dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational performance Using the pre-constructed BF of oral k-mers, aKmerBroom has a runtime of around 1 h for a dataset with fewer than 10 million reads, while using approximately 10 Gb of memory. Beyond this input size, the run time and memory requirement scales linearly with the number of unique anchor k-mers in the input reads. Note that if a new BF has to be constructed from scratch, this one-time step would take around 6 h for a file of 1 billion k-mers. Leaving out the time to build the BF or index the control/reference database and evaluating running time on Real dataset 2, a FASTA file of almost 48 million reads, DeconSeq took around 1 day to run, aKmerBroom took around 4 h, and Recentrifuge 2 h. Both DeconSeq and Recentrifuge were run using 2 Gb of memory.
DISCUSSION Decontaminating ancient oral metagenomes is a challenging computational problem, currently poorly performed using off-the-shelf tools. This work highlights that current ancient metagenomic studies are hindered by suboptimal decontamination methods. We propose aKmerBroom, a tool for contamination removal of ancient oral datasets using a BF constructed on a set of trusted oral k-mers, using a large collection of metagenomes. We evaluated aKmerBroom with three distinct synthetic metagenomic datasets subsampling from sample sources that were fully, partially and non-included in the construction of the BF, and obtained 97:85%, 93:39%, and 89:53% sensitivity and 98:00%, 97:96%, and 94:00% specificity (respectively). We further measured aKmerBroom performance on two real samples and quantified the percentage of ancient oral sample preserved.
aKmerBroom effectively preserves most of the original sample, and removes contaminant reads as estimated by SourceTracker, whereas other methods (Recentrifuge, DeconSeq) discard over 53% of the sequences and remove true ancient oral content, also as estimated by SourceTracker. k-mer-based methods such as aKmerBroom are relevant to modern day DNA analyses because they are reference-free (e. g. , they do not require a database of reference genomes) and they make use of the large corpus of genomic information that has been gathered over the years. Since we use a k-mer-based consensus of samples to decide what to keep, but we do not decide which species specifically are present/ absent in the input sample, our method does not suffer from biases coming from using OTU tables or reference databases.
Others have reported using k-mers to assess contamination in human whole-genome samples by doing meta analyses across different datasets. 37  Thus a trend emerges on using stored genetic information to tackle the problem of contamination assessment and removal, instead of making it a matter that is unique to each study. As it simplified the implementation, the second round of lookups was performed using an exact membership data structure (set). Yet as a future step, performance improvements can be made to reduce the memory requirement for large input files with a significant proportion of ancient reads. For example, this second round could also be implemented using a BF. This way the memory required can then be independent of the size of the input reads dataset. The fact that ancient metagenomic samples are rare and have low biomass of ancient remains often translates into underpowered aDNA studies.
As more and more ancient metagenomic projects are published, tools such as aKmerBroom emerge as a novel and efficient way of incorporating data from previous studies by concisely storing information in the form of a BF. Unlike other methods, aKmerBroom represents the variety of ancient oral metagenomic material across several BioProjects, while not making specific assumptions about the microbial species that should be expected or ignored. It mitigates the effect of small sample size (as the output of several metagenomics studies are put together to construct the BF) while still making computationally manageable analyses. aKmerBroom brings usability improvements to decontamination methods. Prior to it, users had to make decisions on how to properly carry out analyses. For instance, in the case of Recentrifuge, one needs to estimate whether to run the taxonomic classifier Centrifuge with default or modified parameters, selecting for a pre-made index or building an index with the criteria of the user, which is equivalent to curating a database that ideally would be tailored for ancient oral decontamination.
Creating a set of trusted oral k-mers To construct a set of trusted oral k-mers for aKmerBroom, we use a resource from decOM, 36 a method for contamination assessment of ancient oral metagenomic samples. decOM constructs a k-mer matrix from 360 metagenomic samples covering a wide range of environments around the world, labelled as ancient oral (aOral), modern oral (mOral), and their possible contaminants (Sediment/Soil and Skin samples). Sample accession numbers are provided in the supplementary material of the decOM publication. 36  The decOM matrix is built over distinct k-mers of size 31, filtered by retaining k-mers that were present in at least 3 samples in the collection and by removing all k-mers seen only once in a sample, which were likely to be sequencing errors. In order to reduce memory usage, we start by subsampling 10% of the k-mers present in the decOM matrix, we select a set of a high quality oral k-mers by filtering each k-mer that satisfies all of the following conditions: Present in any of the aOral samples or, Present in any of the mOral samples and, Absent in all Skin samples and, Absent in all Soil samples.
In a boolean formula the conditions could be read as (inAOral or inMOral) and not(inSkin or inSoilSediment). We obtain over 1. 5 billion k-mers, which corresponds to roughly 25% of the subsampled set of k-mers matrix (2:5% of whole set of k-mers of the decOM matrix of sources). These k-mers are referred to as trusted oral k-mers. We would like to emphasise that our method is reference-free as it does not require a database or index of reference genomes, however, in the construction of the Bloom Filter, there must be a reference of k-mers considered to be of ancient oral origin. We have defined this set after the conditions previously explained, but the user might come up with their own input set of k-mers too.
Constructing a bloom filter from oral k-mers A Bloom Filter is a space-efficient probabilistic data structure that enables to query the membership of an element within a set, with false positives but no false negatives. 45  As a preprocessing step, aKmerBroom constructs a Bloom Filter (BF) from a set of k-mers (using pybloomfiltermmap  46  ). In aKmerBroom, the user may provide their own set of k-mers, or alternatively use the pre-constructed table of trusted oral k-mers provided with the software and constructed as described in the previous section (see Zenodo file  47  ). In the upcoming section, we discuss how we mitigate the issue of false positives.
Pass 2: Identifying ancient reads All anchor reads from the first pass are k-merized and stored into a new anchor k-mer set. The full input dataset is scanned again, and reads having a proportion R 50% of k-mers present in this new anchor k-mer set are retained as likely to be of ancient origin. Note that non-anchor reads may be retained, as some will satisfy this criteria. The final output of aKmerBroom consists of the set of retained reads.
To determine an appropriate value for the anchor k-mer proportion threshold t, we performed a standard grid search from 10% to 90% over Synthetic dataset 1 (see results). As shown in Figure  4 , we chose a threshold of 50% because it gives us a suitable trade-off between having a high true positive rate (greater than 85%) while also having a low false positive rate (less than 5%). However, the user can also set t according to their desired sensitivity/specificity trade-off. Originally we had tried out only one pass over the initial Bloom Filter built from the set of high quality oral k-mers, and we further improved the results by implementing a second pass based on matches against anchor reads identified from the first pass. Results for this one-step version of aKmerBroom are also shown in Figure  4  (''1 pass'').
ACKNOWLEDGMENTS R. C. was supported by  ANR Full-RNA, SeqDigger, Inception  and  PRAIRIE grants  ( ANR-22-CE45-0007 ,  ANR-19-CE45-0008 ,  PIA/ANR16-CONV-0005 ,  ANR-19-P3IA-0001 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grants agreements No. 872539  and  956229.
METHOD DETAILS We have developed aKmerBroom, the first method able to perform read-level decontamination on ancient oral metagenomes. As an input to aKmerBroom, the user provides a set of reads to be decontaminated. aKmerBroom then scans the input reads against a set of oral k-mers, using two passes to iteratively retain reads likely to be of ancient origin. The main steps are described below, and a high-level summary is provided here. First a set of high quality oral k-mers is determined from a database of ancient and modern oral samples as well as environmental samples. Then, a Bloom Filter is constructed to represent this set approximately in memory. The tool then scans input reads and retains those that have at least 2 consecutive k-mer matches against the filter. Those reads enable us to enrich the set of ancient k-mers by incorporating new putative ancient k-mers.
The nature of the TESS survey leads to multiple observations of the same sectors but about two years apart, and consequently the second transit of the same candidate may be detected. The range of possible orbital periods for a duo-transit candidate then becomes reduced to a discrete set of periods. Osborn (2022)  have presented a new code to estimate the probability of each period alias. Their classification allows one to prioritize the follow-up of a duo-transit candidate by scheduling observations of the highest-probable periods first. Despite a strong detection bias against planets at long orbital periods, the TESS mission and the efficient follow-up effort has led to an increase of well-characterized transiting long-period giants (e. g. ,  Gill et al. 2020b; Hobson et al. 2021; Dalba et al.
TESS photometry The TESS satellite  (Ricker et al. 2015)  observed TOI-5678 during its primary and its first extended mission (year 1 and year 3). Until September 2023 (TESS cycle 5), no further TESS observations are planned. TOI-5678 was observed at a two-minute cadence in three TESS sectors: sector 4  (2018-10-18 to 2018-11-15) , sector 30 (from 2020-09-22 to 2020-10-21), and sector 31  (2020-10-21 to 2020-11-19). We discovered a planet candidate in the TESS data using our specialized duo-transit pipeline  (Tuson & the CHEOPS Consortium 2022). This pipeline was created to search for TESS duo-transits suitable for CHEOPS follow-up. The pipeline concatenates the TESS Pre-search Data Conditioned Simple Aperture Photometry (PDCSAP) light curves from the primary and extended mission, detrends the light curve using a mean sliding window, and then runs a box least squares (BLS;  Kovács et al.
2002)  transit search on the detrended light curve, with parameters optimized for duo-transit detection. TOI-5678 returned a duo-transit candidate with the first transit at the very beginning of the second orbit of sector 4 and a second transit in sector 30. We checked both transits for asteroid crossing and centroid shifts (indicating a background eclipsing binary) before deciding to pursue follow-up observations. This duo-transit candidate was also independently discovered by the Planet Hunter TESS team and was announced as a Community Tess Object of Interest in March 2021. The light curves from the primary and extended mission were obtained through the Mikulski Archive for Space Telescopes 1 and the data reduction was done at the Science Processing Operation Center  (Jenkins et al. 2016).
6). For faint stars, CHEOPS also complies with its requirement, as it provided a photometric precision of 75 ppm in three hours of integration time for a star of Vmag 11. 9  (Benz et al. 2021 ). TOI-5678 was observed with CHEOPS from 2021-09-24 to 2021-11-03 through the Duos program (CH_PR110048: "Duos, Recovering long period duo-transiting planets"), which is part of the CHEOPS Guaranteed Time Observation program. This program aims to solve the orbital periods of TESS duo-transit candidates around stars with a V magnitude greater than 12. A detailed explanation of the Duos program and its scheduling strategy is presented in Sect. 3. 2 of  Osborn et al. (2022). The filler program targets candidates with a transit depth between two and four parts per thousand (ppt) such that the observation of a partial transit is sufficient to obtain a detection of the signal.
Each CHEOPS visit lasts at least three CHEOPS orbits in order to correctly model instrumental trends and to identify the partial transit. Five CHEOPS visits of TOI-5678 were observed with an exposure time of 60 s. The details of the observations are presented in Table  1. The fifth visit of TOI-5678 showed an ingress, and this transit event uniquely validated the orbital period of the candidate. The CHEOPS light curve is shown in Fig. 1 , and no strong systematic trends are visible in the raw data. After this visit, we stopped the monitoring of the target. TOI-5678 has a V magnitude of 11. 4 and is one of the faintest targets to be observed with CHEOPS. We used point spread function (PSF) photometry implemented in the PSF imagette photometric extraction package (PIPE 2 ) to reduce the CHEOPS data.
The iron abundances were computed using a grid of Kurucz model atmospheres  (Kurucz 1993 ) and the radiative transfer code MOOG  (Sneden 1973). The converged spectroscopic parameters are listed in Table  3. The stellar abundances [Mg/H] = 0. 07 ± 0. 06 dex and [Si/H] = 0. 00 ± 0. 04 dex were derived using the classical curve-of-growth analysis method, assuming local thermodynamic equilibrium (e. g. ,  Adibekyan et al. 2012 Adibekyan et al. , 2015)). The same codes and models were used for the abundance determinations. To determine the stellar radius of TOI-5678, we used a Markov chain Monte Carlo infrared flux method (MCMC IRFM;  Blackwell & Shallis 1977; Schanche et al. 2020 ) that computes the stellar angular diameter and effective temperature via known relationships of these properties and the apparent bolometric flux.
The first pair of mass and age values was computed through the isochrone placement algorithm  (Bonfanti et al. 2015 (Bonfanti et al. , 2016)) , which interpolates the input parameters within pre-computed grids of PARSEC 5  v1. 2S  (Marigo et al. 2017 ) isochrones and tracks. The second pair of mass and age values was retrieved by the Code Liégeois d'Évolution Stellaire (CLES;,  Scuflaire et al. 2008 ) code, which computes the best-fit stellar evolutionary tracks following the Levenberg-Marquadt minimization scheme as presented in  Salmon et al. (2021). After carefully checking for the mutual consistency of the two respective pairs of outcomes through the χ 2 -based criterion broadly discussed in  Bonfanti et al. (2021) , we merged the output distributions of both the stellar mass and age, ending up with the following estimates: M ⋆ = 0.
905 +0. 039 -0. 041 M ⊙ and t ⋆ = 8. 5 ± 3. 0 Gyr. The results are presented in Table  3. Following procedures going back to the CoRoT mission  (Fridlund 2008) , we applied the Interactive Data Language package Spectroscopy Made Easy  (Piskunov et al. 1995; Valenti & Piskunov 1996; Piskunov & Valenti 2017). With this code, and using the stellar parameters given above as fixed inputs, we then synthesized a spectrum based on the well-determined stellar atmospheric grid MARCS 2012  (Gustafsson et al. 2008 ) using atomic and molecular parameters from the Vienna Atomic Line Database  (Piskunov et al. 1995)  for the synthesis. Again following schemes outlined in  Fridlund et al. (2020) , and references therein, and while keeping the turbulent velocities V t macro and V t micro fixed at the empirical values found in the literature  (Gray 2008; Bruntt et al.
TESS photometric analysis We selected TOI-5678 as a potential target for the CHEOPS Duos program, as its TESS light curve presents two single transits separated by a data gap of about 716 days. As for all targets in this CHEOPS program, we ran a photometric analysis with the software package MonoTools (Osborn 2022) 6. MonoTools is especially adapted to detecting, vetting, and, modeling multiplanet systems in the case of single-and duo-transiting planets. The first step was to verify that the transit is not due to a rise in the background flux, which is indicative of an asteroid crossing or an artifact. We also tested that the transit model is a better fit to the event compared to a sine wave or a polynomial function. We checked that there was no feature in the X and Y coordinates coincident with the transit event, which would indicate a possible eclipsing binary or blend scenario.
Calculator 8  , we computed that the expected radial velocity precision achievable with a 1800-s HARPS exposure is 1. 4 ms -1 for TOI-5678. A detection of the radial velocity signal was feasible during the next few months of observability (November to February). The target was then selected to be added to an ongoing HARPS program with the planned goal of characterizing long-period transiting exoplanets. Figure  3  presents the radial velocity dataset of TOI-5678 and its corresponding generalized Lomb-Scargle (GLS) periodogram  (Zechmeister & Kürster 2009) , which combines both CORALIE and HARPS datasets. No significant peak was found in the GLS periodogram of the following stellar activity indicators: FWHM and bisector of the CCF or the H α index. The GLS periodogram of the CCF contrast shows some peaks at periods unrelated to the period of the planetary candidate.
We set the maximum value of the orbital period prior to the time span of the dataset, and the upper bound of the semi-amplitude prior was set to twice the peak-to-peak of the radial velocities. Table  A. 1 lists all priors used in this analysis. The analysis of the CORALIE and HARPS data of TOI-5678 with kima led to a detection of one planetary signal with an orbital period of 48. 63 +2. 61  -2. 29 days and a semi-amplitude of 3. 94 +0. 79  -0. 82 ms -1. The Bayes factor between the no planet and one planet model is equal to 52 and 0. 6 between the one planet and two planet model. While the Bayes factor for the one planet model is not superior to 150, a common threshold for significance, it indicates moderate evidence for this model  (Feroz et al. 2011). The presence of transits with a compatible period adds credibility to the signal.
Joint analysis We combined the photometric and radial velocity data to perform an analysis of the planetary system around TOI-5678. We used the software package juliet  (Espinoza et al. 2019)  to jointly model the datasets, as juliet models multiplanetary systems, including transiting and non-transiting planets, within a Bayesian framework. The light curve modeling in juliet is based on batman  (Kreidberg 2015)  for the model of the planetary transits. The stellar activity and instrumental correlations can be specified with Gaussian processes or parametric functions, including linear models (LMs). Photometric datasets from different instruments can be included in the model by adding independent jitter and offset terms as well as dilution factors. To model the Keplerian orbits in the radial velocity data, we used RadVel  (Fulton et al.
2018). Several sampling methods can be implemented in juliet, and we chose the nested sampling method dynesty (Speagle 2019). Data from multiple instruments can be incorporated using separate values for radial velocity jitter by allowing for offsets between the datasets. Two planetary transits are visible in the TESS data in sectors 4 and 30. We chose to include these two relevant sectors and to exclude sector 31, which does not contain any transit feature. Since only the fifth CHEOPS visit displays a partial transit of TOI-5678 b, we chose to only include this last visit for the joint modeling. The model parameters for the planet are the orbital period, the planet-to-star radius ratio, the mid-transit time of the first transit, the impact parameter, the argument of periastron, and the eccentricity.
3. 1. Because the TESS and CHEOPS bandpasses are different, we modeled the limb darkening with a quadratic law using two different sets of q1 and q2 parameters  (Kipping 2013a ). The priors used in the limb-darkening parameters were derived from the LDCU 9 code, which is a modified version of the python routine implemented by  Espinoza & Jordán (2015). It computes the limb-darkening coefficients and their corresponding uncertainties using a set of stellar intensity profiles that account for the uncertainties in the stellar parameters. The stellar intensity profiles were generated based on two libraries of synthetic stellar spectra: ATLAS  (Kurucz 1979)  and PHOENIX  (Husser et al. 2013). Table  B. 1 details the priors used for all the model parameters. The TESS photometric variability was taken into account in one Gaussian process for each sector using an approximate Matern kernel.
The CHEOPS light curves are known to have trends correlated with the roll angle of the spacecraft. Therefore, we used LMs to decorrelate the CHEOPS data. The following 9 https://github. com/delinea/LDCU HARPS offset [ms -1 ] 59. 7 +0. 5 -0. 5 CORALIE jitter [ms -1 ] 3. 0 +4 -2 HARPS jitter [ms -1 ] 1. 3 +0. 4 -0. 2 parameters were scaled and then used for linear decorrelation: background flux, cosine of the roll angle, and the X and Y coordinates. A preliminary analysis of the CHEOPS data allowed us to select the relevant detrending parameters by comparing evidence values of the fits. The functional form of the detrending for the fifth CHEOPS visit is presented in Sect. 2. 2 and in Table  4. The priors on the associated coefficients are all uniform between -1 and one because the parameters were normalized and scaled.
The diversity of intermediate-mass exoplanets seems to be easily explained when studying the formation of Uranus and Neptune  (Helled & Bodenheimer 2014; Valletta & Helled 2022). The authors demonstrate that slight changes in the protoplanetary disk and the environment of the planetary embryos (e. g. , core accretion rate, solid surface density) lead to planets with greater differences in mass and composition. We present TOI-5678 b within the population of transiting exoplanets in Fig. 7. We downloaded the NASA exoplanet database 10 on February 28, 2023, and selected confirmed exoplanets with radius uncertainties lower than 8% and mass uncertainties lower than 20%. With a planetary radius of 4. 91 ± 0. 08 R ⊕ and a stellar insolation of 11. 5 ± 0. 7 S ⊕ , TOI-5678 b is larger than the planets in the group of volatile-rich sub-Neptunes.
Acknowledgements. This work has been carried out within the framework of the  National Centre of Competence  in Research PlanetS supported by the  Swiss National Science Foundation  under grants  51NF40_182901  and  51NF40_205606. The authors acknowledge the financial support of the  SNSF. A. T. acknowledges support from an  STFC  PhD studentship. M. L. acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2194576. P. M. acknowledges support from  STFC  research grant number  ST/M001040/1. J. E. , Y. A. , and M. J. H. acknowledge the support of the  Swiss National Fund  under grant  200020_172746. A. Br. was supported by the SNSA. M. F. and C. M. P. gratefully acknowledge the support of the  Swedish National Space Agency  ( DNR 65/19 ,  174/18 ,  177/19 ).