dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and code availability d The raw sequencing data can be accessed in EMBL-EBI ArrayExpress under the accession numbers E-MTAB-12514 https://www. ebi. ac. uk/biostudies/arrayexpress/studies/E-MTAB-12514 and E-MTAB-12516 https://www. ebi. ac. uk/biostudies/arrayexpress/ studies/E-MTAB-12516. d The list of identified COs and GCs can be found in Table S4. Accession numbers are listed in the key resources table. d The related code is available at Zenodo/GitHub https://doi. org/10. 5281/zenodo. 8204099 d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Software and algorithms BWA v0. 7. 15-r1140 Li and Durbin 55 https://github. com/lh3/bwa Sambamba v0. 6. 8 Tarasov et al. 56 https://github. com/biod/sambamba inGAP-family Lian et al.
57 https://sourceforge. net/projects/ingap-family/ Tandem Repeats Finder v4. 09 Benson G. 58 https://tandem. bu. edu/trf mosdepth v0. 3. 3 Pedersen and Quinlan 59 https://github. com/brentp/mosdepth MADpattern v. 1. 1 Zhang et al. and White et al. 12,50 https://github. com/mwhite4/MADpatterns R v3. 6. 2 https://cran. r-project. org/ ggplot2 v3. 4. 0 https://ggplot2. tidyverse. org/ Pombe_Recombination-v1. 0 This study https://doi. org/10. 5281/zenodo. 8204099 Other Schizosaccharomyces pombe reference genome PomBase database https://www. pombase. org.
We spaced these feeders around the woodland in an approximate grid to cover the entire study area (Fig. 1C ). Access points to feeders were equipped with radio-frequency identification (RFID) antennae (NatureCounters Ltd), with data loggers (Priority1 Design, Australia) recording visits of PIT-tagged birds. We then used the spatiotemporal patterning of these visits to build a foraging association network (see below). On 26 March 2021, we deployed five dispensers containing felting merino wool, each offering two colors of similar hues ad libitum (Fig. 1A ). We used a balanced experimental design with three dispensers providing orange and pink wool, and two containing blue and purple wool (Fig. 1C ). These were spaced approximately 300 m apart to avoid too much overlap between dispenser areas, based on  Surgey et al.
(2012)  that suggested that tits are unlikely to travel much further than 200 m to collect wool. Each dispenser was equipped with RFID antennae and data loggers to record visits to the wool by PIT-tagged birds. We initially created an artificial local preference for one of two wool colors in each area by blocking access to the other color with a transparent plastic sheet. Once provisioned material was observed by experimenters in one or more nests in each dispenser area, we allowed access to both colors (Table  S1 ). To test for any potential innate color preferences, we additionally deployed four dispensers in two separate control areas (not shown in Fig. 1A ), with unlimited access to the two-color pairs (orange/pink and blue/purple) at all times. Throughout the experiment, we monitored all nest boxes across the woodlands every 2 -4 days, recording nest stage and the presence (yes/no), color, and amount (categories 1 -4, see Table  S2 ) of any provisioned lining material.
1C ). From these visits, we identified groups using a Gaussian mixture model to detect clusters in the data stream  (Psorakis et al. 2012). We then built social networks using a gambit of the group approach and calculated edge weights using the simple ratio index which ranges from 0 (never observed together in a group) to 1 (always observed in the same group)  (Cairns and Schwager 1987; Farine 2013; Farine and Whitehead 2015; Hoppitt and Farine 2018). Since the foraging network was highly stable across the 3 weeks of data collection (week 1 to week 2, Mantel R = 0. 634, p = 0. 001; week 2 to week 3, Mantel R = 0. 650, p = 0. 001), we used a static network in NBDA based on all the association data  (Hasenjager et al. 2020). To construct the spatial breeding network, we calculated the Euclidean distances between nest boxes using their GPS locations.
By contrast, we recorded eight females in the control areas using provisioned wool. Data on the first incorporated wool color was only available for two out of the eight nests (one pink, one yellow), but females of all eight nests incorporated both colors. Of the 23 tagged females that visited the dispensers after access was granted to both colors, 16 were registered on both antennae, while seven were only registered on one antenna (Fig. S1 ). This indicates that both antennae were used for perching and that RFID data were not a reliable indicator of the color choice (see also Supplementary file 1).
Background Alzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable.
Estimation procedure The estimation of our disease progression model was done in the Bayesian framework using Hamiltonian Monte Carlo No-U-turn sampling algorithm (HMC-NUTS)  [42]  to approximate the posterior distribution of the parameters with Markov Chain Monte Carlo (MCMC). We used Stan software (version 2. 20. 0)  [43, 44]  through the CmdStan interface with parallel computations on both the chains and the individuals. A commented version of our program, freely adapted from LTJMM  [19] , is available at https:// github. com/ jrmie/ dpm_ ancho red.
This was the case for years of education that showed large differences only in the neuropsychological tests, as an illustration of the concept of cognitive reserve  [52]. Finally, in cohorts on Alzheimer's disease, dropouts and deaths occur and are likely to be linked to the disease progression. In the MEMENTO sample, 786 (36. 0%) patients dropped out or died. By using the mixed model theory, our analyses are robust to missing data under the missing at random (MAR) mechanism which stipulates that the probability of missing data can be fully predicted by the observations. Given the large number of repeated markers we included, the MAR assumption for dropout and death is highly plausible (although not checkable) and our results should not be impacted. Nevertheless, accounting for informative dropout and death would be possible by incorporating the DPAM into a joint modeling approach of the risk of dropout and death  [53].
Funding The MEMENTO cohort is funded by the Fondation Plan Alzheimer (Alzheimer Plan 2008-2012), and the French Ministry of Research (MESRI, DGRI) through the Plan Maladies Neurodégénératives (2014-2019). This work was also supported by CIC 1401-EC, Bordeaux University Hospital (CHU Bordeaux, sponsor of the cohort), Inserm, and the University of Bordeaux. This work received funding from the French National Research Agency (ANR) as part of the Investment for the Future Programme ANR-18-RHUS-0002. The MEMENTO cohort has received funding support from AVID, GE Healthcare, and FUJIREBIO through private-public partnerships. The Insight-PreAD substudy was promoted by INSERM in collaboration with the Institut du Cerveau et de la Moelle Epinière, Institut Hospitalo-Universitaire, and Pfizer and has received support within the "Investissement d' Avenir" (ANR-10-AIHU-06) program.
During the deployment process of BW3, we observed a bright object decoupling from the satellite in three datasets from the Ckoirama Chakana 0. 6 m telescope combined with a simultaneous observation using the Cerro Tololo Inter-American Observatory (CTIO) 0. 9 m telescope on the evening of 10 November 2022 UT. This was later identified through two-line element (TLE) matching as the launch vehicle adaptor (LVA) shown in Fig. 4. The LVA is used to house and protect the folded antenna array during launch and until time of deployment (unfolding). The apparent V-band magnitude of the LVA was measured to be around 5. 5 (slightly fainter than the Starlink Gen1 satellites  3, 11  ): roughly four times brighter than the Dark and Quiet Skies II recommendation that the maintained brightness of a satellite should not exceed magnitude 7 when the orbital height is equal to or less than 550 km (refs.
Observations and data reduction Observations are reported per observatory below. The observatories and the corresponding orbit of BW3 on the days of the observations are shown in Extended Data Fig. 1. All reported satellite magnitudes are corrected for exposure time and the satellite's angular speed across the sky. For images where the trail has no start or end points, this is determined by the ratio of the time required to transverse the detector, τ, based on the trail length and the satellite's angular velocity, dividing by the exposure time, t. The integrated flux of the background stars is then multiplied by this ratio. This is required because although the integrated flux of the background stars is total flux received in t seconds, the total flux within the trail aperture is received in τ seconds.
CMOS Sigma 0. 135 m observations BW3 was observed on two nights (12 and 13 November 2022 UT) from Tucson, Arizona, USA (latitude 32. 3740°, longitude -111. 0168°) using a ZWO432 complementary metal-oxide-semiconductor (CMOS) sensor attached to a Sigma 135 mm f/1. 8 lens. The weather was photometric on the 12th and high cirrus on the 13th; therefore, only the data from the 12th are reported. The data were collected by pointing the instrument at a star along the predicted track of the satellite. To avoid saturation, exposure times for BW3 were kept short (16 ms), which created an additional challenge of insufficient signal-to-noise ratio (SNR) on background stars for photometric calibration. Five 15 s sidereal-tracked images of the same field were obtained before and after the satellite pass (10 images total) for photometric calibration.
Chakana, CTIO, DDOTI and Oukaïmeden observations Observations of BW3 were performed on two nights, 9 and 10 November 2022 UT, using the Chakana 0. 6 m telescope located at the Ckoirama Observatory  21  in northern Chile (latitude -24. 089°, longitude -69. 931°). Observations used the Sloan g′ filter and a 2 × 2 binning technique to reduce the charge-coupled device (CCD) readout dead time and increase the SNR of background stars, thereby improving the accuracy of the differential magnitude measurements of the satellite trail. The observing strategy used the standard 'wait and catch' technique while tracking at the sidereal rate, where the telescope was positioned at the predicted RA and declination (dec. ) derived from a low Earth orbit (LEO) satellite ephemeris code (https://github. com/CLEOsat-group/ satellite-tracking), and the exposure began 5 s before BW3 reached the predicted coordinates.
0455°, longitude -115. 4658°). DDOTI has six 28 cm telescopes on a common mount. Each telescope is equipped with an unfiltered 6,000 × 6,000 CCD, which gives a field of 3. 4° × 3. 4° and a pixel scale of about 2 arcsec pixel -1. For the observations presented here, two of the telescopes were out of service for maintenance, and so, the total field was about 7° × 7°. Further observations of BW3 were conducted on 16 and 17 November 2022 UT with a Takahashi telescope in Oukaïmeden, Morocco (latitude 31. 2064°, longitude -7. 866°)  23. The observations used a CMOS camera and performed both 2 s exposures (16 November 2022) and 1 s exposures (17 November 2022). Sidereal tracking was used, and the data allow for the extraction of the reflective brightness of BW3 and estimate the accuracy of the TLE used to predict the sky position.
The reduction, astrometric calibration, satellite trail detection and its analysis for the observations from Chakana, DDOTI and Oukaïmeden were performed with the CLEOSat pipeline, a custom and open-source end-to-end Python pipeline for the processing and analysis of satellite trail observations (C. A. , J. T. -R. , E. U. -S. and S. N. , manuscript in preparation). The raw FITS image files were processed with ccdproc (https://ccdproc. readthedocs. io/en/). This includes subtracting the dark frame from each image to remove the instrumental signature as well as dividing by the average flat field to correct for non-uniform sensitivity across the chip. The reduced FITS images were then calibrated to be able to map the positions of sources on the detector to their celestial coordinates on the sky.
Reduction of instrumental signatures for dark current and flatfielding as well as photometric calibration of field stars to SkyMapper Southern Sky Survey DR1. 1 (ref. 32) (Vizier: CDS/II/358/smss) to establish zero points was made with Pouākai (https://github. com/CheerfulUser/ Pouakai), with astrometric calibration via astrometry. net (ref. 33). Images acquired back to back were subtracted. Circular aperture photometry was applied at 0. 005 s intervals along the TLE-predicted satellite trail. As the TLE did not correspond to a detected trail, we fit a Gaussian to the normalized counts of the detected trail to spatially offset the TLE onto the trail centre. MOA-R is a substantially wider bandpass filter than Johnson V band, but assuming BW3 is a solar-neutral reflector, we applied an image-specific offset to the MOA-R zero points, thereby transforming the MOA-R photometry to the Johnson V magnitudes reported here.
Magnitudes were measured in the red band by TANGRA (http://www. hristopavlov. net/Tangra3/). These 'red' magnitudes were transformed into V-band magnitude equivalent values by mapping over 200 brightness measurements on stars against their catalogue V-band magnitude, from which an empirical relationship for 'red' magnitudes against V-band magnitudes was obtained for this camera. The Phase Alternating Line (PAL) signal output from the camera was fed into a GPSBoxsprite-2 global positioning system time inserter, which imprinted each video frame with a pulse per second time signal, allowing timekeeping at the millisecond level. The signal was next digitized by an EZcap dongle and recorded in AAV format on a laptop using OccuRec (http://www. hristopavlov. net/ OccuRec/OccuRec. html). The video frames were astrometrically solved on a frame-by-frame basis with TANGRA software (https://www.
Visual observations Visual observations were made between 3 October 2022 and 16 January 2023 UT from sites near Tulsa, Oklahoma, USA. The observer has 20+ years of experience of tracking satellites, has seen 8,000+ unique objects and has reported 25,000+ passes and thousands of brightness estimates of satellites and variable stars (https://hafsnt. com/). In some cases, handheld binoculars were used. Data were gathered on 23 occasions  7. Pass predictions were obtained for the observer's two sites (latitude +36. 139°, longitude -95. 983° and latitude +35. 831°, longitude -96. 141°) from https://www. heavens-above. com/. At a minimum, the location, expected time of observation and reliable limiting brightness must be known, bearing in mind that moving objects may appear visually dimmer than predicted.
TLE accuracy The coordinates (α, δ) of the midpoint of the observed satellite trails of BW3 were obtained using the LEOSat pipeline. The pipeline uses observations and TLEs to detect and analyse satellite trails. Given the TLE, the pipeline provides magnitudes and coordinates of the trails by performing astrometric and photometric calibrations and so, measures the observed trail length. In addition, the pipeline uses the pyorbital package (https://github. com/pytroll/pyorbital) to perform orbital calculations (using the SGP4 simplified perturbation model) to determine the RA and dec. of a satellite for given times. Measurements of the TLE spatial and temporal accuracy require datasets that contain at least one end point of the satellite trail. Satellite trails are point sources spread over a specific length because of the satellite's angular velocity.
For datasets where there are no end points (that is, small FOV detectors), it is only possible to measure the spatial accuracy of the TLE because of the fact that it is not possible to precisely know the satellite's position as a function of time (that is, the point source could be at any point along the trail for any given time in the exposure timestamp). To select coordinates along the sky track of BW3 on each pass, the BW3 TLEs used to predict BW3 orbital ephemerides and therefore, the sky position were used to provide a predicted track in the image. This provides an opportunity to measure the accuracy of the TLEs of BW3 for the observations in which at least one end point of the trail is visible. This provides a boundary condition when integrating the angular velocity over the trail length to obtain the position as a function of time of a moving source.
The results show that while below an elevation of 20°, |σ t | = 2. 38 ± 0. 42 s, where the quoted uncertainty is the 1σ distribution. However, when above 20° elevation, this reduces to |σ t | = 0. 18 ± 0. 06 s. To determine the confidence of the large error measured from the Takahashi data, all known instrumental uncertainties were examined. To examine the propagation of the uncertainties in the orbital equations used by pyorbital, some of the data were compared with TLE predictions using skyfield (https://pypi. org/project/skyfield/). The predicted positions between the two models differ on average by 30 arcsec, which alone cannot explain the large 24 ± 6 arcmin difference between the measured BW3 position and that predicted by the TLE. The next set of errors can come from the accuracy of the telescope location.
The TLE errors are shown in Extended Data Fig. 2  and hint that the timing errors are dependent on elevation. For sensitive detectors, such as those of the Simonyi Survey Telescope's camera of the Vera C. Rubin Observatory, having low-accuracy ephemeris predictions of bright (V < 7) artificial satellites will be a major concern. It will prevent the avoidance of satellites that are too bright for minimization of the impacts of electronic ghosts and non-linear image artefacts by correction to at least the background level  3. Another source of error of the TLE accuracy is the amount of time passed since the TLE epoch. This is often every 8 h but can be longer in some cases. For example, once the LVA was added to the catalogue, there were about two updates a day on average, varying between zero and four per day, with occasions where there were no updates for several days.
Acknowledgements S. E. and C. E. W. acknowledge the support of the  IAU CPS SatHub. S. E. acknowledges  D. Stanley  for his assistance with satellite ground track generation. J. T. -R. acknowledges financial support from  ANID/FONDECYT  (an  Initiation in Research grant ; Project Number  11220287 ), and this material is based upon work supported by the  Air Force Office of Scientific Research  (Award  FA9550-22-1-0292 ). C. A. acknowledges the support of the  Regional Fund  granted by the  ESO-Government of Chile Joint Committee. J. A. -B. and M. T. B. acknowledge  F. Gunn  and  I. Bell-Butler  for their support in the Ōtehīwai Mt. John observations. M. T. B. appreciates support from the  Rutherford Discovery Fellowships  from  New Zealand Government  funding, administered by the  Royal Society Te Apārangi.
Data availability Raw fits images, including calibration frames, are available on Zenodo (https://doi. org/10. 5281/zenodo. 8102655). Raw video data (AAV format) are available on Zenodo (https://doi. org/10. 5281/zenodo. 8102655). The visual data of B. Y. are available at https://hafsnt. com/index. php/ blue-walker-3/. The astrometric data of M. L. are available at https://doi. org/10. 4121/07711969-6e9a-4944-9132-8cdc005ee6a4. Reduced data tables are provided as supplementary machine readable files. Source data are provided with this paper.
Code availability Prediction of expected RA and dec. of satellite for observation is from https://github. com/CLEOsat-group/satellite-tracking. Processing raw FITS files is from https://ccdproc. readthedocs. io/en/. Detection of sources for astrometric calibration is at https://photutils. readthedocs. io/en/stable/. Reduction of Ōtehīwai Mt. John observations is at https://github. com/CheerfulUser/Pouakai. TLE accuracy is at https://github. com/pytroll/pyorbital and https://pypi. org/project/ skyfield/. LEOSat pipeline magnitudes and positions of satellite tracks are at https://github. com/CLEOsat-group/leosatpy https://zenodo. org/record/8012132 and https://doi. org/10. 5281/zenodo. 8012132. TLEerror code is at Zenodo: https://zenodo. org/records/8132639.
B) Control Task Fig. 2  Performance Depending on Age. Performance on both tasks depending on age (months) averaged across prime condition for the developmental language disorder (DLD) and matched control (TD) groups. A For the sentence repetition task, sentence productions were scored for a total possible score in each condition of 18 (current data is averaged across prime conditions). B For the control task, the total number of animals circled was scored for a total possible score in each condition of 99 (current data is averaged across prime conditions). Regression line fitted in R with a linear model for illustration. Shaded error bars represent standard error of the mean. Individual points represent individual participants. results and previous work showing a link with reading age and the efficacy of regular rhythmic primes in TD children  13  , it will be particularly promising to investigate links between reading age and efficacy of rhythmic priming, as well as the mechanisms underlying this connection.
83 months, SD = 4. 72). Data from an additional nine children were collected, but excluded due to a technical error (four children with DLD, one with TD) or failing to meet inclusion criteria (four children with TD). All children with DLD (except one, see below) performed below standardized cut-offs on at least one of the following tasks: (1) Sentence Repetition and (2) Sentence Elaboration subtests from the French version of the Clinical Evaluation of Language Fundamentals (CELF-5  65  ; age-normed cut-off = 7 in each task) or (3) the non-word repetition test from the Batterie Analytique du Langage Écrit 66 (>2 SD below the norm). These tasks are typically used as clinical markers for DLD  67, 68. One older child with DLD performed just above the cut-off on the two CELF tests (with a standardized score of 8; 25th percentile), and below the norm (though within 2 SD) on the non-word repetition test, so was still included.
General design Children came to the lab two times to complete all tasks. The sentence repetition task and the cancellation task were performed in different visits, and order was counterbalanced across children. The additional tasks testing for language and cognitive skills were presented in a fixed order and interspersed with experimental tasks. The beat alignment test (BAT) for rhythm perception was always completed at the end of the second visit so that it did not interfere with the priming tasks. Children also completed further behavioral tasks outside the scope of the current study. The sentence repetition priming task and the BAT, as well as the prime presentation for the visual cancellation task were programmed and presented in E-Prime 2. 0. Children received a small gift at the end of each experimental session.