dataesr/scientific-paragraphs-categorization huggingface dataset:
32% SAMN19677498 52. 72 M 45. 19 52. 43 M (99. 45%) 19. 37 M 11. 37% 652. 53 M 39. 01 M 9. 22 M 5. 33 M 45. 95% 7. 14% 1. 21% SAMN19677502 44. 98 M 38. 56 43. 82 M (97. 42%) 17. 55 M 17. 89% 467. 65 M 22. 14 M 1. 02 M 3. 50 M 33. 85% 1. 21% 1. 10% SAMN19677506 53. 95 M 46. 24 53. 10 M (98. 43%) 20. 23 M 16.
Observing setup The data are recorded with the versatile NUPPI backend  (Desvignes et al. 2011)  based on a CASPER 4  ROACH 5  board. For each of the two linear polarisations, a 512 MHz bandwidth is first digitised using 8-bit analogue-to-digital converters. Then an 8-tap polyphase filter bank using a Hamming window function implemented in the Xilinx Virtex 5 field-programmable gate array (FPGA) splits the band into 1024 channels. Chunks of contiguous 64 MHz baseband data are sent to four servers equipped with two Graphics Processing Units (GPUs) each. There, the NUPPI software, adapted from the Green Bank Ultimate Pulsar Processing Instrument (GUPPI) software, is used to detect the signals from the two linear complex polarisations (i. e. sum the polarisation to form Stokes I) and to average in time every 64 µs before writing the data in PSRFITS 4-bit search mode format.
The 8×64 MHz sub-bands are later merged together using a modified version of PSRFITS_UTILS 6. The total expected amount of data for the survey was therefore around 53 TB. The minimum flux density S min of a pulsar with spin period P s detectable by the SPAN512 survey can be written as S min = S/N × (T sky + T sys ) G δ n p t obs ∆ f W eff P s -W eff [mJy], (4) where S/N, T sky , T sys , n p ,t obs , ∆ f ,W eff denote the pulse signal-tonoise ratio (S/N), sky and system temperature contributions, the number of polarisations summed, the duration of the observation, the observing bandwidth, and the effective pulse width of the pulsar, respectively. Here we take S/N = 8, T sky + T sys = 35 K, n p = 2, ∆ f = 512 MHz, and assume a pulse duty-cycle W eff /P s = 5%. As this survey was designed to achieve a sensitivity at the lowest pointing Declination of SPAN512 (δ ∼ 38 G δ = 1.
Dispersion removal To detect pulsars with a priori unknown dispersion measure (DM), that is, the integrated free electron column density along the line of sight, we mitigate the frequency-dependent dispersive delay by shifting the frequency channels using a wide range of DM trials. We set the maximum value of DM to 3000 pc cm -3 , which is ten times the maximum value predicted by the NE2001 model  (Cordes & Lazio 2002)  of the Galactic distribution of free electrons for the different lines of sight of the SPAN512 survey. By doing this, we can account for some DM excess due to, for example, the presence of a H II region on our line of sight and remain sensitive to high-DM extragalactic fast radio bursts (FRBs). The time resolution of the data decreases by a power-of-two downsampling factor when the dispersive smearing inside a single channel exceeds the sample duration.
Candidate ranking and visualisation A set of ratings is applied to all folded candidates, including the Pulsar Evaluation Algorithm for Candidate Extraction (PEACE) scoring software  (Lee et al. 2013 ) and the Pulsar Image-based Classification System (PICS) neural-network based artificial intelligence (AI), trained with P-ALFA candidates  (Zhu et al. 2014 ). P-ALFA and SPAN512 candidates have similar observing frequency and bandwidth, and share the same folding pipeline. Reusing a neural net trained on the candidates of a different survey may be suboptimal, but the SPAN512 survey did not redetect enough known pulsars to retrain it. The metadata of all candidates are stored in a NoSQL MongoDB database for ease in data mining. We developed PSRHUNT 7 , a Python 7 https://github. com/gdesvignes/psrhunt Qt-based user interface with Matplotlib support  (Hunter 2007) , to connect to the MongoDB database and identify common periodicities of radio frequency interferences across different pointings.
This allowed us to skim more efficiently through the best candidates, similar to the REAPER  (Faulkner et al. 2004 ) and JREAPER software  (Keith et al. 2009). PSR J2139+4716 is a radio-quiet pulsar discovered using Fermi LAT data  (Pletsch et al. 2012). Two others (PSRs J0413+58 and J2206+6151) lie at unobserved survey grid positions. Table  3  reports the S/N for the redetection of 6 previously known pulsars and the 6 remaining known pulsars not redetected by the survey are noted. Inspection of Fig. 3  shows that all six undetected pulsars lie between or at the end of beam swaths. We used the Python notebook SPAN512-BEAMS. IPYNB available online 8 to accurately estimate the sensitivity of the SPAN512 survey at a given sky location.
Known pulsars PSRs J0111+6624, J2115+5448 and J2215+5135 lie in between observation swathes, with estimated G of respectively 0. 32, 0. 25, and 0. 24 K Jy -1 and were not redetected. Figure  4  illustrates this for PSR J2215+5135. Moreover, PSR J0111+6624 has a nulling fraction of 0. 33  (Lynch et al. 2018). For these three sources, we folded the raw PSRFITS data of the closest pointing using the timing ephemeris available from the ATNF Pulsar Catalogue, but again, no pulsations were detected. PSR J2203+50 is a slow pulsar discovered at 350 MHz with poorly constrained localisation  (Hessels et al. 2008). MSPs J0329+50 and J2051+50 were discovered at 820 MHz using the Green Bank telescope, in 30 min pointings of Fermi LAT pulsarlike unidentified gamma-ray sources  (Tabassum et al. , in prep.
New detections In addition to nine detections of previously known pulsars, the SPAN512 survey discovered two new MSPs PSR J2055+3829 and PSR J2205+6012, and the slow pulsar PSR J2048+4951. Guillemot et al. (2019)  described the discovery and timing of PSR J2055+3829. We report the remaining two here. Follow-up NRT observations were recorded with the NUPPI backend in regular timing observing mode (see e. g. Guillemot et al. 2019). Data postprocessing uses the PSRCHIVE pulsar software  (Hotan et al. 2004; van Straten et al. 2012). The data are calibrated in polarisation using polarised noise diode data obtained preceding each observation. Regular observations of 8 https://github. com/gdesvignes/SPAN512 A79, page 5 of 13 A&A 667, A79 (2022)  the quasar 3C286 and the radio galaxy 3C123 are also used to flux-calibrate the data.
PSR J2048+4951 PSR J2048+4951 is a slow pulsar with P s = 568 ms and DM = 223 pc cm -3. Between 2013 and 2019, we performed 56 followup observations at L-band with the NRT. The TOAs of the daily-averaged and frequency-summed pulse profiles were modelled with the TEMPO2  (Hobbs et al. 2006 ) timing software and the Jet Propulsion Laboratory (JPL) DE438 Solar System ephemeris. The timing residuals are shown in Fig. 5  and the bestfit parameters are reported in Table  4. We used this ephemeris to integrate all calibrated pulse profiles and build the high-S/N profile shown in Fig. 6. From this high-S/N pulse profile, we determine the rotation measure (RM) -the frequency-dependent rotation of the position angle of the linear polarisation of the radio waves due to propagation in the magnetised interstellar medium -to be RM = -196(10) rad m -2.
PSR J2205+6012 The SPAN512 survey discovered the millisecond pulsar J2205+6012 in 2013, with P s = 2. 41 ms and DM ∼ 157 pc cm -3 , in a 1. 1-day orbit. Since then, it has been regularly monitored at the NRT with its L-band receiver. Since mid-2019, PSR J2205+6012 has been observed monthly with the Effelsberg radio telescope at S-band (defined as 2-4 GHz) with a few observations at C-band (4-8 GHz). Due to the low number of C-band observations, these data were not included in the timing analysis but were used to constrain the pulsar spectrum. All Effelsberg data are recorded with the PSRIX backend and calibrated in polarisation and flux using observations of a pulsed noise diode and the planetary nebula NGC 7027. More information about the PSRIX system is provided by  Lazarus (2016). With only the last two years of our dataset providing multifrequency coverage at S-band, we split the large fractional bandwidth of the NUPPI backend into four subbands.
The Effelsberg data are fully summed in frequency. Templates for the L-band and S-band averaged pulse profile are built from Notes. We used the JPL DE438 Solar System ephemeris and the Barycentric Coordinate Time (TCB) timescale in our TEM PO2 timing analysis. The values in parentheses represent the 1σ error bars reported by TEMPO2. de-noised time-integrated pulse profiles with the wavelet noise removal software PSRSMOOTH from PSRCHIVE. Before determining the TOAs through cross-correlation of the pulse profiles with the high-S/N templates, the NRT daily pulse profiles are fully integrated in time (from 30 min to 1. 5 h) while the Effelsberg profiles are integrated for up to 35 min. In total, we have 852 and 47 TOAs at L-band and S-band, respectively. Following  Desvignes et al. (2016) , we used TEMPONEST  (Lentati et al.
A and γ are respectively sampled from a log-uniform prior in the log 10 range  [-20,-8 ] and a uniform prior in the range [0,7], for both DM and TN modelling. See also  Caballero et al. (2016)  for more details. MULTINEST  (Feroz et al. 2009 ) sampling uses 2000 live points. Table  5  lists the timing parameters derived from the TEM-PONEST posterior distributions. A Tempo 9 ephemeris is also available online for monitoring by other radio telescopes 9 http://tempo. sourceforge. net/ (see footnote 8). With our current dataset, the TN remains unconstrained (see Fig. 7 ) and we can set the 95% confidence upper limit on its amplitude A TN with log 10 (A 95% TN ) < -13. 2. The DM noise is well constrained and we can reconstruct the DM waveform showing variations of about 0. 02 pc cm -3 during the last 8 yr.
While the DM noise is well constrained, the TN remains unconstrained with our eight-year dataset. suggests that PSR J2205+6012 might be an orthogonal rotator. Unfortunately, the L-band profile is quite scatter broadened and the S-band profile shows little linear polarisation, with a narrow duty cycle. Assuming a dipolar magnetic field, the Rotating Vector Model (RVM;  Radhakrishnan & Cooke 1969)  describes the position angle (PA) of the linear polarisation as the projection of the magnetic field lines rotates with the magnetic axis around the spin axis of the pulsar. Unfortunately, fitting the RVM to the PA does not reliably constrain the magnetic inclination angle or viewing geometry of the pulsar and we are not able to confirm whether or not PSR J2205+6012 is indeed an orthogonal rotator.
Gamma-ray pulsations In the 10 34 ≤ Ė < 10 35 erg s -1 decade where PSR J2205+6012 lies, with Ė = 4π 2 I 0 Ṗs /P 3 s being the spindown power (assuming a moment of inertia for the neutron star of I 0 = 10 45 g cm -2 ), over 70% of MSPs show gamma-ray pulsations in Fermi Notes. Our TEMPONEST timing analysis makes use of the JPL DE438 Solar System ephemeris and the TCB timescale. For the measured timing parameters, the numbers in parentheses represent the 1σ uncertainty taken as the 68% confidence levels around the median value for the posterior of each parameter. As the TN is not constrained, we report the 95% confidence upper limit for the amplitude, A 95% TN. The rms residuals are computed after subtraction of the DM model. LAT data  (Smith et al. 2019). In comparison, ∼ 30% of MSPs in the next lower decade (like PSR J2055+3829, Ė = 4.
We find that the SPAN512 survey should detect between 9 and 25 non-recycled pulsars (taken as the 95% probability contours). Our detection (discovery) of 7 (1) young non-recycled pulsars is below the predicted range. One reason may be that the predictions are sensitive to the P, L, and scale-height parameters, which may overestimate the pulsar population in the survey region. Another cause described by  Lazarus et al. (2015)  is the presence of RFI and red noise in the data that degrades sensitivity towards slow pulsars compared to the theoretical prediction from the radiometer equation (Eq. (  4 )). Similar issues may have caused our pipeline to miss some pulsars. For MSPs, an adaptation of the simple population synthesis of  Johnston et al. (2020)  using parameter distributions similar to those of  Levin et al.
Conclusions We present SPAN512, a new survey for pulsars and transients with the Nançay Radio Telescope, with observations made between 2012 and 2016. We described the processing pipeline, which is designed to remain sensitive to high-DM millisecond pulsars and to binaries with orbital periods 3 h. The survey discovered one non-recycled pulsar and two MSPs, and redetected all previously known pulsars in the survey sky region bright enough for the sensitivity of the survey. Some unknown pulsars could have potentially been missed due to the incomplete survey. We characterise the newly discovered MSP using Nançay and Effelsberg radio data, and using gamma-ray data from the Large Area Telescope on the Fermi satellite. A comparison of the yield of the survey with preliminary population synthesis predictions suggests that far from the Galactic centre and the Galactic plane, the ratio of MSPs to non-recycled pulsars may be larger than currently believed.
We timed PSR J2205+6012 for 8 yr with sub-microsecond precision. The TEMPONEST noise and timing analysis shows that the current timing precision is only limited by radiometer noise. A TOA median uncertainty at 2. 5 GHz of 0. 6 µs and a timing rms of 0. 75 µs set this pulsar as one of the most precisely timed EPTA pulsars  (Desvignes et al. 2016; Chen et al. 2021). This promises exciting prospects for future observations A79, page 11 of 13 A&A 667, A79 (2022)  with ultra-wide-band receivers being commissioned at various telescopes in the Northern hemisphere such as Effelsberg and the Green Bank Telescope. Meanwhile, this dataset will be included in a future EPTA data release. With the highly scattered pulse profile observed at L-band and the large DM variations inferred from our analysis, novel wide-band pulsar timing techniques such as PULSEPORTRAITURE  (Pennucci 2019)  or the Bayesian profile domain timing software TEMPONEST2  (Lentati et al.
Acknowledgements. The authors acknowledge the use of the MPIfR Hercules cluster hosted at the Max Planck Computing and Data Facility in Garching and the  CNRS/IN2P3's Computing Centre. We also acknowledge support from the  CNRS/IN2P3 Computing Center (Lyon -France ) for providing computing and data-processing resources needed for this work. The Nançay Radio Observatory is operated by the  Paris Observatory , associated with the  French Centre National de la Recherche Scientifique. This publication is partly based on observations with the 100-m telescope of the  MPIfR (Max-Planck-Institut für Radioastronomie) at Effelsberg. This research has made extensive use of  NASA 's Astrophysics Data System. The  Fermi LAT Collaboration  acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis.
After a WGD, orthologous genes are expected to be embedded in orthologous neighborhoods. For each individual gene tree, SCORPiOs "crowd-sources" additional information from local syntenic genes to identify errors in orthology relationships. SCORPiOs then reorganizes those gene trees to accurately position the WGD duplication node, if the synteny-consistent solution is equally supported by the sequence alignment. These 26,692 WGD-aware teleost gene trees predict that the ancestral genome of teleost fish contained 46,206 genes after the duplication event, in line with the latest estimates from the Ensembl database (49,255 ancestral teleost genes in release v100; Methods).
Libraries and packages Scripts to build the comparative atlas were written in Python 3. 6. 8 and assembled together in a pipeline with Snakemake version 5. 13. 0  (Köster and Rahmann 2012). The ete3 package (version 3. 1. 1) was used for phylogenetic gene tree manipulation and drawing. Other Python package dependencies used for plots and analyses include Matplotlib (version 3. 1. 1), seaborn (version 0. 9. 0), numpy (version 1. 18. 4), pandas (version 0. 24. 2), pingouin (version 0. 3. 4) for Wilcoxon paired tests and multiple testing correction, and SciPy (version 1. 4. 1) for Fisher's exact tests. Chromosome painting and synteny comparisons were drawn with the RIdeogram R package  (Hao et al. 2020).
Genomic resources Genome assemblies and annotations for the 101 vertebrate genomes were downloaded from various sources, including the NCBI, Ensembl version 95 and GigaDB. The source and assembly version used for each genome are listed in Supplemental Table  S1. The gene coordinates files for the 74 teleost genomes in the comparative atlas have been deposited to Zenodo (https://doi. org/10. 5281/zenodo. 5776772).
Phylogenetic gene trees Initial gene trees were built using the Ensembl Compara pipeline  (Vilella et al. 2009). Briefly, starting from the sets of proteins derived from the longest transcripts in each genome, we performed an all-against-all BLASTP+  (Altschul et al. 1990) , with the following parameters "-seg no -max_hsps 1 -use_sw_tback -evalue 1e-5". We then performed clustering with hcluster_sg to define gene families, using parameters "-m 750 -w 0 -s 0. 34 -O". We built multiple alignments using M-Coffee  (Wallace et al. 2006) , with the command "t-coffee -type = PROTEIN -method mafftgins_msa, muscle_msa,kalign_msa,t_coffee_msa -mode = mcoffee". Next, we conducted phylogenetic tree construction and reconciliation with TreeBeST, using default parameters  (Vilella et al. 2009). Although TreeBeST remains the most efficient method to build gene trees for large data sets  (Noutahi et al.
Expression level of ohnologous genes We used RNA-seq data sets across 11 tissues (bones, brain, embryo, gills, heart, intestine, kidney, liver, muscle, ovary, and testis) in zebrafish and medaka from the PhyloFish database  (Pasquier et al. 2016) , normalized into TPM (transcripts per million transcripts) and quantile normalized across tissues as previously described  (Parey et al. 2020 ). Gene IDs were then converted from Ensembl version 89 to version 95 using conversion tables downloaded from BioMart  (Smedley et al. 2009). Average expression across tissue (Fig. 3C ; Supplemental Fig. S8 ) and by-tissue expression (Supplemental Figs. S9, S10) were calculated for ohnologs grouped by their ancestral chromosome of origin.
ZFIN gene names Zebrafish ZFIN gene names were extracted using BioMart  (Smedley et al. 2009 ) from the Ensembl database (version 95). We extracted the last letter of gene names, which represents "a" and "b" ohnology annotations in ZFIN. We then computed the minimal number of "a" and "b" ZFIN gene name reassignments that would be necessary to be consistent with the comparative atlas. In the comparative atlas, "a" and "b" labels are arbitrarily assigned to duplicated chromosomes; that is, genes descended from Chromosomes 1a and 1b could be swapped to 1b and 1a. To not artificially overestimate discordances, we first swapped such arbitrary "a" and "b" annotations to minimize differences with ZFIN. Finally, we counted the remaining number of "a" and "b" disagreements for zebrafish genes in the comparative atlas that were also annotated in ZFIN.
Acknowledgments We thank  Pierre Vincens  for the coordination of computing resources and all members of the  GenoFish consortium  for fruitful discussions. We also thank  Dan Macqueen  and two anonymous reviewers for their critical reading of this manuscript and their helpful suggestions to improve its clarity. This work is funded by  Agence Nationale de la Recherche (ANR) GenoFish  (grant number  ANR-16-CE12-0035-02 ), and was supported by grants from the  French Government  and implemented by  ANR  ( ANR-10-LABX-54   MEMOLIFE  and  ANR-10-IDEX-0001-02 PSL * Research University ). This project received funds from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 817923  ( AQUA-FAANG ).
Software availability All code for the FishComparativeAtlas pipeline is publicly available at GitHub (https://github. com/DyogenIBENS/FishComparative Atlas). An archive containing a stable version of the code along with all input data (including gene trees) and the final atlas has been deposited in Zenodo (https://doi. org/10. 5281/zenodo. 5776772), to reproduce the generation of the comparative atlas or directly inspect it. The SCORPiOs LORelEi extension is available at GitHub (https://github. com/DyogenIBENS/SCORPIOS) and has also been deposited in Zenodo (https://doi. org/10. 5281/zenodo. 6913688), along with all input data, environments, and outputs. In addition, both the FishComparativeAtlas pipeline and SCORPi-Os LORelEi extension are available in the Supplemental Material.
Data access Gene homology relationships and local synteny conservation between teleosts can be interactively browsed through the Genomicus web server  (Nguyen et al. 2022 ), accessible at https:// www. genomicus. bio. ens. psl. eu/genomicus-fish-03. 01/cgi-bin/sear ch. pl. The homology relationships from the comparative atlas can be downloaded as flat or HTML files via an ftp server (ftp://ftp. biolo gie. ens. fr/pub/dyogen/genomicus-fish/03. 01/ParalogyMap), along with the gene trees in New Hampshire eXtended (NHX) format (ftp. biologie. ens. fr/pub/dyogen/genomicus-fish/03. 01/protein_trees. nhx).
Earth's environments harbor complex consortia of microbes that affect processes ranging from host health to biogeochemical cycles. Understanding their evolution and function is limited by an inability to isolate genomes in a high-throughput manner. Here, we present a workflow for bacterial whole-genome sequencing using opensource labware and the OpenTrons robotics platform, reducing costs to approximately $10 per genome. We assess genomic diversity within 45 gut bacterial species from wild-living chimpanzees and bonobos. We quantify intraspecific genomic diversity and reveal divergence of homologous plasmids between hosts. This enables population genetic analyses of bacterial strains not currently possible with metagenomic data alone.
Only 31 of these putative plasmids matched existing plasmid sequences in the PLSDB plasmid database  [22] , indicating that more than 94% of the recovered plasmid sequences were novel. Consistent with phylogenetic and geographic barriers to exchange of plasmids, we found that plasmids were most often shared between genomes of related taxa found within the same bacterial and Pan host species (Fig. 3A ; Additional File 1: Fig. S6 ). However, in many cases, plasmids containing homologous stretches of DNA were found in genomes from multiple distantly related bacterial taxa (i. e. , different families, Additional File 1: Fig. S6 ). Similarly, we observed several cases where similar plasmid sequences were detected in multiple Pan host individuals (Fig. 3B-D ). Across all plasmids, we observed significantly greater nucleotide similarity (i.
Thus, isolate sequencing enables chromosome-and plasmid-resolved genomic analyses of bacterial species that remain difficult with metagenomic data alone. Our workflow has several advantages and disadvantages relative to existing approaches for high-throughput bacterial isolation and whole-genome sequencing. One major advantage is its simplicity, as it relies on standard microbiological and molecular biology approaches and is fully automated on the OpenTrons platform. For example, relative to microfluidics-based isolation  [25, 26]  or single-cell genome sequencing approaches  [27, 28] , our method is readily applicable by labs without the need for capital-intensive specialized equipment. The equipment costs necessary to execute our full protocol are also dramatically lower than for a number of previously-developed high-throughput genome sequencing workflows that achieve low marginal costs using expensive robotics  [16, 17, 29].