dataesr/scientific-paragraphs-categorization huggingface dataset:
2 Hz. The precision and accuracy of the measurements is dominated by the signal-to-noise ratio (SNR), compared to which the contributions from cavity mode position uncertainties and frequency reference stability is negligible. The chief advantage of the broadband mode-dispersion technique, which cannot be implemented in conventional Fourier-transform spectrometers based on thermal light sources,  Table  1. Positions of the second overtone rovibrational lines of CO in N 2 at 714 Torr pressure, 21. 5 °C temperature. The uncertainties of dispersion and absorption measurements in the parentheses are 2σ fit uncertainties. The uncertainties of reference line positions are the combined uncertainties of pressure shifts from ref. 42  and transition frequencies from the HITRAN database. Diff. columns contain differences in line positions between dispersion and absorption measurements, and dispersion and reference values.
Acknowledgements The authors acknowledge support by  KIT  through the  "Virtual Materials Design" (VIRTMAT)  project by the  Helmholtz Association  via the  Helmholtz program  " Science and Technology of Nanosystems " (STN), by the  German Excellence Strategy through Deutsche Forschungsgemeinschaft (DFG)  via the Excellence Cluster EXC 2082 "3D  Matter Made  to  Order"  ( 3DMM2O ), the  Carl Zeiss Foundation , and by the  KIT Nanostructure Service Laboratory (NSL). M. Kadic was supported by the  French "Investissements d'Avenir" program , project  ISITE-BFC  (Contract No. ANR-15-IDEX-03 ).
Retinal Function Analysis by Electroretinography 76] [77] 83  Scotopic ERGs were performed at 6, 8, 10, and 12 hours postinfection using Espion E2 software (Diagnosys LLC, Lowell, MA, USA). After infection, mice were dark adapted for at least 6 hours. Infected mice were anesthetized as previously described and pupils were dilated with topical phenylephrine (Akorn, Inc. ). Two gold wire electrodes were placed on each cornea and reference electrodes were place on the forehead and on the tail. Eyes were then stimulated by five flashes of white light (1200 cd s/ m 2 ) and retinal responses were recorded as A-wave and B-wave amplitudes for infected eyes and compared with the uninfected eyes of the same animal.
Acknowledgments The authors thank  Mark K. Coggeshall ,  Tarea Burgett , and  Alanson W. Girton  ( Oklahoma Medical Research Foundation ) for stimulating discussions on peptidoglycan purification,  Feng Li  and  Mark Dittmar  ( OUHSC Live Animal Imaging Core, Dean A. McGee Eye Institute, Oklahoma City, OK, USA ) for invaluable technical assistance, and  Excalibur Pathology (Moore, OK, USA)  and the  OUHSC Cellular Imaging Core  ( Dean A. McGee Eye Institute, Oklahoma City, OK, USA ) for histology expertise. The human Muller cell line (MIO-M1) was a kind gift from  Astrid Limb  ( Institute of Ophthalmology, Moorfields Eye Hospital, London ). Supported by  National Institutes of Health  grants  R01EY028810  and  R01EY024140  (to MCC). Our research is also supported in part by  National Institutes of Health  grants  R01EY025947  and  R21EY028066  (to MCC),  National Eye Institutes Vision Core  Grant  P30EY027125  (to MCC), a  Presbyterian Health Foundation   Research Support Grant Award  (to MCC), a  Presbyterian Health Foundation Equipment Grant  (to  Robert E.
FUNDING SZ and DY were sponsored by "China Scholarship Council (CSC)". SZ was also funded by the "Les Amis des Instituts Pasteur à Bruxelles, asbl". We are grateful to Professor Jean-Paul Dehaye for the discussion. This study was also sponsored by the "Belgian Fund for Scientific Research (Grand equipment-F. R. S-FNRS). " The bioprofiling platform used for the proteomic analysis was supported by the European Regional Development Fund and the Walloon Region, Belgium. This work was boosted by the iPROMEDAI TD1305 European COST action.
Participants Thirty-six (n = 36) elite male hurlers (mean ± SD, age: 27 ± 4 years, height: 181 ± 5 cm, mass: 86 ± 4 kg) volunteered to partake in the current observational investigation. To be considered as elite, each player has been selected from this club to join the county team as previously described  [1, 6]. Specifically, they competed at the highest level (Provincial and All-Ireland Championship) according to the Gaelic Athletic Association rules  [1, 5, 6]. The players were classified according to their playing position during each match resulting in the following number of data sets per position: full backs: n = 50, half backs: n = 50, midfielders: n = 50, half forwards: n = 50, and full forwards: n = 50. Only those who were free from injury and illness were eligible to partake in the study.
Procedures The current study was designed to examine the metabolic power variables of elite hurling players with respect to position and halves during competition. The sample size was based on previous hurling studies  [1, 6]. Data were collected during 22 games across 3 full competition seasons (February 2016 -August 2018) resulting in 250 individual samples being collected (Fig  1 ). Data were included only if a full match (70 minutes) was completed. GPS was used to quantify players' running performance during competitive games. All competitive matches took place between 14. 00 and 18. 00 hours. The players were requested to abstain from strenuous physical activity in the 24 hours before competitive matches and to report to the game fully hydrated  [1]. The players' movements were measured using GPS sampling at 10-Hz (STATSports Viper Pod, Newry, Northern Ireland).
The GPS device was encased within a protective harness between the player's shoulder blades in the upper thoracic-spine region  [1, 26]. Before entering the field of play GPS devices were fixed to the athletes, the device was then activated and satellite lock established for a minimum of 15 minutes before the commencement of each match  [27]. All players wore the same GPS unit for each match during the seasons analysed to minimize inter-unit error  [28] [29] [30]. The validity and reliability of this device have previously been reported  [30, 31]. After the completion of each match, GPS data were downloaded to a computer through the bespoke STATSport analysis software (STATSport Viper Firmware 2. 28) to be stored and analysed. Each file was trimmed so that data recorded only when the player was on the field were included for further analysis.
The proprietary software provided instantaneous raw velocity data at 0. 10-second intervals, which was then exported and placed into a customized Microsoft Excel spreadsheet (Microsoft, Redmond, WA, USA). This customized spreadsheet allowed for the calculation of traditional speed-based measures such as: total distance (m), relative distance (m�min -1 ), high-speed distance (m: � 17 km�h -1 ), sprint distance (m: � 22 km�h -1 ), N˚accelerations (> 2 m. s -2 ), N˚decelerations (< 2 m. s -2 )  [32] , and maximal velocity (km�h -1 )  [4]  (e. g. S1 Table ). Furthermore, the spreadsheet allowed for estimation of average metabolic power (P met : W�kg -1 ) and power across 6 zones: minimal power (> 0-5 W�kg -1 ), low power (> 5-10 W�kg -1 ), intermediate power (> 10-15 W�kg -1 ), moderate power (> 15-25 W�kg -1 ), high power (> 25 to 50 W�kg -1 ), elevated power (> 50 W�kg -1 ), and HMLD (> 25 W�kg -1 )  [8].
Statistical significance was set at a � 0. 05. Cohen's effect size (d) was used to describe the differences in running performance across positions and halves of play and was categorized with d < 0. 20, 0. 20-0. 59, 0. 60-1. 19, 1. 20-1. 99, and � 2. 00 and interpreted as follows: trivial, small, moderate, large, and very large differences, respectively  [33]. Statistical analysis was performed using SPSS version 22. 0 (IBM Corp, Armonk, NY, USA).
Kernel-phase observables extracted from mid-to high-Strehl images are proving to be a powerful tool to probe within a few angular resolution elements of point sources. The attainable contrast is however limited by the dynamic range of the imaging sensors. The Fourier interpretation of images with pixels exposed beyond the saturation has so far been avoided. We show that in cases where the image is dominated by the light of a point source, we can use an interpolation to reconstruct the otherwise lost pixels with an accuracy sufficient to enable the extraction of kernel-phases from the patched image. We demonstrate the usability of our method by applying it to archive images of the Gl 494AB system, taken with the Hubble Space Telescope in 1997. Using this new data point along with other resolved observations and radial velocity measurements, we produce improved constraints on the orbital parameters of the system, and consequently the masses of its components.
However it was found that a uniform error estimator made the interpolation less sensitive to the evolution of the small instrumental phase over long timescales, and therefore produced smaller biases in the observables. In practice, since the images are large, an exponential windowing function (sometimes called super-Gaussian) was used to exclude the pixels that have low SNR: g = e -( r r 0 ) 4 , ( 2 ) where r is the distance from the pixel to the approximated center of the star, and r 0 is a radius parameter chosen depending on the SNR of the image. From the error estimation point of view this can be seen as using the inverse of the values of the mask as the corresponding σ 2 ii terms of a diagonal matrix Σ. A constant value of r 0 = 40 pixels gave satisfactory results with the NICMOS images and was used for the whole dataset.
The Kernel-Phase method and pipeline Kernel analysis relies on the discrete model of the pupil of the telescope which was built using a dedicated python package called XARA 1 , developed in the context of the KER-NEL project. A discrete pupil model of 140 subapertures was constructed following a Cartesian grid covering the aperture of the telescope with an outer diameter of 1. 95 m, with an inner obstruction diameter of 0. 71 m and spider 0. 07 m thickness spider arms. Figure  2  shows the sampling of the model both in the pupil plane and in the UV plane. According to the Nyquist-Shannon sampling theorem, the grid's pitch of b min = 0. 13 m allows for detection up to ρ max = λ/2b min ≈ 1. 76 arcsec at 2. 22µm without aliasing. The extraction of kernel-phases requires five steps: 1. Integer pixel recentering of the image.
2. Application of an apodization mask to reduce the effect of readout noise and avoid Fourier domain ringing. 3. Sub-pixel recentering of the image by application of a wedge phasor in the Fourier domain. 4. Extraction of the complex visibility vector. 5. Multiplication of the phase of the visibility by the Kernel matrix. This procedure is applied to each image of a given dataset, and produces each time a vector κ of n k = 262 kernelphases.
Fidelity of the saturation recovery procedure Since the aim of this study is to enable the extraction of kernel-phases, the main concern is with the fidelity of the 1 XARA is available at github. com/fmartinache/xara phase of the Fourier transform of the image. The discretized pupil model (see Sect. 2. 3) provides a comprehensive sampling scheme of the Fourier plane. A binary star image with a 10 3 contrast companion is simulated as described in 2. 2 with a central peak intensity corresponding to four times the dynamic range of the sensor, resulting in nine clipped pixels. Figure  3  shows the Fourier phase compared between the saturated image where the outer edge of the uv plane is unusable, the ideal nonsaturated image, and the recovered image for which the phase information was restored.
Kernel analysis of NICMOS archival images The dataset that we propose to examine comes from the Hubble Space Telescope proposal # 7420. The aim of the study was to observe nearby stars within 10pc of the Sun in order to find very low mass (M < 0. 2M ) stars. Observations were conducted using the HST Near Infrared Camera and Multi-Object Spectrograph camera 2 through the F110W, F180M, F207M and F222M filters during from 1997 to 1998. Analysis of the data was published by  Krist et al. (1998)  and  Dieterich et al. (2012)  where PSF subtraction was performed through manual adjustments. They determined detection limits by trying to identify artificially inserted companion stars over the subtraction residual. Contrast detection limits are given for the F180M filter from 0. 2" to 4". In comparison, our model is sensitive between 0.
Here we relied on the metadata provided along with the image in the cal. fits output of the CALNICA re-duction pipeline. This is explained in more detail for our case in chapter 2. 2. 1 of the NICMOS Data Handbook by  Thatte & Dahlen (2009). All the images of the dataset were obtained using MUL-TIACCUM mode with a STEP128 sampling scheme for 128 seconds exposures. This is a mode that uses multiple nondestructive readouts to compute the flux on each pixel. If saturation is detected in some of the last samples, the first non-saturated samples can still be used to compute the flux. In this case, a pixel is only considered saturated if the first 0. 303 seconds of exposure are sufficient to saturate the pixel. This explains the remarkable dynamic range of single images. Bad pixels are also identified during this step.
Choice of calibrators The calibration of interferometric observations requires the selection of point sources observed under similar conditions and with spectral and photometric properties similar to those of the target. The dataset associated with proposal # 7420 consists of images acquired on a wide variety of targets with respect to intrinsic brightness and spectral types; with observations separated by several weeks. We used two different procedures for: the detection of binary signals among all targets of the sample and; for the binary model fitting and parameter estimation. For the detection, we built a generic calibrator through the elimination of images producing outlier kernel signals (in the sense of Euclidean distance to the median of the remaining subset) one at a time until 10 out of the original 159 remained.
3λ/D) with a high contrast ( ≈ 55), and the central peak of the images is saturated. Contrast sensitivities for this target in the same dataset in the F180M filter were later reexamined by  Dieterich et al. (2012)  to be ∆ H = 0. 1 at 0. 2" and ∆ H = 2. 6 at 0. 4" on this target which is insufficient for this system. Figure  8  shows a crop of the saturated image used here, and the corresponding unusable uv phase signal. Overcoming the saturation problem allows us to use the more sensitive kernel analysis and adds a valuable high precision visual astrometric point. With the recent observations reported by  Mann et al. (2018) , the visual observations of this binary system now span 18 years, covering more than one orbital period. Together with the radial velocity data, these observations give strong constraints on the orbital parameters of the system, and therefore on the masses of its components.
(2007). The lmfit package  (Newville et al. 2014) , a python implementation of the Levenberg-Marquardt algorithm is used to obtain a least-square fit for a 10 degrees of freedom problem, resulting in the parameters presented in table 3 and Fig. 9. The apparent magnitude of the system from 2MASS m K = 5. 578±0. 016 together with the distance and contrast provide us with absolute magnitude of M K = 5. 29 ± 0. 017 for Gl 494A and M K = 9. 65 ± 0. 15 for Gl 494B. Figure  10  shows how this compares to the expected mass-luminosity relationship provided by  Benedict et al. (2016)  for the M dwarfs in the solar neighborhood. The two components are found to be in reasonable agreement with the model.
Conclusion We have demonstrated using both simulation and on-sky data that it is possible to use our knowledge of the PSF to recover the saturated pixels of images. Unlike the original images, the mended images can then be used successfully in kernel-phase analysis. The proposed method works particularly well in the case of images dominated by the PSF of a single star, like in the case of high contrast binary stars or exoplanetary systems. Its primary purpose is to expand the field of applicability of kernel-phase methods, especially to the larger body of archival space telescope images. As an example, we have used the technique on images from the HST of the Gl 494AB system with a saturated core, and produced a new visual astrometry data point at an interesting older epoch and small uncertainties.
Acknowledgements. The authors wish to thank  Xavier Delfosse ,  Stephane Udry ,  Damien Ségransan  and the  CORAVEL team at the observatoire astronomique de l'université de Genève  for allowing us to use the data from the CORAVEL instrument; and  Sébastien Peretti  for helping us with the data. KERNEL has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement  CoG -683029 ). This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
For DNA staining, 2 μg/ml of ethidium bromide is added on each sample. Fifty cells per slide and 2 slides per sample were analysed using a Nikon 50i fluorescence microscope equipped with a camera and the Komet 6. 0 software. The extent of DNA damage was evaluated for each cell through the measurement of intensity of all tail pixels divided by the total intensity of all pixels in head and tail of comet. The median from these 100 values was calculated and named as % tail DNA. The experiment was done in triplicate.
Reactive oxygen species formation Intracellular H 2 O 2 was measured by flow cytometry using H2-DCF-DA (dichlorodihydro-fluorescein diacetate) (Life Technologies). After treatment with filtered native or aldehyde-depleted faecal waters diluted 1/100 for 1 h, the cells were gently trypsinized, stained for 20 min at 37 °C with H2-DCF-DA (10 μM) in HEPES-buffered solution, and next analysed using a MACSQuant Analyzer (Miltenyi Biotec). Menadione (100 μM) (Sigma-Aldrich) was used as a positive control. Each measurement was conducted on 40,000 events in the B1 channel (525 ± 25 nm) and analysed with VenturiOne software.
Instrumental settings All 1 H-NMR spectra from faecal extracts were obtained on a Bruker Ascend 800 Advance III NMR spectrometer (Bruker, France) on the LISBP metabolomics platform (Meta-Toul) operating at 800. 13 MHz for the 1 H resonance frequency using an inverse detection 5-mm 1 H-13 C-31 P- 15 N cryoprobe (CQPCI) attached to a cryoplatform (preamplifier cooling unit). The 1 H-NMR spectra were acquired at 298K using the Carr-Purcell-Meiboom-Gill (CPMG) spin-echo pulse sequence with pre-saturation and a total spin-echo delay (2nτ) of 100 ms. A total of 32 transients were collected into 64,000 data points using a spectral width of 15 ppm, a relaxation delay of 5 s, and an acquisition time of 2. 72 s.
Data analysis Data were analysed by applying an exponential window function with a 0. 3-Hz line broadening prior to Fourier transformation. The resultant spectra were phased, baseline corrected, and calibrated to TSP (δ 0. 00) manually using Mnova NMR (v9. 0, Mestrelab Research). The spectra were subsequently imported into MatLab (R2014a, MathsWorks, Inc. ). The region containing the water resonance (δ 4. 6-5. 2 ppm) was removed, and the spectra were normalised to the probabilistic quotient  [19]  and aligned using a previously published function  [20]. All data were analysed using full-resolution spectra. Data were mean-centred and scaled using the unit variance scaling prior to analysis using projection on latent structure-discriminant analysis (O-PLS-DA). A first PLS-DA model was build using the mixOmics R package (6.
The quality of the run was checked internally using PhiX, and then, each pair-end sequences were assigned to its sample with the help of the previously integrated index. Each pair-end sequences were assembled using Flash software  [27]  using at least a 10-bp overlap between the forward and reverse sequences, allowing 10% of mismatch. The lack of contamination was checked with a negative control during the PCR (water as template). The quality of the stitching procedure was controlled using four bacterial samples that are run routinely in the sequencing facility in parallel to the current samples.
Doi 10. 5281/zenedo. 15524), yielding to 189,803 clusters (an average of 6400 per sample). Cluster abundances were filtered at 0. 005%  [30]  and/or had to be present at least in three samples, yielding to 332 final clusters (an average of 212 clusters per sample) corresponding to 1,425,084 final valid reads (an average of 44,534 valid reads per sample). One hundred percent of clusters were affiliated to OTU by using a silva123 16S reference database and a taxonomic multi-affiliation procedure (Blast+with equal multi-hits  [31] ). Since rarefaction has shown to result in high rates of false-positive tests for differential abundance, counts were not rarefied  [32]. Richness and diversity indexes of bacterial community, as well as clustering and ordinations, were computed using the Phyloseq package (v 1.
Differentially abundant taxa were identified by characterising the difference between two different diets (multivariate analysis, Kruskall-Wallis non-parametric pairwise comparisons) using LEfSe algorithm with an alpha value of 0. 01 and a threshold on the logarithmic LDA score for discriminative features of 3  [34]  Univariate differential abundance of taxa was also tested using a negative binomial noise model for over dispersion as implemented in the R package DESeq2 (v1. 14. 1,  [32, 35] ). In order to identify taxa altered by haem, LRT method was first applied to select significantly affected taxa across the 4 diets (86 taxa) before applying a pairwise post-test comparison (contrast/Wald test) to sort taxa altered by haem as compared to control diet (39 taxa). In parallel, a 2 × 2 factor design combined with a Wald test was applied in order to identify taxa for which haem effect changed across calcium exposition (interaction term).
On the 33 final taxa corresponding to the interaction term, 12 taxa were selected because the addition of calcium in haem-enriched diet restored their initial observed level in the control diet. Taxa were considered significantly differentially abundant between diets if their adjusted P value was below 0. 01 and if estimated change was log2FC > |1. 5|. Tests were corrected for multiple inferences using the Benjamini-Hochberg method to control the false discovery rate. The sequences used for analysis can be found in the MG-RAST database under the project name "Haem_calcium", with the following accession numbers: mgp89255. Integrative analysis of two or three datasets using rCCA or extended sGCCA respectively Regularised canonical correlations analysis (rCCA)  [36]  or extended sparse generalised canonical analysis (sGCCA named DIABLO)  [37]  was performed using the R package mixOmics (v 6.
1. 1) in order to improve the representation of the links between bacterial taxa, metabolite signatures, physiological traits (metadata), and most importantly, lipoperoxidation status. Both of these supervised multiblock approaches (two or three datasets), for which generic recommended frameworks were applied  [36, 37] , are able to maximise common or correlated information into a single exploratory analysis. Prior to integrative analysis with other datasets, the microbiota dataset was processed according to the multivariate statistical mixMC framework, which included data processing using Total Sum Scaling (TSS) normalisation and log-ratio transformation  [38].