dataesr/scientific-paragraphs-categorization huggingface dataset:
Then the tracking method was applied for 10 minutes providing convincing registration results even for large deformations and occlusions by the hands of surgeons or when the organ is pushed inside the abdominal cavity. The simulation was completely interactive with a frame rate above 100 FPS thanks to GPU parallelization (see  [29]  for details). Since no intraoperative CT scan was available, we do not have any ground truth associated with this data to evaluate the accuracy of the registration for internal structures. The only metric available is to measure the 2D re-projection error of the outline of the liver and the projected contour of the model. We manually segmented the visible contour of the organ every 100 frame of the video and measured the Hausdorff distance in pixels between the segmented contour and the projected contour of the model.
Instruments Data used in the study derived from students' selfreported answers to 2 instruments (supplementary tables A and B). Learning approaches were measured with the Revised two-factor Study Process Questionnaire (R-SPQ-2F), developed by Biggs et al. [4]. The original version consisted of 20 items scored from 1 (this item is never or only rarely true for me) to 5 (this item is always or almost always true for me), with 2 major subscales: the Deep Approach (DA) and the Surface Approach (SA). The R-SPQ-2F being potentially culturally-sensitive  [28, 29] , the French version presented by Gustin  [30]  was used after a minimal validation to ensure a reasonable application in our context. Exploratory factor analysis with two factors and geomin rotation led us to eliminate items 3 and 7 of SA.
Statistical analysis We considered eligible for the analyses data from students who completed both questionnaires with less than 5% of missing data by dimension. As such, from the returned questionnaires, 1394 were eligible for analyses. The mean scores of DA, SA, and of the five DREEM subscales were computed for each group. Pearson's correlations were calculated between all observed variables. The differences in scores between the three educational contexts were computed with confidence intervals (95% CI) and effect sizes (negligible if <0. 30 and practically important if >0. 70  [34] ).
It tested the direct and indirect (i. e. , mediated via students' perception of their educational context) relationships. The model assumed that the 5 DREEM subscales were intercorrelated with one another as well as DA with SA. This model 2 was fitted in all participants (Model2_All) and 5 times after removing each class from the whole sample in turn (sensitivity analysis, Model2_WOClas) to evaluate the robustness of the model. Only standardized path coefficients were reported. Descriptive analyses were performed in R language (version 3. 3. 0) available at https://cran. r-project. org/. Factorial and path analyses (structural equation modelling) were performed with Mplus (version 7. 11) available at https://www. statmodel. com/. The root mean square error of approximation (RMSEA) and Comparative Fit Index (CFI) were used to appreciate model fit in path analysis  [35, 36].
| Study population and data sources We used data collected from the French Renal Epidemiology and Information Network (REIN) registry. The REIN registry is intended to include all ESRD patients on RRT-either dialysis or transplantation-living in France, including the overseas districts. After beginning in 2002 and progressively expanding, it has included the entire country since 2011 and covers 100% of the ESRD patient population in each region. The details of its organizational principles and quality control have been described elsewhere. 33 Clinical data are recorded when RRT begins and annually thereafter by the attending nephrologist, and their accuracy is verified by REIN research assistants. Events such as death, wait-listing (regardless of donor type, ie, living and/or deceased), and KT are prospectively recorded on occurrence.
34 The SAS MIANALYZE procedure was used to combine the results of the analyses throughout the 20 imputed datasets. To compare transplant and dialysis patients who had similar baseline characteristics, transplant patients were matched to a sample of nontransplant patients (2 dialysis patients for one transplant patient) for age, gender, diabetes, number of cardiovascular comorbidities, year of RRT initiation, active malignancy, mobility status, and time on dialysis. The matching algorithm used was the greedy method. Matching was exact for all the variables cited except for dialysis time, which had to be greater in dialysis patients than in transplant patients. For instance, a patient with preemptive KT was matched with any dialysis patient with the same age, gender, diabetes status, number of cardiovascular comorbidities, year of RRT initiation, active malignancy status, and mobility status.
| Statistical analysis Continuous variables are reported as means (standard deviation, SD) and categorical variables as percentages. The survival benefit of KT was assessed by comparison first with a cohort restricted to patients on the waiting list and then with the matched cohort of dialysis patients, by using Cox regression models with KT as a time-dependent variable. This model accounts for immortal time in transplant patients  35, 36  and deals with the variation of the transplant effect over time. For the cohort of waiting-list patients, follow-up started on the date of either placement on the waiting list or RRT initiation, whichever came later. In the matched cohort of dialysis patients, follow-up started with RRT and Cox regression models were stratified by pairs to take data matching into account.
In both cases, patients had their data censored for end of follow-up period (December 31, 2014) or the last date they were known to be alive, whichever came first. Because mortality risk varies with time since KT, hazard ratios for mortality after KT were estimated at different points: every 3 months in the first-year posttransplant and yearly for the next 2 years. Hazard ratios (HRs) of mortality change after KT were also depicted with cubic spline functions (SAS-Macro RCS). Complementary analyses were performed, stratified by the presence of cardiovascular comorbidities or diabetes. Separate analyses were also done for patients with preemptive and nonpreemptive KT, by censoring the follow-up of the other subgroup at the time of transplantation. To identify risk factors for posttransplant mortality, survival was defined as the time from transplantation to death and analyzed with Cox regression models.
| Mortality risk after KT compared to matched dialysis patients Analysis of the matched datasets showed mortality rates of 17. 3% among transplant patients and 17. 6% among their matched nontransplant counterparts, after a mean post-RRT initiation follow-up period of 41. 6 (SD 26. 7) months (45. 3 [SD 28. 7] and 39. 6 [SD 25. 3] months, respectively). Through month 3, the transplant patients did not have a significantly higher risk of death than did the dialysis patients matched for age, gender, diabetes, number of cardiovascular comorbidities, year of RRT initiation, active malignancy, mobility status, and time on dialysis (Table  3 , Figure  4 ). Although the early mortality risk tended to be lower in transplant patients without cardiovascular comorbidity or diabetes, the association was not statistically significant (Figure  5 ).
We did see a weak signal (Combined P = 1 × 10  An MITF ChipSeq peak also overlies this same region (gray track, GSM1517751). NFIC is expressed in melanocytes, and a second larger MITF peak overlies intron 1 in two ChipSeq experiments viz. GSE50681_MITF, see short solid black bar, and also the tall sharp gray peak below it in GSM1517751. See Supplementary Methods for details Genetic relationships with telomere length and pigmentation. In the GWAS-PW analysis combining melanoma and telomere length (TL) (see Supplementary Methods), there was considerable locus overlap, while by contrast only TERC was detectably shared between nevus count and TL (Supplementary Fig. 41 ). Note that SNPs in OBFC1 were only significantly associated with melanoma in the phase 2 analysis of Law et al. 10  -which are not utilized in the GWAS-PW analysis-although they were suggestively associated (P = 10 -5 ) with nevus count.
Manhattan and Q-Q plots for the nevus GWAS meta-analysis (GWASMA) are shown in Supplementary Fig. 45  and for each of the contributing studies in Supplementary Figs  46 47 48 49 50 51 52 53 54 55. We combined the results from the nevus meta-analysis above with results from stage 1 of a recently published meta-analysis of CM  10. Stage 1 of the CM study consisted of 11 GWAS data sets totaling 12,874 cases and 23,203 controls from Europe, Australia, and the United States; this stage included all six published CM GWAS and five unpublished ones. We do not utilize the results of stage 2 of that study, where a further 3116 CM cases and 3206 controls from three additional data sets were genotyped for the most significantly associated SNP from each region, reaching P < 10 -6 in stage 1. As a result, certain melanoma association peaks are not genome-wide significant in their own right in the present bivariate analyses.
On the other hand the Troponin (385 Å) peak and the M2 and M3 peaks are all much stronger, often by a factor of 2, in patterns from the long sarcomere length rigor than the short rigor. Table  2. Measured values for the intensity, spacing, and cross-meridional widths of the four main meridional peaks visible in the two summed X-ray patterns from Plaice muscles put into rigor at long and short sarcomere length. The spacings were calibrated using the measured position of the M3 reflection above and the peaks were fitted using the program Peakfit. The peak intensities were corrected for width differences in the peaks by multiplying the measured intensity by the FWHM of each peak measured perpendicular to the meridian  [19]. See Appendix A for details and justification of the width correction.
The results are shown in Figure  10  for both rigor samples using both phase sets. The electron density maps were produced using the program Synthseries, updated and adapted for MS Windows by Carlo Knupp, December 2005, from the program FSYNTH, written by John Squire, February 1993, and updated by John Barry, September 1995. Using the two phase sets it is clear that the electron density maps for the two rigor structures are rather different. There is much more mass at the actin positions in the short, full overlap, rigor (SL = 2. 2 µm: Figure  10a, c ) than in the longer rigor (SL = 2. 5 µm; Figure  10b, d ). One might expect this since the overlap of the myosin and actin filaments is less at the longer sarcomere length. But is there a population difference as well? In order to assess this we calculated the amount of electron density within a 90 Å radius of the centre of the actin filaments in the two cases (see  [23, 25] ).
The X-ray chambers were filled with rigor solution without BDM to record the X-ray diffraction patterns. In the first study, X-ray diffraction patterns were taken using a laboratory based Elliot Big Wheel Rotating Anode generator, with a Huxley-Holmes Crystal Monochromator camera, and recorded on photographic film. Three kinds of X-ray diffraction pattern were recorded: one from relaxed muscles at SL = 2. 45 µm, one from muscles put into rigor at the short SL of 2. 2 µm, and one from muscles put into rigor at the longer SL of 2. 5 µm. A total exposure time of 8 h was obtained for each of the three patterns. Recorded patterns on film were digitally scanned using a LeafScan scanner at a resolution of 10 µm per pixel. Using the program FibreFix  [18] , the diffraction patterns were centred and aligned with their meridian vertical and equator horizontal.
This gave a total exposure, after summation of the patterns, of 82. 0 s for SL = 2. 2 µm and 90. 5 s for SL = 2. 5 µm. Patterns were selected that were reasonably straight with respect to the attenuation strip along the equator so that none of the equatorial reflections were lost off the edge of the strip. Using the program FibreFix  [18] , the chosen patterns were corrected for camera background scattering by removing the X-ray pattern taken from the apparatus without a muscle present. The patterns were centred and aligned with their meridian vertical and equator horizontal so that the patterns at each sarcomere length could all be added together to produce two summed patterns, one at SL = 2. 2 µm and one at SL = 2. 5 µm. Quadrant folding was carried out on the summed patterns. Background scatter in the summed patterns was removed using first the 2D roving window fitting algorithm and then the 2D circularly symmetric fitting algorithm in FibreFix.
Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates).
g. ,  Seidlitz et al. , 2018b; Hutchison and Everling, 2012) , and the potential for broad reaching cross-species studies remains unexploited. Perhaps most challenging is the limited availability of data. Here, we introduce the PRIMatE Data Exchange (PRIME-DE) to create an open science resource for the neuroimaging community that will facilitate the mapping of the non-human primate connectome. To accomplish this, we aggregate a combination of anatomical, functional, and diffusion MRI datasets from laboratories throughout the world and make these data available to the scientific community. It merits emphasis that PRIME-DE supports an ongoing process that will remain open to new contributions of data from macaques and other non-human primate species.
Overview At present, PRIME-DE contains 25 collections aggregated across 22 sites; to date, data from 217 primates are included (see Table  1  for information on each institution). Contributions will continue to be accepted and shared on a rolling basis. To promote usage of a standardized data format, we organized all data using the Brain Imaging Data Structure (BIDS) format   S1, S2 , S3, and S4 and detailed on the PRIME-DE website. Across collections, R-fMRI acquisition durations varied from 8 to 155 min per subject. In two collections, subjects were in an awake state. In five collections, subjects were scanned both awake and under anesthesia. One collection scanned 51 post-mortem specimens. In the remaining 17 collections, subjects were scanned under anesthesia. For the three collections with NV-FMRI, acquisition durations varied from 55 to 375 min.
Automated Quality Assessment Consistent with the established policy of INDI, all data contributed to PRIME-DE was made available to users regardless of data quality; users should check data quality before inclusion in their analyses. The rationale of this decision has been the lack of consensus on optimal quality criteria in regards to specific measures or their combinations and cutoffs-a reality that is even more pronounced in non-human primate imaging given the variation in data quality and characteristics across scan protocols. Of note, a benefit of sharing data with differing levels of quality data is also important for those working to develop methods for evaluating, and at times overcoming, such variations. Following the tradition of recent INDI data-sharing consortia, a collection of automated, reference-free quality assurance measures, known as the Preprocessed Connectome Project Quality Assurance Protocol (PCP-QAP;  Shehzad et al.
, 2015) , is being made available with the PRIME-DE datasets. These measures focus on structural and temporal (when appropriate) aspects of the datasets. Table  3  provides a brief description of the measures included, and Figures  1  and 2  depict a subset of QAP results  (Magnotta et al. , 2006; Mortamet et al. , 2009; Giannelli et al. , 2010; Jenkinson et al. , 2002; Friedman et al. , 2006; Nichols, 2012). As would be expected, measures of head motion are notably smaller for sites using anesthetized scan sessions than for awake (NIMH (L), NIMH (M), NKI, Newcastle, Lyon Neuroscience Research Center). Importantly, the measures provided are not intended to be definitive for the field or all encompassing; rather, they are included to spur interest in the potential utility and further development of automated measures.
Nichols, 2012 Global Correlation (GCORR) b M correlation of all combinations of voxels in a time series. Illustrates differences between data due to motion/physiological noise. Larger values reflect a greater degree of spatial correlation between slices, which may be due to head motion or ''signal leakage'' in simultaneous multi-slice acquisitions. -Here, we provide a brief description of the Preprocessed Connectome Project Quality Assessment Protocol. These measures have been computed for all structural MRI (sMRI) and resting-state functional MRI (R-fMRI) datasets in PRIME-DE. The table was adopted from Di  Martino et al. (2017). a For R-fMRI data, these metrics are computed on mean functional data b For R-fMRI, these metrics are computed on time series data. M, mean; GM, gray matter; WM, white matter; SD, standard deviation typically customized, resulting in substantial variation in the quality and characteristics of data collected at different sites.
Additionally, commonly used pre-processing pipelines, used extensively with human neuroimaging datasets, often fail to work properly on non-human primate datasets. As a result, researchers commonly work to optimize individual steps for their datasets outside of traditional workflows, resulting in different pipelines and processing steps across groups. There are efforts underway to form best practices to guide this process and help researchers avoid the need to redefine pipelines themselves (e. g. ,  Seidlitz et al. , 2018b; Love et al. , 2016) ; currently, however, it is still necessary for researchers to do so. Spatial quality metrics include: contrast-to-noise ratio (CNR), smoothness of voxels indexed as full width at half maximum (FWHM), signal-to-noise ratio (SNR), and artifactual voxel detection (Qi1).
, 2016). Data collected in individual macaques can be aligned to these templates using affine and non-linear registration. These templates provide a common anatomical space and coordinate system for specifying specific brain locations and visualizing data collected across days, animals, and laboratories. Of note, some templates link to volumetric digital brain atlases  (Frey et al. , 2011; Reveley et al. , 2017; Seidlitz et al. , 2018b; Saleem and Logothetis, 2012)  derived from analysis of histological tissue  (Saleem and Logothetis, 2012; Paxinos et al. , 1999; Paxinos, 2009). These anatomical parcellations can be warped to individual subjects using standard linear and non-linear registration algorithms (e. g. , AFNI's 3dAllineate and 3dQwarp). Scripts to automate this alignment are available for the single-subject D99 template (https://afni.
nimh. nih. gov/pub/ dist/atlases/macaque) and the recently published National Institute of Mental Health Macaque Template (NMT;  Seidlitz et al. , 2018b ; https://afni. nimh. nih. gov/NMT). The NMT is a high-resolution (0. 25 mm isotropic) T1 template built from in vivo scans of 31 young adult macaques. This volume (and accompanying surfaces) is representative of the adult population and provides anatomical detail akin to that of ex vivo templates, which require days of scanning to acquire. The NMT is available via the PRIME-DE website as well as on GitHub (https://github. com/jms290/ NMT). The database also includes resting-state data from three subjects that have been aligned to the NMT (see NIMH (M) in Table  1 ). A similar multi-subject template also exists for prepubertal rhesus monkeys  (Fox et al.
, 2015) ; additionally, the publically available UNC-Wisconsin Rhesus Macaque Neurode-velopment Database features a longitudinal dataset that can be used to provide insights into age-related changes in structure  (Young et al. , 2017). Other anatomical parcellations have been defined on the surface using the single-subject F99 template (available in Caret;  Van Essen et al. , 2012) , which can be used for analysis on the cortical sheet. For example, the cortical parcellation from Markov et al. (  2014 ) includes quantitative tract-tracing connectivity estimates for a subset of these regions. Improving Skull Extraction, Segmentation, and Registration A high-quality T1 image with isotropic voxels is important for skull extraction. There are a number of brain extraction algorithms and available tools, e.
g. , the Brain Extraction Tool (BET in FSL;  Smith, 2002) , 3dSkullStrip in AFNI  (Cox, 1996) , the Hybrid Watershed Algorithm  (HWA in FreeSurfer; (Segonne et al. , 2004) , BSE in BrainSuite  (Shattuck and Leahy, 2002) , Robust Brain Extraction (ROBEX;  Iglesias et al. , 2011) , Primatologist toolbox  (Balbastre et al. , 2017) , and ANTs  (Avants et al. , 2011). Most of these tools can be effectively applied to human data; however, the performance is suboptimal and variable in NHP due to the differences in brain structure (e. g. , size, adipose tissue, olfactory bulb) and the quality of the T1 image (SNR, inhomogeneous intensity). Accordingly, the parameters and/or related atlas library need to be customized to optimize the brain extraction in NHP. For example, in AFNI, the program ''3dSkullStrip'' with alternative options ''-monkey,'' ''-marmoset,'' and ''-surface_coil'' is available for brain extraction in NHP.
Population brain templates, such as the NMT, can further improve and automate the registration and brain extraction process  (Seidlitz et al. , 2018b). Standard segmentation algorithms can separate gray versus white matter, but if the signal is not homogeneous, which is typically the case at higher magnetic fields, segmentation in some parts of the brain will be better than others (especially subcortically). Registration of T2 datasets to T1 structural scans also remains a challenge. Affine or non-linear registration algorithms can work well provided that intermediate scans are available. For instance, a full brain T1 structural scan from the same individual obtained along with T2 images (also with as much coverage of the brain as possible) could be crucial for registering T2 datasets to any of the freely available monkey template brains that are registered to macaque atlases.
One way to reduce or eliminate the manual intervention during brain extraction and tissue segmentation-using only the typically acquired T1 scan-is to rely on priors defined on a highresolution and high-contrast template. The multi-subject NMT includes manually refined masks of the brain, cortical gray matter, and various tissue types (including blood vasculature;  Seidlitz et al. , 2018b). Applying the inverse anatomical alignment transformations to the NMT brain mask produces an approximate single-subject mask for brain extraction. A more precise individual brain mask and tissue segmentation can be obtained using the NMT's representative brain and tissue segmentation masks as priors. The NMT distribution includes scripts that use AFNI and ANTs to perform these mask refinements (as well as morphological analysis).