dataesr/scientific-paragraphs-categorization huggingface dataset:
com/brooklabteam/Mada-Bat-CoV/. Briefly, our full genome ML phylogeny was comprised of 122 unique NCBI records, corresponding to all available full genome sequences with bat hosts under NCBI taxon IDs, Betacoronavirus (694002), unclassified Betacoronavirus (696098), Betacoronavirus sp. (1928434), unclassified Coronaviridae (1986197), or unclassified Coronavirinae (2664420) (107 records), in addition to all full genome Betacoronavirus (694002) reference sequences with a non-bat host (14 records), plus one Gammacoronavirus outgroup (accession number NC_010800). The full genome phylogeny additionally included three full length Madagascar Nobecovirus sequences returned from CZID (two from R. madagascariensis and one from P. rufus), which are described in this paper for the first time, for a total of 125 unique sequences.
Our amino acid phylogenies consisted of S, E, M, and N gene extractions from a subset of the same representative set of near-full genome length sequences used in the RdRp analysis: the same 17 full-length Betacoronavirus reference sequences, 17 near full-length Nobecovirus sequences, and the one Gammacoronavirus outgroup, in addition to our three full genome Madagascar sequences derived from R. madagascariensis and P. rufus. Gene extractions were carried out using annotation tracks reported with each accession number in NCBI or, in cases where annotations were unavailable, genes were manually annotated and extracted in Geneious Prime based on alignment to homologs. After nucleotide extraction, genes were translated prior to alignment. Finally, our Bayesian time-resolved Nobecovirus phylogeny consisted of all available full genome Nobecovirus sequences in NCBI virus; because recombination events can muddle molecular clock estimation in phylogenetics, we constructed two different versions of this timetree, one including 18 Nobecoviruses sequences only for which no past history of recombination has been described, and a second which added two additional sequences from the Nobecovirus GCCDC1 clade known for its p10 orthoreovirus insertion.
After compiling sequences for each phylogenetic analysis, sequence subsets for the full-length, RdRp, and four amino acid phylogenies were aligned in MAFFT v. 7 (81, 82) using default parameter values. Alignments were checked manually for quality in Geneious Prime, and the RdRp alignment was trimmed a fragment (259 bp) conserved across all sequences in the subset. All sequence subsets and alignment files are available for public access in our GitHub repository: https://github. com/ brooklabteam/Mada-Bat-CoV/. After quality control, alignments were sent to Modeltest-NG  (83)  to assess the best fit nucleotide or amino acid substitution model appropriate for the data. All alignments for ML analysis (full genome, 259 bp RdRp fragment, and amino acid protein sequences) were then sent to RAxML-NG  (84)  to construct the corresponding phylogenetic trees.
We tested trees using both an uncorrelated exponentially distributed relaxed molecular clock (UCED) and a strict clock but ultimately reported results from the strict clock assumptions, as similar results were inferred from both. Markov chain Monte Carlo (MCMC) sample chains were run for 600 million iterations, convergence was checked using TRACER v1. 7  (88) , and trees were averaged after 10% burnin using TreeAnnotater v2. 6. 3  (89)  to visualize mean posterior densities at each node. All resulting phylogenies (both ML and Bayesian) were visualized in R v. 4. 0. 3 for MacIntosh, using the package 'ggtree'  (90).
After alignment, genomes were analyzed for amino acid similarity in the program pySimplot (91), using the P. rufus and, subsequently, the R. madagascariensis genome as query sequences, the HKU9 and Eidolon helvum African Nobecovirus clades as references, and the corresponding Madagascar sequence as the alternative. Analyses were carried out using a window size of 100 aa and a step size of 20 aa. Next, all three full length nucleotide sequences of Madagascar Nobecovirus genomes were aligned with grouped full genome sequences corresponding to the two disparate Nobecovirus lineages: the HKU9 lineage (EF065514-EF065516, HM211098-HM211100, MG693170, NC_009021, MG762674) and the E. helvum African lineage (MG693169, MG693171-MG693172, NC_048212). As before, alignment was conducted in MAFFT v. 7  (81, 82)  using default parameter values.
After alignment, genomes were analyzed for recombination in the program SimPlot (v. 3. 5. 1). Nucleotide similarity plots were generated using the P. rufus and, subsequently, the R. madagascariensis genomes as query sequences, the HKU9 and Eidolon helvum African Nobecovirus clades as references, and the corresponding Madagascar sequence as the alternative. Bootscan analyses were conducted on the same alignment, using the same query and reference inputs. Both nucleotide similarity and Bootscan analyses were carried out using a window size of 200 bp and a step size of 20 bp.
Nucleotide Sequence Accession Numbers All three annotated full-length genome sequences (two from R. madagascariensis, one from P. rufus), plus four additional RdRp gene fragment sequences (two from R. madagascariensis, one from P. rufus, and one from E. dupreanum) were submitted to NCBI and assigned accession numbers OK020086-OK020089 (RdRp fragments) and OK067319-OK067321 (full genomes).
This finding highlights CoVs' capacity to undergo both homologous (within-clade) and heterologous (out-of-clade) recombination. Since bats are known to maintain co-infections with many viruses simultaneously  (103, 104) , this recombinatory potential could allow CoVs to rapidly acquire new genetic material from coinfections, including receptor binding or host immune evasion capabilities that may expand host range. The orthoreovirus insertion within the GCCDC1 virus genome was not detected among the CoVs in our dataset, though, anecdotally, mNGS of fecal and throat samples collected in our sampling did identify evidence of orthoreovirus infection in several throat swabs derived from R. madagascariensis bats, highlighting the potential for recombination opportunities between these two viral groups.
ACKNOWLEDGMENTS The authors acknowledge  Kimberly Rivera  and  Sarah Guth  for help in the field and the lab and the  Virology Unit at the Institut Pasteur de Madagascar and Maira Phelps of the Chan Zuckerberg Biohub (CZB)  for logistical support. They additionally thank  Angela Detweiler ,  Michelle Tan , and  Norma Neff  of the CZB genomics platform for mNGS support and thank the  Brook lab at the University of Chicago  for helpful contributions to the manuscript.
FUNDING Research was funded by the  National Institutes of Health  ( 1R01AI129822-01 grant  to  J-MH , PD, and CB),  DARPA  ( PREEMPT Program Cooperative Agreement  no. D18AC00031  to CB), the  Bill and Melinda Gates Foundation  ( GCE/ID OPP1211841  to CB and J-MH), the Adolph C. and  Mary Sprague Miller Institute for Basic Research in Science (postdoctoral fellowship  to CB), the  Branco Weiss Society in Science (fellowship to CB) , and the  Chan Zuckerberg Biohub.
75-1, 1-3, 3-10 and 10-30 MeV, and if not otherwise stated, the same energy bands will be adopted in the present paper. Event selection parameters used in this paper are discussed in Sect. 3. 3. 2 https://doi. org/10. 5281/zenodo. 6462842 3 Throughout this paper we denote by instrumental background all events that are not related to celestial gamma-ray emission.
Each viewing period comprises a single EVP and TIM file, OAD files are provided per day. EVP data are handled by the GammaLib class GCOMEventList, TIM data by GCOMTim, and OAD data by GCOMOad and GCOMOads, where GCOMOad implements a single OAD record (or superpacket), and GCOMOads implements a collection of OAD records. These data structures are combined in an unbinned COMPTEL observation, implemented by the GammaLib class GCOMObservation. To construct an unbinned COMPTEL observation, the input data for a viewing period can be specified either by their file names or via a so-called observation definition XML file. The HEASARC archive mixes different versions of EVP files that have different levels of processing for the ToF values. GammaLib will automatically correct for these differences, assuring that ToF values accessed through GammaLib are always ToF III values (see Appendix B).
Only events for modules that are signalled as active are considered for the event binning. The detector module status is provided by the GCOMStatus class that relies on a database of daily status values that is provided with GammaLib. During the COMPTEL operations a certain number of photomultiplier tubes (PMTs) of the D2 modules failed, degrading the localisation precision of the event interactions in the respective modules. One option is to exclude these modules from the analysis, reducing the number of available events by about 25%. Alternatively, events from zones around the faulty PMTs can be excluded in the analysis, allowing recovery of a large fraction of the events for the analysis. Both analysis options are implemented in GammaLib (see Appendix E) and are explored in the analysis of LS  5039 (cf.
Sect. 4. 3). COMPTEL event cubes are stored as three-dimensional FITS images with the file type designation DRE. Projections of an event cube generated using GammaLib for viewing period 2. 0 6 and total energies in the interval 1-3 MeV are shown in Fig. 2  where for comparison the projections of an equivalent DRE obtained using the COMPASS software are shown. The projections of the datasets are nearly indistinguishable, illustrating that the event cubes generated by GammaLib are equivalent to those produced by the COMPASS software.
Sect. 3. 3). The exposure map is given in units of cm 2 s and is stored as a two-dimensional FITS image with the file type designation DRX. For illustration, Fig. 3  compares projections of the DRX obtained by GammaLib for viewing period 2. 0 to the projections for an equivalent DRX that was obtained by COMPASS. The projections of the datasets are indistinguishable, illustrating that the exposure maps generated by GammaLib are equivalent to those produced by the COMPASS software.
Similar to the computation of the exposure map, the superpackets {S } considered are those that satisfy the same selection criteria that were applied to the event selection. Details are provided in Appendix F. The computation of the geometry function is implemented in the method GCOMDri::compute_drg and results are stored as three-dimensional FITS images with the file type designation DRG. Figure  4  illustrates the result of the geometry function computation, again in the form of projections for viewing period 2. 0. Equivalent projections for a geometry function obtained using the COMPASS system are superimposed. The projections of the datasets are indistinguishable, illustrating that the geometry functions generated by GammaLib are equivalent to those produced by the COMPASS software.
ctools scripts To support the science analysis of COMPTEL data, we extended the ctools package with a number of dedicated Python scripts, providing basic building blocks that each perform well-defined science data analysis tasks. This includes the generation of a database based on the data available in the HEASARC archive, the selection of COMPTEL viewing periods from this database, event binning and response computation, data combination, background modelling, model fitting, generation of test statistic (TS) maps, residual inspection, observation simulations, and source detection, as well as generation of pulsar pulse profiles. These scripts complement the already existing generic science analysis tools in ctools that may be used in combination with the new scripts. Table  2  summarises the COMPTEL-specific scripts that we added to ctools.
Before starting a COMPTEL data analysis, a database needs to be generated from the data that are available in the HEASARC archive. This analysis step, which only needs to be performed once on a given computer, is performed by the comgendb script. Once the database is generated, a typical COMPTEL analysis starts with executing comobsselect to select the relevant viewing periods for a given source or source region and observing time interval from the database. Results are provided as an observation definition file in XML format, which contains all the relevant information for the subsequent analysis steps. Following selection, the data are binned into three-dimensional event cubes and the corresponding DRX and DRG cubes are generated using comobsbin. The binning can be done for an arbitrary number of energy bands, enabling a joint spectral-spatial analysis that was not supported by the original COMPASS software.
comobsbin also computes an initial background model DRB using the PHINOR algorithm (see Sect. 4. 1), as well as the IAQ response matrices for the relevant energy bands. All output files are stored as FITS files in a data store, avoiding the recomputation of identical data files in subsequent analyses. Alternative background models using the algorithms described in Sect. 3. 5 can be generated using comobsback. Individual viewing periods can be combined into single event cubes for each energy band {E} using the comobsadd script, leading to a considerable speed-up of the data analysis. The combination of viewing periods is however not required, and alternatively the data can be analysed using a joint maximumlikelihood analysis of the selected viewing periods, a possibility that was not supported by the original COMPASS software.
The script then uses the ctlike tool to fit the source and background models to the binned data. The celestial source model that results from the fit is then used to update the DRM model and to regenerate a background model using the selected algorithm. A new ctlike fit is then performed using the updated model, and the procedure is repeated until the maximum log-likelihood change between subsequent iterations becomes negligible, typically less than 0. 05. Usually, the SRCLIX algorithm converges after a few iterations. A84, page 8 of 31 Figure  7  illustrates the algorithm by showing TS maps for subsequent SRCLIX iterations for viewing period 1. 0 that were obtained using the cttsmap tool. The TS is defined as  (Mattox et al. 1996) , where ln L(M s + M b ) is the log-likelihood value obtained when fitting the source and the background models together to the data, and ln L(M b ) is the log-likelihood value obtained when fitting only the background model to the data.
We note that DRM designates here the model cube that comprises both the source and background components. By default, comobsres uses R(α, δ) = sgn(N(α, δ) -M(α, δ)) × 2 N(α, δ) ln N(α, δ) M(α, δ) + M(α, δ) -N(α, δ) (26) to compute the significance of the residuals R(α, δ) in Gaussian σ, where the sign term indicates whether the measured number of counts is larger or smaller than the number of counts predicted by the model. Some special cases need to be treated separately. Namely, if N(α, δ) = 0 the residual significance is R(α, δ) = sgn(N(α, δ) -M(α, δ)) 2M(α, δ), (27) while if M(α, δ) = 0 the significance cannot be computed and we set R(α, δ) = 0. The comobssim script enables the simulation of DRE event cubes by sampling the events according to a Poisson distribution using the expectation given by a model.
Pulsar and nebula components We now turn to an analysis that separates the emission from the Crab pulsar and the pulsar wind nebula to validate the implementation of the pulsar phase computations. The most comprehensive analysis of the emission from the Crab pulsar and pulsar wind nebula components using COMPTEL data was performed by  Kuiper et al. (2001)  using data collected over the nine years mission duration of CGRO. While  Kuiper et al. (2001)  used data from 33 viewing periods with pointing axis within 30 • of the Crab pulsar, we analysed 24 viewing periods that we found with the same selection criteria in the HEASARC database, covering the viewing periods specified in Table  1  of  Kuiper et al. (2001)  between viewing period 1. 0 and viewing period 616. 1. As in the analysis above, viewing period 426.
0 was excluded from the list since no usable EVP file exists for this observation in the HEASARC database. Similarly to  Kuiper et al. (2001)  we used ephemerides for the Crab pulsar from the Princeton radio pulsar database, provided in the form of an ASCII file named psrtime. dat 8 that is part of the reference data of the X-ray Timing Explorer (XTE) module of the HEASoft software (version 6. 29)  9. We first used compulbin with an angular resolution measure of ±3. 5 • to produce pulse profiles for the four standard COMPTEL energy bands, as displayed in Fig. 2  of  Kuiper et al. (2001). The results of this analysis are shown in Fig. 13 , on which we overlay for comparison the pulse profiles obtained by  Kuiper et al. (2001). Since the  Kuiper et al. (2001)  profiles were obtained for a larger dataset and angular resolution measures that were not specified in their paper, we scaled the profiles so that the minimum and maximum number of events in the profiles matches the numbers that we obtained in our analysis.
We also applied a phase shift of -0. 03 to the pulse profiles of  Kuiper et al. (2001)  to match them to our profiles. While we do not know the origin of this small discrepancy in the pulse phases, we note that a phase shift of -0. 03 corresponds to a difference of about 0. 5 arcsec in the assumed right ascension of the Crab pulsar. In GammaLib, the pulsar position is taken from the ephemerides file, which in the present case is the radio position in the Princeton database, while  Kuiper et al. (2001)  do not specify the position that they assumed for the Crab pulsar. We note that the radio position in the Princeton database differs by about 0. 5 arcsec from the International Celestial Reference System (ICRS) position provided by the Set of Identifications, Measurements, and Bibliography for Astronomical Data (SIMBAD) service 10 , which could be at the origin of the observed phase shift.
5 • , -0. 5 • ) from the HEASARC database. In total this resulted in a list of 41 viewing periods, starting with viewing period 5. 0 and ending with viewing period 712. 0. Up to viewing period 712. 0 our list is identical to Table  1  of  Collmar & Zhang (2014) , yet the authors included 12 more viewing periods in their analysis that are not available in the HEASARC archive. For our analysis we combined the data in a data space of 140 × 123 × 25 bins of 1 • × 1 • × 2 • in size and centred on (l, b) = (15. 0 • , -4. 5 • ), which corresponds to the same dimensions that were used by  Collmar & Zhang (2014)  in some of their analyses. We used the four standard COMPTEL energy bands for our analysis. Similar to  Collmar & Zhang (2014)  we used the binary ephemeris of  Casares et al. (2005) , which is an orbital period of 3.
26 Al line emission from Carina As the next step we validated the capacities of GammaLib and ctools for gamma-ray line emission analysis together with its abilities to assess the spatial morphology of the emission. As reference, we chose the COMPTEL detection of point-like 1. 8 MeV line emission from the Carina region for this validation, as reported by  Knödlseder (1994)  and  Knödlseder et al. (1996a) , which are to our knowledge the only published studies where an analysis using a parametric spatial model was performed with COMPTEL. In their studies the authors analysed COMPTEL data from viewing periods 1 to 301 combined in a data space of 100 × 100 × 25 bins of 1 • × 1 • × 2 • in size, centred on (l, b) = (286. 5 • , 0. 5 • ), which corresponds to the peak position of the observed 1. 8 MeV line emission feature.
(1996a) , yet we tested an alternative analysis method that jointly handles the 26 Al line signal and any underlying continuum emission. This is more in line with the GammaLib and ctools philosophy of explicitly modelling all emission components and provides a more accurate handling of underlying continuum emission. Specifically, we split the data within the 1-3 MeV energy interval into 11 energy bins, specified in Table  3 , and analysed them jointly using a combination of model components describing the 1. 8 MeV line signal, any underlying continuum gamma-ray emission and the instrumental background. To model the 1. 8 MeV line emission A84, page 18 of 31 spectrum we used a Gaussian spectral component with a fixed mean of 1. 809 MeV and a standard deviation of σ = 58. 9 keV that corresponds to COMPTEL's instrumental energy resolution at that energy.
Conclusion We have implemented a comprehensive science analysis framework for COMPTEL gamma-ray data in the GammaLib and ctools software packages, and we have demonstrated that our software reliably reproduces published analysis results that were derived using the COMPASS software. Having public, free, and validated software for COMPTEL science data analysis now opens the HEASARC COMPTEL archive to the community for scientific exploration. In the medium-energy gamma-ray band, from 1-30 MeV, the COMPTEL archive still contains the most sensitive observations ever performed, and a unique dataset for exploring the non-thermal Universe and nuclear transition lines. Since the 1990s when the COMPTEL data were taken, the field of gamma-ray astronomy has made impressive progress thanks to satellites such as the International Gamma-Ray Astrophysics Laboratory (INTEGRAL) and Fermi and ground-based observatories such as the High Energy Stereoscopic System (H.
E. S. S. ), the Major Atmospheric Gamma-ray Imaging Cherenkov Telescope (MAGIC), and the Very Energetic Radiation Imaging Telescope Array System (VERITAS). Many source classes that are known today were not established as gamma-ray emitters during the COMPTEL era, and the COMPTEL data were never comprehensively analysed with the current knowledge in the field. An exception to this is the post-mission discovery of the orbital modulation of MeV gamma-ray emission of LS 5039 in the COMPTEL archival data by  Collmar & Zhang (2014) , a source that was not an established gamma-ray emitter in the 1990s. Thanks to GammaLib and ctools, such discoveries are now achievable by the community at large. We want to stress that our work was also motivated by the goal of reducing the carbon footprint of astronomical research.
The HEASARC archive was created from the original COMPTEL data using dedicated file conversion software that generated FITS files following recognised standards  (Pence et al. 2010). While interpreting these FITS files using Gam-maLib we found that the units in some of the table columns were not correct. We summarise the inconsistencies that we encountered in Table  A. 2. We furthermore found that the good time interval dataset for viewing period 8. 0 with the identifier MPE-TIM-11481 only contains about half of the good time intervals that were defined in COMPASS, probably as the result of a file truncation error in the input ASCII file that was used for the generation of the FITS file in the HEASARC archive. The HEASARC archive also comprises binned data products for standard energy bands that can be used for analysis using GammaLib, yet we note that the world coordinate system information attached to these data products is incorrect.
While the FITS headers suggest that the data cubes are provided in Mercator projection, the event cubes are presented in a cartesian pro- given in Mercator projection GammaLib assumes that they originate from the HEASARC archive and corrects the world coordinate system information upon reading of the event cubes. The HEASARC archive comprises further data products such as telescope housekeeping data, gamma-ray burst detector data and sky maps that are not used by GammaLib.