variety of human fecal studies from Qiita. UniFrac distance matrices were computed prior to and following bloom removal, followed by Mantel tests. The results of this procedure are outlined in Table  S4. Finally, we correlated the relative intensities of the HPLC-MS data associated with the antibiotic and plant cohorts against the fraction of blooming reads.
The bloom typically observed in these samples (and in the full AGP data set) is E. coli (ID 04195686f2b70585790ec75320de0d6f from reference 15), although a few of the other bloom sequences were represented at high read fraction as well. Samples were retrieved from Ϫ80°C and thawed on ice. The swab head was broken off into 500 l sterile 1ϫ Dulbecco's phosphatebuffered saline and vortexed vigorously for 30 s. Serial dilutions from this initial stock were made, including 1:10,000 and 1:1,000,000. Ten microliters of the 1:10,000 dilution was inoculated into 1. 5 ml sterile tryptic soy broth (TSB; BD catalog no. 2253534) in sterile 96-deep-well plates (community cultures [CC]) and incubated overnight at 37°C on an orbital shaker at 500 rpm. Values of optical density at 600 nm (OD 600 ) above 0.
For each isolate, contigs with abnormally high or low coverage as defined by the 1. 5ϫ interquartile range (IQR) rule were dropped. The characterization of the metabolites from the supernatant using HPLC-MS is discussed under "HPLC-MS analysis" above. Following assembly of the draft genomes, taxonomic assessment by Kraken (95) revealed that of the 119 successfully sequenced colony isolate cultures, 95 matched the bloom organisms identified by Amir et al. (15). Compellingly, 70 of these isolate genomes contained exact 16S sequence matches to a bloom organism identified by reference 15, including 65 which matched the dominant E. coli bloom in the American Gut (Table  S4 ). Biosynthetic gene cluster analysis of isolates. The read data for the isolates were then assessed for predicted biosynthetic gene clusters (BGCs).
Colibactin triggers DNA double-strand breaks in eukaryotic cells  (96, 97)  and induces cellular senescence and metabolic reprogramming in affected mammalian cells  (98). Of the 11 samples containing the longest colibactin-like BGC, 10 of them contained the exact E. coli bloom 16S sequence described above; the 11th isolate was actually a canine fecal sample plated alongside human samples (as the AGP allows participants to submit pet samples). Although colibactin is frequently harbored by various E. coli strains, the variations of colibactin BGCs across various isolates have not been studied before. Genomic analysis revealed wide variations in colibactin-like BGCs, suggesting that various strains produce related but not identical variants of natural products  (99). These variations may give rise to the suite of LysoPE-associated spectra identified between the 16S and HPLC-MS data sets.
To investigate the metabolic potential of the gut microbiome for producing conjugated linoleic acid from linoleic acid, we estimated the abundance of linoleic acid isomerase (LAI) in the fecal metagenome. We focused this investigation on the "plant" cohort, which were samples selected to maximize the difference between the number of types of plant metadata category as discussed above. First, we translated the assembled metagenomes to metaproteomes using Prodigal gene prediction software. To map LAI to these metaproteomes, we used a representative LAI protein sequence (UniProt D2BQ64), which was matched against UniProtKB (via https://www. ebi. ac. uk/Tools/hmmer/) for multiple sequence alignment (MSA). The resulting MSA file in Clustal format was then used to generate a hidden Markov model (HMM) profile for LAI using hmmbuild in HMMER software  (100).
The results were converted to BIOM tables, filtered to retain valid samples only (see above), and collapsed from family to the three types of classes (see above). The relative abundance table containing antibiotic classes was used for downstream analysis. For each metadata category, the upper and lower extrema were selected for comparison. The Kruskal-Wallis H test (using the SciPy command Kruskal) was performed to assess the differential abundance between upper and lower extrema for each of the nine antibiotic classes. The H statistic and P value were reported for each antibiotic class. Specifically, for the cohort "Abx," the metadata column "antibiotic_history" was tested. Categories "Week" and "Month" were merged into "within 1 month"; "I have not taken antibiotics in the past year. " was abbreviated as "beyond 1 year.
" Other categories were omitted. For the cohort "plant," metadata column "types_of_plants" was tested. Categories "less than 5" and "6 to 10" were merged into "10 or less"; "More than 30" was retained; others were omitted. Filtering bacterial blooms for metabolomics analysis. To assess and account for the impact of the metabolites contributed by these organisms, we have performed HPLC-MS analysis of cultures of blooming organisms to establish possible contributions, as described above. It was found that there is a negligible overlap of the bloom-associated metabolites with the compounds detected in AGP samples (Fig. 2B ). Furthermore, we have verified that none of the compounds discussed in this work (LA, CLA, and compounds in Fig. S4A  to I) are present in these bloom cultures. The main organism implicated in bloom was determined to be E.
coli, as described earlier, and MS data corroborate these findings (network not shown). Considering that the metabolites resulting from microbial activity in cultures can differ significantly from those in vivo (e. g. , many of the metabolites could originate not from de novo synthesis but rather from microbial modifications of external compounds that are not present in medium, e. g. , from the host), we also explored associations of metabolites in AGP metabolomics samples and blooms. Spearman rank correlation analysis of the fraction of 16S reads in a sample reporting as bloom to metabolites observed in the same samples revealed several features that correlate significantly (Table  S5 ). There exists a significant overlap between the antibiotic and VioScreen study subsets, indicating a potential common origin of these features.
The remaining samples were further filtered down to 125 samples to include only high-quality fecal microbiome data (at least 1,250 sequences/ sample) at a single time point per subject. For those cases, we created a 1:1 matched sample of patients and nonpsychiatric comparison (NC) participants based on age (Ϯ5 years), BMI, history of diabetes, smoking frequency, country of residence, census region (if in the United States), and sequencing plate. For each of the cohorts, we calculated beta-diversity distance matrices using Bray-Curtis dissimilarity and weighted UniFrac. On the resulting matrices, we ran pairwise PERMANOVA with 999 permutations between "cases" (people who reported mental illness) and NCs (outmatched control data set). Differential abundance testing was performed using the permuted mean difference test at 10,000 permutations, with discrete FDR (112) correction at alpha ϭ 0.
Determination of model performance The parameters used as the model database were adjusted to validate and calibrate the model by obtaining simulated solutions, optimally close to the observed data  (Abrishamchi et al. , 2007). The performance criteria are expressed by the ratio of the simulated and the observed flows, and are estimated statistically by calculating the performance indices  (Koffi, 2007). According to  Miossec (2004) , the evaluation criteria are not universal, the principle is to compare the simulated and the observed flows. In this study, we used the following criteria: 2. 3. 1. The mean square error RMSE RMSE n OV SV OV i i i = - ae è ç ç ö ø ÷ ÷ é ë ê ê ù û ú ú å 1 2 ( ) ( 6 ) where n is the number of observations ; OV i is observed volume i; SV i is simulated volume i.
Acknowledgments We thank the director and staff of the  LMI-TREMA laboratory at Cadi Ayad University , especially  Salah Er-Raki  and  Said Khaba. The staff of the CESBIO of Paul Sabatier University Toulouse 3, particularly the coordinator of the  PHC Maghreb  project  N °32592VE ,  Valerie Ledantec. Giles Boulet ,  Michel Lepage. Kuper Marcel  and  Khaldi Abdelkader  are also gratefully acknowledged.
Data Analyses Peak lists and resulting files were searched against the UniProt-Swiss-Prot database (2014_10 version Homo sapiens taxonomy) using Phenyx 2. 6 software (Gene Bio, Geneva, Switzerland). Variable amino acid modifications were oxidized methionine, glutamine, and asparagine deamidation, and TMT-labeled peptides amino terminus (þ229. 1629 Da if required). TMT-labeled lysine (if concerned) and carbamidomethylation of cysteines were set as fixed modification. 18 rypsin was selected as the enzyme, with one potential missed cleavage. Only proteins matching two different and unique peptide sequences were selected for identification and quantification. A false discovery rate of 1% was selected. Protein quantification was obtained from Mascat method in Easyprot (version 2. 3) as statistical tools in order to calculate the proteins ratios between the CSCR SRF (tag 130) and the RRD SRFs (tag 128).
Chemometric Analysis Raw UHPLC-HRMS data were converted to appropriate format to be processed by XCMS online software (The Scripps Research Institute, San Diego, CA) for peak detection, chromatogram alignment, and isotope annotation. 22 Preprocessed data were normalized by using sample-wise mean normalization as well as feature-wise Pareto scaling. The assessment of the metabolic patterns was driven by the use of unsupervised and supervised learning approaches, including prediction analysis for microarrays data mining (PAM) into the R software (Version 3. 3. 0, R Foundation for Statistical Computing, R Core Team, 2016, Vienna, Austria. http://www. R-project. org) and the 'pamr' package, as well as the online Metaboanalyst tool (http://www. metaboanalyst. ca/). PAM classifier gives the opportunity to keep only the subset of features that maximized the model performance in predicting class membership.
Biological Process and Pathway Analysis The Panther software (Protein Analysis Through Evolutionary Relationships, version 10. 0; www. pan therdb. org) was used to analyze the protein lists in order to generate the gene ontology categories (protein class). 25 The overrepresentation of the pathways in which the differential proteins of the CSCR SRF compared with the RRD SRF are involved was performed with QIAGEN's Ingenuity Pathway Analysis (IPA; QIAGEN Redwood City, CA). Overrepresentation analysis on the metabolomics data was performed by Consensuspathdb (http://con sensuspathdb. org) to obtain accurate insight into the underlying biology of differentially expressed metabolites exploiting the KEGG database (www. genome. jp/kegg/pathway. html). Enrichment set analysis was appreciated by 4150 identifications for human species included in the KEGG library.
Cytoscape software (Version 3. 2. 1, http://cytoscape. org) and the Met-Scape plugin (Version 3. 1. 2; metscape. ncibi. org/) were used to build the compound-gene metabolic network. 26 From the set of genes obtained in the compound-gene network, gene ontology terms were integrated and functionally organized in a gene ontology network function using the ClueGO plugin (Version 2. 2. 5; apps. cytoscape. org/apps/cluego). 27.
Subretinal Fluid Metabolome After matching against known metabolites in the HMDB database using the mass to charge ratio (m/z), the putative metabolite list was reduced to 651 single molecules. The supervised learning approach was successful in reconstructing sample classes, based on the differentially expressed metabolites between CSCR and RRD. In the SRF sample from the CSCR patient, 76 metabolites were differentially regulated as compared with RRD of which 43 were downregulated and 33 were upregulated (see Supplementary Table  S3  for a list of differentially regulated metabolites). Overrepresentation pathway analysis was performed on the deregulated metabolites (Fig. 4 ). It confirmed the proteomics results with the protein digestion and absorption pathway (P ¼ 3EÀ9) defined by approximately 20% coverage of inputted metabolites (specifically methionine, phenylalanine, valine, tyrosine, leucine, glutamine, arginine, isovalerylcarni- tine, and tyramine-o-sulfate), which is in accordance with the peptidase and protease inhibitor activity differentially regulated within the enzyme in the proteomics analysis.
22). The chemical subclassification of metabolites deregulated in CSCR, as compared with RRD, was obtained from the HMDB library. The largest subgroup (30%) belonged to the amino acid/peptide subclass, confirming the overrepresentation pathway analysis (see Supplementary Fig. S1  for a chart of deregulated metabolites). Using this classification system, the next highly susceptible metabolite classes were phosphosphingolipids (12%), fatty acids/conjugates (7%), and fatty acid esters (6%), which demonstrated alterations in lipid metabolism, in agreement with the proteomic results showing deregulation of the LXR/RXR pathway. Finally, the pathway-based network was explored to create a compound-gene network on the MetScape software (Fig. 5 ), providing a comprehensive overview of the specific SRF metabolic signature in the CSCR sample.
The most increased proteins in RRD SRF identified by Shitama et al. 63  were albumin, transferrin, transthyretin, and cathepsin D. Other proteins, identified as potential markers of RRD were a-1 antitrypsin, clusterin (also known as apolipopretein J), pigment epithelium-derived factor (PEDF), and apolipoprotein A-IV (APOA4). In contrast, our proteome analysis of SRF in RRD could not identify albumin, transferrin or a-1 antitrypsin that had been depleted among the 12 major plasma proteins removed at the initial step of our protocol. Yet, we identified transthyretin, clusterin, cathepsin D, PEDF, and APOA4 in the SRF from patients with RRD (See Supplementary Table  S1 ). Moreover, as proteomic methods differ between studies,  54, 55, 64  direct comparison of the SRF proteomes to published datasets is not possible.