dataesr/scientific-paragraphs-categorization huggingface dataset:
Polarisation and Faraday rotation imaging To analyse the polarisation and Faraday rotation properties of the target, we phase-shifted the calibrated uv-data to the coordinates of the host galaxy (12 h 35 m 01 s. 5, +53 • 17 55 ), which lies almost at the centre of the extended emission. We calibrated the data for short-timescale phase variations caused by the ionosphere, then averaged to 32 s to reduce the data size and to help speed up the subsequent imaging, while avoiding any significant time smearing (e. g. Neld et al. 2018). Both the phase-shifting and time-averaging were done using NDPPP (van Diepen 2011) 3. The imaging software wsclean  (Offringa et al. 2014)   4  was used to create I, Q, U, V channel images at 97. 6 kHz resolution, for a 25 field of view (∼twice the linear size of J1235+5317).
g. Laing & Bridle 2014). Additionally, if the faint collimated emission SE of the host is indeed a jet, then the larger amount of depolarisation towards the SE lobe is opposite to that expected for the Laing-Garrington effect. Models of the variations in RM across radio galaxies in groups and clusters are typically constructed assuming turbulent magnetic field fluctuations over a range of scales embedded in a spherically-symmetric gas halo whose radial density profile is derived from X-ray observations (e. g. Guidetti et al. 2008). For J1235+5317 we do not have X-ray data to constrain the properties of the hot gas environment, although it is likely that the redsequence host galaxy is close to the centre of a poor cluster  (Hao et al. 2010). Therefore, we attempt to estimate the required density and field strength to self-consistently explain the mean RM and depolarisation (e.
Location of large scale structure filaments The catalogue of  Chen et al. (2015 Chen et al. ( , 2016) )  provides a cosmic filament reconstruction from the SDSS data for 130 redshift slices in the range 0. 05 < z < 0. 7. In Figure  6 , we plot the location of the filaments that are in the foreground of J1235+5317 (i. e. at z < 0. 34). There are five filaments identified in different foreground redshift slices that pass through the field. We assign a thickness of 1 Mpc to each filament  (Vazza et al. 2015c ) to determine which filaments most likely intersect lines of sight towards the polarized lobes (Figure  6 ). For a thickness of 1 Mpc, there are four filaments that cover the NW lobe and one filament that covers the SE lobe. Therefore, we estimate that there is an excess of three filaments covering the NW lobe.
Acknowledgements. This paper is based (in part) on data obtained with the  International LOFAR Telescope (ILT)  under project codes  LC2_038  and  LC3_008. LOFAR (van Haarlem et al. 2013) is the  Low Frequency Array designed  and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefitted from the following re-cent major funding sources:  CNRS-INSU, Observatoire de Paris and Université d'Orléans, France ;  BMBF ,  MIWF-NRW, MPG, Germany ;  Science Foundation Ireland (SFI) ,  Department of Business, Enterprise and Innovation (DBEI) , Ireland; NWO, The Netherlands; The  Science and Technology Facilities Council, UK; Ministry of Science and Higher Education, Poland.
SPO and MB acknowledge financial support from the  Deutsche Forschungsgemeinschaft (DFG)  under grant  BR2026/23. Part of this work was carried out on the Dutch national einfrastructure with the support of the SURF Cooperative through grant e-infra 160022 & 160152. The LOFAR software and dedicated reduction packages on https://github. com/apmechev/GRID_LRTwere deployed on the e-infrastructure by the LOFAR e-infragroup, consisting of  J. B. R. Oonk  ( ASTRON & Leiden Observatory ),  A. P. Mechev  ( Leiden Observatory ) and  T. Shimwell  ( ASTRON ) with support from  N. Danezi  (SURFsara) and  C. Schrijvers  (SURFsara). This research has made use of data analysed using the  University of Hertfordshire  high-performance computing facility (http://uhhpc. herts. ac. uk/) and the  LOFAR-UK computing facility  located at the  University of Hertfordshire  and supported by  STFC  [ ST/P000096/1 ].
This research made use of Astropy, a community-developed core Python package for astronomy (Astropy Collaboration et al. 2013) hosted at http://www. astropy. org/, of  Matplotlib (Hunter 2007 ), of  APLpy (Robitaille & Bressert 2012 ), an open-source astronomical plotting package for Python hosted at http://aplpy. github. com/,and of TOPCAT, an interactive graphical viewer and editor for tabular data (Taylor 2005). FV acknowledges financial support from the  ERC   Starting Grant  "MAGCOW", no. 714196 , and the usage of e usage of computational resources on the Piz-Daint supercluster at  CSCS-ETHZ (Lugano, Switzerland)  under project  s701  and  s805. Based on observations made with the Nordic Optical Telescope, operated by the  Nordic Optical Telescope Scientific Association at the Observatorio del Roque de los Muchachos, La Palma, Spain , of the  Instituto de Astrofisica de Canarias.
KEH and JPUF acknowledge support by a  Project Grant  ( 162948-051 ) from  The Icelandic Research Fund. The Cosmic Dawn Center is funded by the  DNRF. RJvW acknowledges support from the  ERC Advanced Investigator programme NewClusters 321271  and the  VIDI research programme  with project number  639. 042. 729 , which is financed by the  Netherlands Organisation for Scientific Research (NWO). HA benefited from grant DAIP # 66/2018  of  Universidad de Guanajuato. KT is partially supported by  JSPS KAKENHI  Grant Number  16H05999  and  17H01110 ,  MEXT KAKENHI  Grant Number  15H05896 , and  Bilateral Joint Research Projects of JSPS. LKM acknowledges support from  Oxford Hintze Centre for Astrophysical Surveys  which is funded through generous support from the  Hintze Family Charitable Foundation.
We quantified these observations using region-of-interest (ROI) analyses. Human data were more reliable per block, and so we collected much more data in macaques, and when necessary subsampled the human data (Fig  2a, b ). ROIs were defined using the same low vs. high and tone vs. noise contrasts. ROI size was varied by selecting the top N% of soundresponsive voxels, rank-ordered by the significance of their response preference for the relevant contrast. We used a standard index to quantify selectivity in independent data: (preferred -nonpreferred) / (preferred + nonpreferred) (Figs  2c-f ; Supplementary Figs 3&4 plot responses for preferred and nonpreferred stimuli separately). Results from an example ROI size (top 5% of sound-responsive) summarize the key findings (Fig  2c, d ): low-frequency and high-frequency selectivity were significant in both species (group-level, ps < 0.
We tested five macaques and six human subjects using a range of sound intensities (from 65 to 80 dB). We focus on data from the two macaques with comparable reliability to data from humans, but results were similar using reliability-matched data from all 5 monkeys (Supplementary Fig 9). Human subjects showed clusters of voxels that responded more strongly to voiced vs. noise-vocoded calls of matched sound intensity (Fig  3e ; Supplementary Fig 10). These clusters had a similar location to the tone-selective voxels identified in Experiment IA. Monkeys also showed voxel clusters that responded preferentially to voicing, and these voxels partially overlapped low-frequency tonotopic fields. ROI analyses confirmed these results in both macaques and all human subjects (ps < 0. 025 for all but the two largest ROI sizes), but revealed that voice-preferring voxels in macaques were less selective than those in humans (Fig 3f, Supplementary Fig 11; ps < 0.
Images of faces, bodies, and vegetables were presented in two scans and color gratings were presented in the third. Visual stimuli were never presented in M3 or in any of the other experiments in this study. Since our results were robust across subjects and experiments, the presence of visual stimuli in those sessions cannot explain our findings. Human MRI scanning-Human data were collected on a 3T Siemens Trio scanner with a 32-channel head coil (at the Athinoula A. Martinos Imaging Center of the McGovern Institute for Brain Research at MIT). Functional volumes were designed to provide good spatial resolution and coverage of auditory cortex. Each functional volume (i. e. a single 3D image) included 15 slices oriented parallel to the superior temporal plane and covering the portion of the temporal lobe superior to and including the superior temporal sulcus (3.
4 s TR, 30 ms TE, 90 degree flip angle; 5 discarded initial acquisitions). Each slice was 4 mm thick with an in-plane resolution of 2. 1 × 2. 1 mm (96 × 96 matrix, 0. 4 mm slice gap). iPAT was used to minimize acquisition time (1 second per acquisition). T1-weighted anatomical images were also collected (1 mm isotropic voxels). Macaque MRI scanning-Monkey data were also collected on a 3T Siemens Trio scanner (at the Massachusetts General Hospital Martinos Imaging center). Images were acquired using a custom-made 4-channel receive coil. Functional volumes were similar to those used in humans but had smaller voxel sizes (1 mm isotropic) and more slices (33 slices). Macaque voxels (1 mm 3 ) were ~17 times smaller human voxels (17. 4 mm 3 ), which helped to compensate for their smaller brains  36.
T1-weighted anatomical images were also collected for each macaque (0. 35 mm isotropic voxels in S1 and S2; 0. 5 mm in S3). Data preprocessing-Human and monkey data were analyzed using the same analysis pipeline and software packages, unless otherwise noted. Functional volumes from each scan were motion corrected by applying FSL's MCFLIRT software to data concatenated across runs (for one scan in M3, we re-shimmed midway through and treated the data before and after re-shimming as coming from separate scans). Human functional data was then aligned to the anatomical images using a fully automated procedure (FLIRT followed by BBRegister)  41, 42. For macaques, we fine-tuned the initial alignment computed by FLIRT by hand, rather than using BBRegister since MION obscures the gray/white-matter boundary upon which BBRegister depends.
Manual fine-tuning was performed separately for every scan. Functional volumes were then resampled to the cortical surface (computed by FreeSurfer) and smoothed to improve SNR (3 mm FWHM kernel in humans; 1 mm FWHM in macaques). Results were similar without smoothing. Human data were aligned on the cortical surface to the FsAverage template brain distributed by FreeSurfer. We interpolated the dense surface mesh to a 2-dimensional grid (1. 5 × 1. 5 mm in humans, and 0. 5 × 0. 5 mm in monkeys) to speed-up surface-based analyses (grid interpolation was performed using flattened surface maps). Macaque surface reconstructions, using Freesurfer, required manual fine-tuning (described by Freesurfer tutorials) that was not necessary for human anatomicals, since the software's automated procedures have been extensively fine-tuned for human data (we also changed the headers of the anatomicals to indicate 1 mm isotropic voxels, thus making the effective brain sizes more similar to human brain sizes).
We used the following HRFs for BOLD and MION: We included the first 10 principal components from white matter voxels as nuisance regressors in the GLM, similar to standard de-noising techniques  21, 43  (using Freesurfer's white-matter segmentation). We found this procedure improved the test-retest reliability of the estimated responses in macaques. White-matter regressors had little effect on the reliability of human responses, but we included them anyway to make the analysis pipelines as similar as possible. Regression analyses were implemented in MATLAB (using pinv. m) so that we could use a custom permutation test, described below. HRF BOLD t = t -2. For each contrast and voxel, we computed a z-statistic by subtracting the relevant regression beta weights and dividing this difference score by its standard error (estimated using ordinary least squares and fixed effects across runs).
For each set of permuted condition orders, we computed voxel-wise significance values using the analysis just described. We then thresholded this voxel-wise significance map (two-sided p < 0. 01) and recorded the size of the largest contiguous cluster that exceeded this threshold. Using this approach, we built up a null distribution for cluster sizes across the 10,000 permutations. To evaluate significance, we counted the fraction of times the cluster sizes for this null distribution exceeded that for each observed cluster based on un-permuted orders. Reliability-matching-We believe that our study is the first to compare the selectivity of brain responses between humans and macaques while matching the data reliability. We used the reliability of responses to sounds relative to silence as a measure of data quality (Fig  2a ).
First, we estimated the beta weight for each condition in each voxel using two, nonoverlapping sets of runs. Second, for each condition we correlated the vector of beta weights across voxels in the superior temporal plane and gyrus for the two datasets. Finally, we averaged the test-retest correlation values across all ten conditions in the experiment. This procedure was performed using different numbers of runs to estimate response reliability as a function of the amount of data. To estimate the test-retest reliability of the entire dataset, which cannot be measured, we applied the Spearman-Brown correction to the split-half reliability of the complete dataset. For each human, we selected the number of runs that best matched the reliability of each monkey (using the curves in Fig  2a ), subject to the constraint of needing at least 2 runs per subject (required for the ROI analyses).
This analysis was performed iteratively, using one run to measure responses and the remaining run(s) to select voxels (cycling through all runs tested). We used standard ordinary least squares (OLS) instead of a permutation test to compute the significance values that were then used to select voxels (both for the sound > silence inclusion threshold and to rank-order voxels by the significance of their response preference for a given contrast). We chose not to use a permutation test because the subsampled human datasets did not have many runs, and thus there were not many condition orders to permute. In addition, because we selected the most significantly responsive voxels for a given contrast (after an initial sound > silence screen), the analysis is less sensitive to the absolute significance value of each voxel.
OLS regression analyses were also implemented in MATLAB. No whitening correction was used since we found that including white-matter regressors substantially whitened the model residuals. Fixed effects was used to pool across runs. We used a standard metric to quantify selectivity ([preferred -nonpreferred] / [preferred + nonpreferred]). This metric is bounded between -1 and 1 for positive-valued responses and is scale-invariant, which is useful because a voxel's overall response magnitude is influenced by non-neural factors (e. g. MION, vascularization). With negative responses, the metric is no longer easily interpretable. We therefore truncated negative values to 0 before applying the selectivity metric. Negative responses were rare, occurring for example in highlyselective tonotopic ROIs for nonpreferred frequencies (see Supplemental Fig 3 which  separately plots responses to low and high-frequency stimuli).
We averaged ROI responses across the N sampled test runs to compute a single bootstrapped sample. For macaques, N was always equal to the total number of runs. For subsampled human datasets, N was equal to the number of runs needed to match the reliability of one of the monkey datasets (as described in Reliabilitymatching above). In cases where N was smaller than the number of runs available, we selected the N-1 localizer runs randomly (if a test run was sampled multiple times, we used the same randomly selected localizer runs). For group analyses, we sampled K subjects with replacement from all K subjects available, and then for each subject, bootstrapped across runs, as described in the previous paragraph. For each sampled human subject, we also randomly sampled a specific monkey whose reliability we sought to match.
A large amount of data was collected: 279 runs in M4 (18 scanning sessions), and 276 runs in M5 across (17 scanning sessions). Each run lasted 8 minutes. Scanning took place over a ~2. 5-month period. Data preprocessing-Data were analyzed using the same pipeline as Experiment IA, with one minor difference: manual alignment was not done separately for each scan. Instead, functional data from all scans (after motion correction within a scan) of a given monkey were aligned to the middle functional scan using FLIRT. The middle functional scan was then aligned to the anatomical scan using FLIRT followed by hand-tuning. We chose this approach because of the large number of scans, and because the scan-to-scan functional alignment was high quality. Data from one scan session (in M5) was discarded because MION was not properly administered which was obvious from inspection of the image.
Another scan (in M4) was discarded because not enough images were acquired per run. As noted above, we analyzed runs in sets of three, with one run per intensity. We excluded five runs (four in M4, one in M5) because we did not complete a full cycle of three runs (e. g. only tested 70 dB but not 75 and 80 dB). Six runs were excluded (three in M4, three in M5) because they were repeated unintentionally (i. e. exactly the same stimuli and stimulus orders as one of the other runs). The run/scan session totals mentioned above are post-exclusion. Reliability-matching-We used human responses to diotic noise from Experiment IA for comparison with the monkey data from this experiment. To compare reliability, we averaged responses across the three intensities to make the dataset comparable to the human dataset where only a single intensity was tested.
Acknowledgements We thank  Galina Gagin  and  Kaitlin Bohon  for their help training and scanning animals M1, M2, and M3; and  Katy Schmidt ,  David Yu ,  Theodros Haile ,  Serena Eastman , and  David Leopold  for help scanning animals M4 and  M5. This work was supported by the  National Institutes of Health  (Grant  EY13455  to N. G. K. and Grant  EY023322  to B. R. C. ), the  McDonnell Foundation   (Scholar Award  to J. H. M. ), the  National Science Foundation  (Grant  1353571  to B. R. C. and  Graduate Research Fellowship  to S. N. H. ), the  NSF Science and Technology Center for Brains, Minds, and Machines  ( CCF-1231216 ), and the  Howard Hughes Medical Institute  ( LSRF Postdoctoral Fellowship  to S. N. H. ). The animal work was performed using resources provided by the  Neurophysiology Imaging Facility Core (NIMH/NINDS/NEI ,  Bethesda MD ), as well as the  Center for Functional Neuroimaging Technologies at MGH  (Grant  P41EB015896 ) and a  P41 Biotechnology Resource grant  supported by the  National Institute of Biomedical Imaging and Bioengineering (MGH).
Analysis of Cell Motility in A Collagen Type I Gel After gelation of a 1. 5 mg/mL bovine collagen type I gel (Advanced Biomatrix, San Diego, CA, USA), the left and right chambers of an Ibidi µ-slide chemotaxis (Ibidi, Martinsried, Germany) were filled with RPMI 1640 medium supplemented with 1% fetal calf serum (FCS, Merck, Darmstadt, Germany). The migration of cells was monitored using a time-lapse microscope (Lumascope LS620, Etaluma, Carlsbad, CA, USA), at 37 • C, in a 5% CO 2 atmosphere, for 24 hours. The series of time-lapse images was analyzed using the ImageJ manual-tracking plug-in and Chemotaxis and Migration tool software (ibidi). Three independent experiments were performed.
Acknowledgments: We thank  Matthieu Piel ,  Aastha Mathur  and  Juan Manuel Garcia Arcos ,  Institut Pierre-Gilles de Gennes, Paris , for helpful discussions. We thank  Yvonne Michel  and  Monica Mörtel  for excellent technical assistance. The lentiviral vector pLenti. PGK. LifeAct-GFP. W was a gift from  Rusty Lansford.
Participants This study is a part of a large prospective population-based dataset conducted in the Cancer Institute of Montpellier (ICM) which is referral centre in France  [23]. Since January 2009, 11 428 visits (n=4362 patients) were screened in our cancer institute to assess their nutritional status. Patients were eligible for inclusion in this cross-sectional study if they 1/ provided from January 2009 to December 2011 the diet history, the ingesta-VVAS score, and the clinical characteristics (i. e. , Eastern Cooperative Oncology Group performance status, height, NRI, modified Glasgow Prognostic Score, current body weight, usual body weight), 2/ came for their first stay in our institute, 3/ were hospitalized for more than 48h, and 4/ scheduled for a chemotherapy treatment. Patients were excluded if they 1/ were hospitalized for surgery, 2/ had stopped oral intake because of a physician's prescription, 3/ received artificial nutrition (enteral or parenteral nutrition) or 4/ received oral nutritional supplements.
Detailed data about food preparation methods, ingredients used in mixed dishes, and the brand name of commercial products were explored. The amounts of each food consumed were estimated in reference to a common size container, standard measuring cups and spoons. The experienced dietician calculated energy intake based on the French food composition tables 1 (in kcal. kg -. day -  ). To avoid any methodological bias, the performance of the ingesta-VVAS was randomized either before or after the dietary 24-hour recall. Percent WL was based on weight reported in the previous 6 months; if missing, the next longest time frame for reported percent WL was substituted where available (i. e. , usual body weight, previous 3 or 2 months, or previous month). Based on international and French definitions before treatment  [24] , the BMI were separated into three classes: low (≤20), intermediate  (21) (22) (23) (24) (25) (26) (27) (28) (29) (30)  and high BMI (>30).
Acknowledgments: The authors are grateful to the dietetic staff for their assistance with patient recruitment toward the end of the study and their support in completing the study:  Stéphanie Arnac ,  Berénice Clavié ,  Laure Francioni , and  Anne Fallières. They also thank  Drs. Vanessa Guillaumon  and  Jean-Pierre Bleuse , and  Pr. Marc Ychou  for their support.
General description of the computations All numerical simulations were performed using the Électricité de France (eDF) in-house open source solver Code Saturne (http://code-saturne. org)  [19]. The code is based on an unstructured collocated finite-volume approach for cells of any shape. It uses a predictor/corrector method with Rhie and Chow interpolation  [20]  and a SIMPLEC algorithm,  [21]  for the pressure correction. For LES, pure second-order central differencing scheme was applied for the velocity components whereas for the temperature a central difference scheme with a slope test was used. Here the slope test determines whether to use a pure central scheme or to use first-order upwinding based on limiting of the overshoots. It is worth mentioning here that the slope test is done locally both in time and space.