dataesr/scientific-paragraphs-categorization huggingface dataset:
from East and Central Africa (HM045823 from Angola 1962; HM045784 from Central African Republic 1984; HM045812 from Uganda 1982; KY038947 from Central African Republic 1983; HM045793 from Central African Republic 1986; HM045822 from Central African Republic 1978; and KY038946 from Central African Republic 1975). Dataset 1 also included 29 sequences from Brazil, including the new 18 genomes reported here from the ECSA lineage and 3 genomes from the outbreak caused by the ECSA lineage in June 2016 in Maceio, Alagoas states, northeast Brazil (Fig 1A) CT = cycle threshold, d = days from onset of symptoms to sample collection. Corresponding sequencing statistics are available in S1 Table. Isolates were collected around 2. 3 (range: 0-5) days after onset of symptoms. Acc. Number = GenBank accession number.
Acknowledgments We are thankful to all personnel from  Secretaria de Vigila  ˆncia em Saude SVS/MS,  PAHO/Brazil ,  Roraima  and Boa Vista Health Surveillance System that coordinated surveillance and helped with data collection and assembly. We thank  Oxford Nanopore Technologies  for the support to the  ZIBRA-2 (Zika in Brazil Real time Analyses-second round ) project with.
Methodology/Principal findings Nı ´vel Superior  (grant  88887. 130716/2016-00 ). FGN is funded by  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico  (http:// www. cnpq. br,grant  440856/2016-7 ) and  Coordenaca ˜o de Aperfeicoamento de Pessoal de Nı ´vel Superior  (http://www. capes. gov. br,grants  88881. 130825/2016-00and 88887. 130823/2016-00 ). L. C. J. A. is supported by the  EU 's  Horizon 2020 Programme through ZIKAlliance  (grant  PRES-005-FEX-17-4-2-33 ). N. R. F. is funded by a  Royal Society  and  Wellcome Trust   Sir Henry Dale Fellowship  ( 204311/Z/16/Z ), internal  GCRF  grant  005073 , and  John Fell Research Fund  grant  005166. This research received funding from the  ERC  (grant agreement  614725-PATHPHYLODYN ). MS was partially supported by the  National Institute of General Medical Sciences  of the  NIH (USA)  under award number  R01GM130668.
3 (range: 0-5) days after onset of symptoms. Acc. Number = GenBank accession number. Isolate State, Municipality Acc. Ct RT-qPCR Coverage Age, Sex Collection d Number (%) date AMA290 AM, Manaus MK121891 NA 90. 2 76, F 15/07/2015 5 AMA291 AM, Manaus MK121892 NA 80. 7 48, F 15/07/2015 4 AMA292 AM, Manaus MK121893 NA 90. 2 50, M 15/07/2015 0 AMA293 AM, Manaus MK121894 NA 84. 4 42, M 31/01/2016 4 AMA294 RR, Boa Vista MK134712 NA 90. 2 45, F 01/12/2014 2 AMA295 RR, Unknown MK134713 NA 90. 2 9, F 11/11/2014 1 AMA74 AM, Manaus MK121895 15 90. 2 32, F 20/03/2017 2 AMA346 RR, Boa Vista MK121896 13. 7 90. 2 30, F 03/03/2017 1 AMA350 RR, Bonfim MK121897 27. 15 54. 7 32, F 20/02/2017 1 AMA352 RR, Boa Vista MK121898 17. 33 88. 6 3, F 22/02/2017 1 AMA354 RR, Boa Vista MK121899 23. 36 86. 9 19, F 17/03/2017 1 AMA362 RR, Iracema MK121900 18.
63 88. 6 31, F 17/03/2017 1 AMA364 RR, Boa Vista MK121901 25. 93 83. 3 19, F 17/03/2017 2 AMA366 RR, Boa Vista MK121902 19. 87 90. 0 36, F 17/03/2017 2 AMA368 RR, Boa Vista MK121903 25. 91 93. 1 26, F 15/03/2017 2 AMA369 RR, Boa Vista MK121904 21. 55 95. 6 52, M 02/03/2017 3 AMA374 RR, Boa Vista MK121905 27. 41 71. 4 64, F 02/03/2017 4 AMA379 RR, Boa Vista MK121906 17. 5 96. 1 38, F 27/02/2017 4 AMA381 RR, Boa Vista MK121907 16. 66 97. 7 31, F 27/02/2017 4 AMA382 RR, Boa Vista MK121908 14. 58 76. 6 30, F 05/03/2017 1 [35]s://doi. org/10. 1371/journal. pntd. 0007065. t001[35]. Dataset 2 (ECSA-Br) included only the 29 Brazilian genome sequences. Using a robust nonparametric test.
Estimating the number of substitution events per site that have occurred during the evolution of a pair of amino acid sequences is a common task in phylogenetics and comparative genomics that often requires quite slow maximum-likelihood procedures when taking into account explicit evolutionary models. Data presented in this article are large sets of numbers of substitution events and associated numbers of observed differences between pairs of aligned amino acid sequences that have been generated through a simulation procedure of sequence evolution under a broad range of evolutionary models. These data are available at https://zenodo. org/record/2653704 (doi: 10. 5281/zenodo. 2653704). They are accompanied in this paper by figures showing the strong relationship between the corresponding evolutionary and uncorrected distances, as well as estimated numerical constants that determine non-linear functions that fit the simulated data.
9]. For each of those selected multiple sequence alignments, the branch lengths of the associated phylogenetic tree were refitted using RAxML-NG v0. 8. 1 BETA  [41]  with the corresponding evolutionary model, and the matrix of patristic distances d was computed using gotree v0. 2. 10 (https://github. com/evolbioinfo/gotree). Of note, for each of the 27 considered evolutionary models, INDELible and RAxML-NG were both used with the corresponding empirical replacement matrix file gathered from http://giphy. pasteur. fr/empiricalmodels-of-amino-acid-substitution. Finally, as each pair of distance matrices (i. e. uncorrected and evolutionary distances p and d) represents numbers of observed differences and occurred substitutions per site, respectively, each entry was multiplied by the total number of sites (i.
For each of the 27 considered evolutionary models, empirical values of p r from the corresponding amino acid replacement matrix were used for computing b (Table  2 ). For each evolutionary model and each of the two PC and EI gamma distances, the numerical constant a was estimated by weighted nonlinear regression from the pairs of integer versions of uncorrected and evolutionary distances p and d gathered from the corresponding simulation file (see above) and divided by the number of simulated sites (i. e. 50,000). Each least-square estimation of the parameter a was performed using R v3. 5. 3  [45]  with the function nls. Default Gauss-Newton algorithm was used with relative weighting (i. e. each d was weighted with d À2 ) and starting value a ¼ 2. All simulation datasets are available at https://zenodo.
org/record/2653704 (doi:10. 5281/zenodo. 2653704). The 20,000 phylogenetic trees selected for simulating sequence evolution are available as a text file together with descriptive statistics summarizing the corresponding patristic distances. For each of the 27 evolutionary models (see Tables  1  and 2 ), blocks of simulation data (i. e. PhylomeDB identifiers, random seeds, trees with refitted branch lengths, integer values of p and d) are available as text files. Estimated values of a are given for the PC and EI gamma distances in Tables  1  and 2 , respectively, together with the associated 95% confidence intervals and mean squared errors. Figs. 1e6 represent the 27 scatter plots of simulated d against p, as well as the regression curves for the two PC and EI gamma distance functions. Each scatter plot is also available with and without the regression curves at https://zenodo.
), as well as the associated text files containing simulation datasets (https://zenodo. org/record/2653704) and figures showing the relationship between p and d (Figs. 1e6, and image files available at https://zenodo. org/record/ 2653704). Specifications table Subject area Computational biology, Bioinformatics More specific subject Phylogenetics area Type of data Text files, Images, Tables How data was Computer simulation, nonlinear regression acquired Data format Simulated, Analyzed Experimental factors Simulated data from publicly available phylogenetic trees Experimental Amino acid sequence evolution simulation, evolutionary and uncorrected distance estimations, and features nonlinear regression Data source location Institut Pasteur, Paris, France Data accessibility Simulation data and scatter plot figures are available at https://zenodo.
2 to 0. 5. Figure  2  shows examples of spectra acquired at an altitude close to the optimal one. No methane absorptions are apparent, while we accurately measure the faint H2O lines within the range, which, at very low water content, have an absorption depth comparable to a 1 ppbv CH4 absorption (panel c). Corresponding profiles of H2O (see Methods), are characterized by an unprecedented accuracy compared to previous profiling  20, 21. [ Figure 2]    Based on the noise level, CH4 absorption integrated over the line of sight can be tentatively fitted along with absorption of CO2 while taking into account the instrument spectral resolution (see Methods). This way, an estimation of methane detection limits, converted into volume mixing ratios, for the full data set acquired by ACS and NOMAD was made.
Interestingly, mesoscale model simulations have shown that the depth of the boundary layer in Gale crater is significantly lowered  24  due to the crater size and depth. Even if this would tend to maintain methane locally, the same simulation  24, 25  shows that the slope winds on the side of the crater and the induced updraft above the rims is so intense that methane should be efficiently injected into the atmosphere at 10 km altitude. In no case can we consider Gale to be an isolated crater (see Methods for additional information). To reconcile the lack of CH4 detection in the TGO data and the positive CH4 detection at the surface by Curiosity, one must invoke a mechanism able to fully eradicate methane in the lower atmosphere at a rate ~1000 times faster than that predicted by the conventional chemistry.
NOMAD also provides mapping of several constituents (aerosols/dust/clouds, and O3, H2O, HDO, CO, and other trace gases) in nadir mode with an instantaneous footprint of 0. 5 x 17 km 2 (LNO spectrometer) and 5 km 2 (UVIS spectrometer) respectively, with a repetition rate of 30 Martian days. For this work, we analysed SO channel data measured between 21 April and 1 August. SO measures 4 spectral bins in each of 5 or 6 diffraction orders per second in solar occultation mode, among which a series of specific diffraction orders were chosen (order 133 to 136 spanning 2990 to 3080 cm -1 spectral range) where methane features are located. To increase the sensitivity of the NOMAD SO measurements, we accumulate all the spectral measurements in each occultation from the 4 detector bins into 3 km vertical bins.
The synthetic spectra were computed with spectral line parameters from HITRAN 2016  33  corrected to account for the CO2 atmosphere, as described in  21. Temperature and pressure profiles were extracted from the Mars Climate Database  34. Aerosol extinction is assumed to have a grey behaviour within a diffraction order. The retrieved profiles of H2O and the attempts to retrieve CH4 for two selected occultations are shown in Extended Data Figure  2. The H2O profiles, even though obtained from faint lines corresponding to very dry conditions, are nevertheless characterized by a greater accuracy compared to previous H2O profilings  21. However, this formal retrieval shows no trace of methane. Sometimes very small methane abundances of ~20 pptv in the R-branch (3058 cm - 1 ) were retrieved above the 1-σ level.
However, this result was not confirmed, in any case, by a corresponding result in the Q-branch (3018 cm -1 ). We conclude that these constitute false positive detections, perhaps due to a residual fixed pattern noise in the spectrum. The effect of the atmospheric aerosol loading on the retrieval accuracy is illustrated in the right panel where the "profile" of transmittance noise is shown along with the optical slant density, as measured along the line of sight. As no methane was detected within the analysed dataset, we selected a faster approach to estimate robust upper limits of CH4vmr for the considered dataset covering the April to August period of 2018. The sensitivity in solar occultation is produced by the number of species molecules observed along the LOS. We assume that no methane can be retrieved within the 1-σ error bars.
altitude in Extended Data Figure  4. A similar simplified retrieval method was used to estimate the detection limit from NOMAD spectra. The forward model computes the optical column density for each spectrum separately, assuming a constant mixing ratio along the line-of-sight and using the most recent HITRAN 33 CH4 line list with CO2 pressure broadening coefficients. The optical depth is then convoluted to the instrument's PSF with FWHM of 0. 15 cm -1 and then multiplied by the grating blaze and AOTF functions. The transmittance spectra are fit by minimizing the chi-squared using the Levenberg-Marquardt algorithm (via the Python Scipy wrapper of MINPACKS lmder  35  ) to determine an optimal polynomial background and CH4 mixing ratio. The standard error of the mixing ratio is derived from the covariance matrix of the optimal fit parameters.
A compilation of all the retrieved upper limits from the ACS-MIR dataset covering the period from April 21 st to September 4 th 2018 using (Top) the Q-branch, and (bottom) the R-branch of CH4 absorption. The colour scale denotes Ls. Symbols of alike colour denote individual profiles. ord. #182) upp. lim. x 10 10 CH 4 (ord. #180). CO 2 Lat: 69. o L s : 192. 7 o 0 50 100 150 200 250 CH 4 upper limit in vmr (pptv).
Acknowledgements ExoMars is the space mission of  ESA and Roscosmos. The ACS experiment is led by  IKI, Space Research Institute in Moscow , assisted by LATMOS in France. The project acknowledges funding by  Roscosmos  and  CNES. Science operations of ACS are funded by  Roscosmos  and  ESA. IKI affiliates acknowledge funding under grant # 14. W03. 31. 0017  and contract # 0120. 0602993 (0028-2014-0004 ) of Russian Government. The NOMAD experiment is led by the  Royal Belgian Institute for Space Aeronomy (IASB-BIRA ), assisted by Co-PI teams from  Spain (IAA-CSIC), Italy (INAF-IAPS) , and the  United Kingdom (Open University ). This project acknowledges funding by the  Belgian Science Policy Office (BELSPO) , with the financial and contractual coordination by the  ESA Prodex Office  ( PEA 4000103401 ,  4000121493 ), by  Spanish MICINN  through its  Plan Nacional  and by European funds under grants  ESP2015-65064-C2-1-P  and  ESP2017-87143-R (MINECO/FEDER) , as well as by  UK Space Agency  through grants  ST/R005761/1 ,  ST/P001262/1 ,  ST/R001405/1 ,  ST/S00145X/1 ,  ST/R001367/1 ,  ST/P001572/1  and  ST/R001502/1 , and  Italian Space Agency  through grant  2018-2-HH.
A. The PyCBC search A pipeline to search for GWs from CBCs is constructed using the PyCBC software package  [7, 8]. This analysis performs direct matched filtering of the data against a bank of template waveforms to calculate the signal-to-noise ratio (SNR) for each combination of detector, template waveform, and coalescence time  [70]. Whenever the local maximum of this SNR time series is larger than a threshold of 5. 5, the pipeline produces a single-detector trigger associated with the detector, the parameters of the template, and the coalescence time. In order to suppress triggers caused by high-amplitude noise transients ("glitches"), two signal-based vetoes may be calculated  [71, 72]. Using the SNR, the results of these two vetoes, and a fitting and smoothing procedure designed to ensure that the rate of single-detector triggers is approximately constant across the search parameter space, a single-detector rank ϱ is calculated for each single-detector trigger  [73].
The updated results we report here supersede those previously published. Four new gravitational-wave events are reported here for the first time: GW170729, GW170809, GW170818, and GW170823. All four are binary black hole events. As noted in Sec. III, data from O1 are reanalyzed because of improvements in the search pipelines and the expansion of the parameter space searched. For the O2 events already published, our reanalysis is motivated by updates to the data itself. The noise subtraction procedure  [52]  that is available for parameter estimation of three of the published O2 events was not initially applied to the entire O2 dataset and, therefore, could not be used by searches. Following the procedures of Ref. [51] , this noise subtraction is applied to all of O2 and is reflected in Table  I  for the four previously published O2 GW events, as well as the four events presented here for the first time.
37 y -1 ) and 7. 92 × 10 -3 y -1 in the GstLAL search (previously, 0. 17 y -1 ). These improved FAR measurements for GW151012 are the most salient result of the reanalysis of O1 with the O2 pipelines; no new gravitational-wave events were discovered. The first binary black hole observation, GW150914, remains the highest SNR event in O1 and the second highest in the combined O1 and O2 datasets, behind only the binary neutron star inspiral GW170817. Recently, Ref. [88]  appeared. That catalog also presents search results from the PyCBC pipeline for O1 and also finds GW150914, GW151012, and GW151226 as the only confident gravitational-wave events in O1, with identical bounds on FAR to the PyCBC results in Table  I  for GW150914 and GW151226. The measured FAR for GW151012 is not identical but is consistent with the results we present in Table  I.
GW170817 Across the entirety of O1 and O2, the binary neutron star inspiral GW170817 remains the event with the highest network SNR and is accordingly assigned the most stringent possible bound on its FAR by PyCBC and the highest value of L (the logarithm of the likelihood ratio) of any event in the combined O1 and O2 dataset by GstLAL. As explained in detail in the original detection paper  [18] , a loud glitch occurs near the end of this signal in LLO. For the matched-filter searches, this glitch is excised via timedomain gating (and that gating is applied consistently to all such glitches throughout O2). Because the cWB pipeline is designed to detect short signals, it does not use that gating technique, and it rejects this event because of the glitch.
GW170729 We turn now to gravitational-wave events not previously announced. The first of these is GW170729, observed at 18∶56∶29. 3 UTC on July 29, 2017. The PyCBC pipeline assigns it a FAR of 1. 36 y -1 , the GstLAL pipeline a FAR of 0. 18 y -1 , and the cWB pipeline a FAR of 0. 02 y -1. As it is identified with the highest significance among all three search pipelines by the weakly modeled pipeline, it is worth investigating whether this event is unusual in some way, exhibiting effects (for instance, precession or higher-order modes) not adequately modeled by the templates used in the matched-filter searches. As a relatively simple way of investigating this question, a comparison study is done between the PyCBC pipeline and cWB, using software injections with parameters drawn from the SEOBNRv4 ROM parameter estimation of this event.
Scattered light Scattered light is a common source of noise in the LIGO and Virgo detectors. Stray light is reflected back into the main interferometer path, resulting in excess power in the data  [91, 229, 230]. Motion of the reflective surface, such as optic mounts, phase shift the reflected light. When this motion is smaller than the wavelength of the main laser, 1064 nm, the resultant artifacts are associated with stationary noise contributions to the interferometer spectrum. Larger motions result in archlike shapes in the timefrequency spectrograms. It is these larger motions that impact transient searches and which, therefore, concern us here. This motion is observed by auxiliary sensors and is used to help identify periods of scattering  [231]. Scattering light can be present in the data for stretches of multiple hours during periods of increased ground motion  [91].
Short-duration transient Short-duration transients last less than one second and have a high amplitude. The highest-amplitude transients can cause overflows of the digital-to-analog converters used to control the positions of the test masses. These transients occur in both LIGO detectors at a rate of approximately 1 per hour, with their cause largely unknown. The large amount of excess power due to these artifacts produces a large impulse response during the whitening process, affecting the ability of searches to optimally search the surrounding data  [8]. For this reason, these artifacts are gated by the searches before analysis, using the procedure described in Ref. [8]. Notably, the instrumental artifact present in LIGO-Livingston during GW170817  [18]  is of this class. For systems like GW170817, gating is shown to remove short-duration transients without a significant bias to astrophysical parameter estimation  [234] ; full glitch subtraction with BAYESWAVE  [53] , as used for the GW170817 parameter estimation, produces more robust results.
The parameter estimation analyses in O1 assumed uniform priors for the calibration uncertainties in frequency. For the analyses in this catalog, frequencydependent spline calibration envelopes  [98]  are incorporated into the measured GW strain to factor in potential deviations from the true GW strain due to uncertainties in the detector calibration  [64, 65]. We marginalize over the additional calibration parameters. See Sec. II B in Ref. [97]  for details. The low-frequency cutoff for likelihood integration used for parameter-estimation analyses is set to 20 Hz with the following exceptions: For GW170817 the analysis starts at 23 Hz, for GW170818 at 16 Hz, and for the GW170608 Hanford data at 30 Hz. Because of the expansion of the Universe, we measure redshifted masses from GW observations.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS) , and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
The PCR products were analyzed by agarose gel electrophoresis and sent to Genomics BioSci & Tech (New Taipei City, Taiwan) for sequencing using the same PCR primers. The sequences obtained were checked for ambiguity, assembled in MegaX  [33]  and submitted to the National Center for Biotechnology Information (NCBI) for a nucleotide BLAST search. The ITS sequences of the fungal isolates were deposited in NCBI with the accession numbers given in S1 Table.
Five PCR reactions were performed for each sample and pooled. The pooled sample was run on a 1% agarose gel, gel-purified using EasyPure PCR/Gel Extraction Kit (Bioman Scientific Co. , Ltd. ) and sent to Genomics BioSci & Tech (New Taipei City, Taiwan) for Illumina sequencing (MiSeq Reagent kit v3, 600 cycles). TruSeq DNA Nano (input 120 ng / PCR 5 cycles) was used for library preparation. Negative PCR controls (water controls) were run to detect contamination of the PCR ingredients. Most of the following bioinformatics processes were run in QIIME 1. 9. 0  [37]. The raw sequences were filtered with a phred score �Q29 (a base call accuracy of �99. 87%). The raw reads were merged into single reads and adaptors, primers and barcode sequences were removed using QIIME with the script split_library.