dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional information may be found in  Gillon et al. (2016)  for  WHT and TRAPPIST, in Ducrot et al. (2018)  for  SSO and LT, and in Gillon et al. (2017)  and  Burdanov et al. (2019)  for AAT, UKIRT and VLT. For all ground-based observations, a standard calibration (bias, dark and flat-field correction) was applied to each image, and fluxes were measured for the stars in the field with the DAOPHOT aperture photometry software  (Stetson 1987). Differential photometry was then performed using an algorithm developed by  Murray et al. (2020)  to automatically choose and combine multiple comparison stars, optimized to use as many stars as possible, weighted appropriately (accounting for variability, color and distance to target star), to reduce the noise levels in the final differential lightcurves.
Transit time measurements and analysis Gathering together the heterogeneous sample of transits obtained from a variety of ground-and spacebased telescopes, we transformed the time stamps to the BJD TDB time standard prior to photometric analysis. We analyzed the datasets together with a global photometric analysis of all single-planet transits, as described in  Ducrot et al. (2020) , with a separate analysis of the overlapping transits once the single-transit analysis was completed. For each planet a fixed time of transit for epoch zero (T 0 ) and fixed period (P ) were used, but with timing offset ("TTV") as a fitted parameter for each transit as described by  Ducrot et al. (2020). To derive T 0 and P , a linear regression of the timings as a function of their epochs was performed for each planet to derive an updated mean transit ephemeris; their exact values can be found in Table  4  of  Ducrot et al.
(2020). The timing offsets are then added back to the ephemeris to obtain the measured transit times and uncertainties. The final observed dataset for the transit-timing analysis is given by: y = ({t obs,ij , σ ij ; j = 1,. , N i }; i = 1,. , 7), where i labels each of the seven planets, N i is the number of transits for the ith planet (Table  1 ), and j labels each transit for the ith planet, so that t obs,ij is the jth observation of the ith planet, and σ ij is the corresponding measurement error. The total number of transits is N trans = Np i=1 N i = 447 where N p is the number of transiting planets. Table  14  lists the complete set of transit times and uncertainties which were utilized in the present analysis. With this sample of transit times collected, we proceed to describe our dynamical analysis, starting with the likelihood function and evidence for outliers.
Independent N-body TTV analysis In addition to the N-body code described above, we use the GPU hybrid symplectic N-body code GENGA  (Grimm & Stadel 2014)  with a Differential Evolution Markov Chain Monte Carlo Method (DEMCMC; ter Braak 2006) as described in  Grimm et al. (2018)  to perform an independent TTV analysis. The parameters for the MCMC analysis are x = ({m i , P i , t 0,i , e i , ω i }; i = 1,. , N p ). The mass of the star is taken to be M = 0. 09M , and the time step of the N-body integration is set to h = 0. 05 days. The likelihood is assumed to be a normal distribution with the timing errors derived from the timing analyses. For comparison, we have rerun the likelihood-profile computation described above using a normal distribution in place of a Student's tdistribution. The derived masses from the two different analyses agree well with a maximal deviation of the median masses of better than 0.
µ M ⊕ 0. 09M Mp σµ µ P t0 e cos ω e sin ω = Mp M ⊕ 0. 09M M * [10 -5 M * ] % [day] [BJDTDB-2, We fit a "photo-dynamical" model  (Carter et al. 2012 ) to the data with the following procedure. From the bestfit plane-parallel, edge-on transit time model, we compute the sky velocity at each of the mid-transit times, t 0 , from the model (in N-body code units). We then convert the code units to physical units using the density of the star, obtaining the sky velocity, v sky , in units of R * /day. We account for quadratic limb-darkening of the star with parameters (q 1,Ch1 , q 2,Ch1 , q 1,Ch2 , q 2,Ch2 ) in the two Spitzer channels, and for each planet we specify a planet-to-star radius ratio (R p /R * ) and we assume mid-transit impact parameter (b 0 ), which is constant for all transits of a given planet.
In table 5 we report the median and 68. 3% confidence interval of the stellar density. The inferred density is both slightly larger and more precise 9 As implemented in the package https://github. com/madsjulia/AffineInvariantMCMC. jl 10 Using https://github. com/tpapp/MCMCDiagnostics. jl than prior analyses  (Delrez et al. 2018a ), which we discuss below. Combining the measured density with the measured orbital periods of the planets, we derive the semi-major axis of each planet in units of the stellar radius, a R * = P 2 GM 4π 2 R 3 ρ * ρ 1/3. ( 11 ) With the measured impact parameters, we compute the inclinations of the planets from  (Winn 2010 ) I = cos -1 b 0 a R * -1 , ( 12 ) where we have neglected the eccentricity in this formula due to the extremely small values of the eccentricities of the planets from the transit-timing analysis (cf Table  2 ).
Core-free planets Given that the data may be consistent with an isocomposition mass-radius relation, we next consider another intriguing possibility: that the interiors of the planets are fully oxidized. If, instead of forming a core, all of the iron is oxidized and remains in the mantle, the size of a planet may increase by a few percent  (Elkins-Tanton & Seager 2008). This turns out to be about the amount of radius inflation necessary to match the TRAPPIST-1 planets when compared with our Solar system planets. If we assume that the refractory ratios match a Solar composition, and that all seven planets lack an atmosphere, then it turns out that all seven planets are consistent with a core-free, oxidized composition (Fig. 12 ; red line). For this model the bulk mass abundance ratios for Fe/Si/Mg/O are 29.
Planet masses and radii in context In our current analysis of the transit-timing data for TRAPPIST-1, we have found larger mass ratios for all planets save planet e compared with our most recent analysis in  Grimm et al. (2018). Even though most of the planets have shifted by 1σ or more, this does not indicate that the prior analysis was in error. In fact, the masses of all of the planets are strongly correlated, and thus when one planet shifts in the transit-timing solution, they all shift. With the more extensive dataset analyzed here, we provide a better constraint over the transit-timing timescale, and can also better account for outliers thanks to some redundancy in our measurements. Given the high precision of the Spitzer timing measurements, we expect that our current analysis may remain the most reliable constraint upon the masses of the planets until the transit times can be measured with JWST.
In Figure  19  we compare our measurements for the seven TRAPPIST-1 planets with our Solar System planets and with exoplanets with radii <1. 7R ⊕ and masses measured to >5σ retrieved from the NexSci database on 26 Feb 2020  (Akeson et al. 2013; Christiansen 2018) , as well as planet parameters reported in  Dai et al. (2019)  and Kepler-93b from  Dressing et al. (2015). 13 The uncertainties on the other planets' masses are the best available to date from radial-velocity measurements, and yet they are much larger than the uncertainties for the TRAPPIST-1 planets, whether considered in a relative or absolute sense. The larger uncertainties of the RV planets makes the core-mass fractions difficult to constrain for these more massive planets -core-free and cored models are consistent with most of these planets' parameters at the 1σ level (Fig.
2020 ). An effort to coordinate these observations amongst the exoplanet and planetary science communities is underway via the TRAPPIST-1 JWST Community Initiative  (Gillon et al. 2020). All to say, long-term studies of TRAPPIST-1 for spectroscopy will also yield transit times for each transit observed, enabling a transit-timing analysis of the results. To estimate the maximum possible precision of observations with JWST, we have simulated a five-year 14 For specifications of these programs, see https://www. stsci. edu/jwst/observing-programs/ approved-gto-programs. program in which every transit of every planet in TRAPPIST-1 is observed with NIRSPEC  (Birkmann et al. 2016). The NIRSPEC instrument was chosen as its prism mode covers 0. 5-5 microns, covering the peak of the SED of the star, and thus maximizing the number of photons detected, which is about two orders of magnitude per transit greater than collected by Spitzer.
Our approach yields a density of superior precision due to several factors. The transit times in the Spitzer data are constrained by all of the measured transits in the photodynamic model so that fewer degrees of freedom are needed to fit the times (37 free parameters in the N-body model versus 447 transit times fit to each transit). The stellar mass we take from the analysis by  Mann et al. (2019) , M * = 0. 0898±0. 0023M. 15 This mass has a precision of 2. 6%, which limits the mass precision for several of the planets. We are at the point that to improve the mass measurements of the planets we will need to improve the measurements of the star. We used the luminosity estimate from  Ducrot et al. (2020) , which is slightly lower than that estimated by  Gonzales et al. (2019)  due to a difference in the measured bolometric flux.
ACKNOWLEDGEMENTS This work is based in part on observations made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. Support for this work was provided by  NASA  through an award issued by  JPL/Caltech. EA was supported by a  Guggenheim Fellowship  and  NSF  grant  AST-1615315. This research was partially conducted during the  Exostar19 program  at the  Kavli Institute for Theoretical Physics at UC Santa Barbara , which was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958. We also acknowledge support from  NASA 's  NExSS Virtual Planetary Laboratory , funded under  NASA   Astrobiology Institute Cooperative Agreement Number   NNA13AA93A , and the  NASA   Astrobiology Program  grant  80NSSC18K0829.
This work was facilitated though the use of the advanced computational, storage, and networking infrastructure provided by the Hyak supercomputer system at the  University of Washington. TRAPPIST is a project funded by the  Belgian Fonds (National) de la Recherche Scientifique (F. R. S. -FNRS)  under grant  FRFC 2. 5. 594. 09. F. TRAPPIST-North  is a project funded by the  University of Liége , in collaboration with  Cadi Ayyad University of Marrakech (Morocco). B. -O. D. , C. D. and J. H. acknowledge support from the  Swiss National Science Foundation  (grants  PP00P2-163967 ,  PZ00P2_174028 , and  200020_19203 , respectively). Calculations were performed on UBELIX (http://www. id. unibe. ch/hpc), the  HPC cluster at the University of Bern. This work has been carried out in the framework of the  PlanetS National Centre of Competence in Research (NCCR)  supported by the  Swiss National Science Foundation (SNSF).
This research has made use of  NASA 's Astrophysics Data System and of services produced by the  NASA   Exoplanet Science Institute  at the  California Institute of Technology. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No. 832738/ESCAPE  and  European Research Council (ERC ; grants agreement n • 679030/WHIPLASH and  803193/BEBOP ). M. T. thanks the  Gruber Foundation  for its generous support to this research. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP/2007-2013) ERC Grant Agreement n°336480 , and from the  ARC  grant for Concerted Research Actions, financed by the  Wallonia-Brussels Federation.
Pontastacus leptodactylus is a native European crayfish species found in both freshwater and brackish environments. It has commercial importance for fisheries and aquaculture industries. Up till now, most studies concerning P. leptodactylus have focused onto gaining knowledge about its phylogeny and population genetics. However, little is known about the chromosomal evolution and genome organization of this species. Therefore, we performed clustering analysis of a low coverage genomic dataset to identify and characterize repetitive DNA in the P. leptodactylus genome. In addition, the karyogram of P. leptodactylus (2n = 180) is presented here for the first time consisting of 75 metacentric, 14 submetacentric, and a submetacentric/metacentric heteromorphic chromosome pair. We determined the genome size to be at ∼18.
, 1978; Wang et al. , 1999). Today, repetitive DNAs can now be analyzed more easily owing to the recent advances in next generation sequencing (NGS) and high-throughput in silico analysis of the information contained in the NGS reads  (Weiss-Schneeweiss et al. , 2015; Ruiz-Ruano et al. , 2019). Development of the RepeatExplorer software tool allows for de novo repeat identification using analyses of short sequences, randomly sampled from the genome  (Novák et al. , 2010 (Novák et al. , , 2013)). The Tandem Repeat Analyzer (TAREAN) further improved the RepeatExplorer pipeline allowing for the automatic identification and reconstruction of monomer sequences for each satDNA family in the species, collectively referred to as satellitome  (Novák et al. , 2017). Decapod crustaceans present an attractive study model due to the existence of polyploidy, a large quantity of AT-rich HSs as well as the adaptation to a broad range of environments  (Mlinarec et al.
DNA Barcoding and Phylogenetic Network Reconstruction Mitochondrial cytochrome oxidase subunit I (COI) barcode region was amplified and sequenced from genomic DNA of two individuals taken from Lake Maksimir using primer pairs LCO-1490 (5 ′ -GGTCAACAAATCATAAAGATATTGG-3 ′ ) and HCO-2198 (5 ′ -TAAACTTCAGGGTGACCAAAAAAT CA-3 ′ ) described in  Folmer et al. (1994). PCR reaction conditions and purification of PCR product followed the protocols described in  Maguire et al. (2014). Sequencing of purified PCR products was performed by Macrogen Inc. (Amsterdam, Netherlands). Phylogenetic analysis included a total of 129 COI gene sequences of which 127 were downloaded from GenBank (accession KX279350), while the other two were obtained from Lake Maksimir individuals obtained in this study (Supplementary Table  1 ).
Sequences were edited using SEQUENCHER 5. 4. 6 (Gene Codes Corporation, Ann Arbor, MI, USA) and aligned using MAFFT  (Katoh and Standley, 2013). Sequences were collapsed to unique COI haplotypes using the software DnaSP 6. 12. 03  (Rozas et al. , 2017). A median joining network was constructed on COI haplotype dataset using PopArt  (Bandelt et al. , 1999)  to visualize nonhierarchical haplotype relationships and their geographical distribution. Sites containing ambiguities were excluded from network reconstruction. This approach is recommended as a standard for cytogenetic studies as it links karyotypes with DNA barcodes  (Lukhtanov and Iashenkova, 2019).
Fluorescence intensities of 5000 nuclei per sample were recorded. We used the CytExpert 2. 3 software for histogram analyses. The total quantity of DNA in the sample was calculated as the ratio of the mean fluorescence signal of the 2C peak of the stained nuclei of the crayfish sample divided by the mean fluorescence signal of the 2C peak of the stained nuclei of the reference standard times the 1C amount of DNA in the reference standard. Three individuals were scored to produce biological replicates. For one individual we prepared different tissues to make sure that we have not used polyploid tissue. The genome size is reported as 1C, the mean amount of DNA in Mb in a haploid nucleus. Next Generation Sequencing, Data Pre-processing, and Clustering Analysis Raw Illumina pair-end reads 150 bp long obtained from low coverage DNA-seq experiments on Pontastacus leptodactylus are available from the European Nucleotide Archive (NGS run accession: SRR7698976).
After the quality filtering (quality cut-off value: 10 according to  Novák et al. , 2020b ; percent of bases in sequence that must have quality equal to/higher than the cut-off value: 95 and filtered against a costomized database containing P. leptodactylus mitochondrial sequences), the reads were subjected to similarity-based clustering analysis using RepeatExplorer2  (Novák et al. , 2010 (Novák et al. , , 2013)). We used a subset of reads (2 × 125,000) representing coverage of 0. 002×. Genome coverage was calculated as follows: coverage = (r × l)/g, where r corresponds to number of reads used in our analysis, l to read length and g to haploid genome size of P. leptodactylus. The clustering was performed using the default settings of 90% similarity over 55% of the read length. To confirm the results obtained through the RepeatExplorer pipeline, reconstruction of monomer sequences of individual satellite DNA families was performed using TAREAN analysis, specific for identification of satellite DNA repeats  (Novák et al.
Repeat Classification Repeat cluster classification of the top 0. 01% clusters identified in comparative analysis was implemented in RepeatExplorer through which similarity searches with DNA and protein databases. After de novo identification of contigs that make up repetitive elements in RepeatExplorer, contigs were further classified using two homology-based approaches applied in LTRClassifier  (Monat et al. , 2016) , specific for LTR retrotransposons, and Censor  (Jurka et al. , 1996)  for all repetitive elements. This was followed by manual examination of individual clusters graph shapes, similarity searches using BLASTN and BLASTX against public databases (https://blast. ncbi. nlm. nih. gov/Blast. cgi), inspection for the presence of sub-repeats using program dotmatcher (https://www. bioinformatics.
Preparation of Chromosome Spreads, Chromosome Measurements, and Idiogram Reconstruction Four adult males (m = 17. 01, 16. 10, 32. 21, and 16. 27 g) were used for the cytogenetic study. Chromosome spreads were prepared according to the method described in  Mlinarec et al. (2011). Individual chromosomes in karyotype were measured using LEVAN plug-in  (Sakamoto and Zacaro, 2009)  for the program ImageJ  (Schneider et al. , 2012)  to obtain the relative chromosomal length (RCL) data. RCL were then imported into the RIdiogram package  (Hao et al. , 2020)  of R programing environment for the ideogram reconstruction. Idiogram was further modified in the Inkscape vector graphics software (Inkscape Project, 2020) to include the 45S rDNA and DAPIpositive bands.
Accession Codes Cloned sequences of satellite repeats were deposited in genBank under accession numbers MW044674 for PlSAT1-21, MW044678 for PlSAT3-411, MW044675 for PlSAT6-70, MW044677 for PlSAT7-134, and MW044676 for PlSAT14-79. COI gene sequences were deposited in GenBank under accession numbers MW045515 for Hap1 and MW045516 for Hap2.
368, correlation coefficient: 0. 056) (Figure  3 ). Only one monomer of the perfect telomeric sequence motif (TTAGG/CCTAA) was present within the consensus sequence of 13 satellite elements, while monomers of other satDNAs contained no telomeric sequence motifs. Based on BLAST searches the satDNA sequences showed no similarity with any other DNA sequence deposited in non-redundant databases. Supplementary Table  2  shows the reconstruction of representative monomer sequences for each satDNA family. Genomic abundance of satellite DNAs ranged from 0. 01% up to 10. 91% of the genome (Supplementary Table  2 ). SatDNA family PlSAT1-21 showed the highest abundance (10. 91%), followed by PlSAT2-21 (3. 79%) and PlSAT3-411 (1. 29%).
Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i. e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.
The final results are 4-dimensional images that enable the visualization of brain structure and the study of the functional activities of the brain regions. fMRI can be conducted in two settings, resting-state and task-based. Even though experimental tasks fMRI, may be more suitable to achieve high classification accuracies. But for taskbased fMRI data, it is difficult to acquire large datasets. Usually the sizes of the task-based datasets are composed of less than 50 subjects  [11]. Moreover, it can be ill-suited for many individuals with ASD, particularly those who would be considered low-functioning or young children  [13]. Furthermore, the resting-state fMRI requires neither a constrained experimental setup nor the active and focused participation of the subjects. It also has been proven to capture interactions between brain regions that may lead to neuropathology diagnostic biomarkers  [14].
Many methods have been used to study the connectivity networks contained in resting state fMRI images, such as, the multi-echo independent component analysis (multi-echo ICA)  [15] , the Group-ICA  [16]  and the neighborhood one-class SVM  [17]. Those later allow to come up with descriptors in order to classify subjects. The big issue of the above cited works is the small size of the data that prohibits the generalization of descriptors, as addressed by  [18] ,  [19]. This made the authors of  [20] ,  [21]  and  [22]  use a large database, ABIDE, to overcome the handicap of reduced samples of previous autism research. In  [20] , 964 subjects fMRI images' BOLD signal were used to compute 7266 x 7266 connectivity matrices, by calculating the pair-wise correlation between each ROI. The 7266 ROIs were formed by clustering seed voxels into Euclidean-close regions separated by at least 5 mm.
For this, we use tools that are technically adaptive to this theory, namely, Hierarchical clustering (HC)  [25]  and Minimum spanning tree (MST)  [26]. HC permits to extract local-range and long-range connections, while MST is used to identify the under-connectivity between the long-range regions. It is worth mentioning that we already investigated the under-connectivity deficit in  [27]. Here, we extend the investigation to long-range (inter-cluster) under-connectivity, which is a more specific property of the autistic brain. To generalize our results, experiments are conducted on the ABIDE (Autism Brain Imaging Data Exchange) database. ABIDE was created to gather a large base of data to facilitate the autism research, and overcome the lack of generalization due to the small size of testing data.