dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected, proteases were peculiar of primary colon tumor: ADAM 9, 10, TSL1 and MMP1, 2, 9, 11, and 12 have been found solely in colon tumor  (230)  and not in the metastasis, suggesting their role in the migration process. In another paper, the paired biopsies from tumor and its normal counterpart were obtained from 13 patients. Fifty-six proteins have been identified in the insoluble tissue fraction, after the extraction of lipids and soluble proteins. The digested peptides from ECM fraction were analyzed using a nano-ESI source by means of label-free quantitation approach (e. g. , solely based on measurements of observed peptide ion peak intensities). The obtained data for Beside collagens, other ECM proteins are deregulated in CRC. One study report that MAGP2 (Microfibrial-associated glycoprotein 2), which is ECM component, is upregulated in CRC tissues compared to adjacent tissue, promoting proliferation, migration and invasion of cancer cells and the increase in it promoted malignant phenotypes of CRC cells including proliferation, migration, and invasion.
Results patient and treatment characteristics. One hundred patients were included between 2007 and 2016. The mean follow-up period was 33 months (range 20-64 months). Patients' demographics and clinical data are summarized in Table  1. Mean age was 63 years (SD ± 10 years); sex ratio was 3. 3 men for 1 woman. KPS at baseline was 90-100, 70-80, and < 70 for 30%, 58%, and 12%, respectively. Forty-two percent of patients presented with extra-cranial metastasis. Forty-four percent were symptomatic for their BM. Sixty-seven percent had a solitary BM, 23% had 2 BM, and 10% had 3 or more BM. Mean long axis of BM was 16. 6 mm (± 10. 4 mm). Mean time from simulation and diagnosis MRI to treatment was, respectively, 18. 1 ± 12. 1 days and 27. 7 ± 22. 5 days. SRT was conducted with Cyberknife™. The gross tumor volume (GTV) was outlined on both enhanced contrast CT images and CT-MRI fused images.
These include prescribed radiation dose, lesion size, histology, and lesion morphology (tumor necrosis is associated with a poor response)  17. A review of retrospective studies showed a local control rate > 70% for a BED 12 > 40 Gy (for α/β = 12 Gy)  18. Rodrigues et al. published a predictive score for BM control after SRT, including dose and morphology, based on retrospective data  19. This score should be validated prospectively. In our study, we calculated BED 12 of the SRT schedules: only 1 received less than 40 Gy (treated with 24 Gy in 4 fractions). We did not report BM morphology, but we found that adenocarcinoma histology was associated with lower neurological deterioration (p = 0. 001), as well as solitary BM (p < 0. 001), suggesting that these patients have the best clinical benefit from SRT procedures.
Methodology As a first step, the organising committee, together with the expert coordinators, defined the issues to be addressed and selected the experts in charge of each of them. Questions were formulated in PICO (Patient Intervention, Comparison, Outcome) format. Extensive bibliographic research dating back to 2004 was made via the PubMed and Cochrane databases. To be eligible for inclusion in the analysis, the publications had to be published in English or French according to the 2004 French guidelines and considered by the group of experts as being important. The group schedule is provided in Table  1.
1-In patients at risk of spinal cord injury, it is probably recommended to maintain a systolic blood pressure level > 110 mmHg, before the injury assessment performed, to reduce mortality. (GRADE 2+) STRONG AGREEMENT Rationale Mortality of severe trauma patients, whether traumatic brain injury or not, is inversely correlated with systolic blood pressure (SBP) at hospital admission. An analysis of data from 87,0634 National Trauma databank patients found a 4. 8% increase in severe trauma mortality with each 10 mmHg decrease in SBP at admission from 110 mmHg SBP in severe trauma patients without severe head injury and from 115 mmHg SBP when severe head injury was not excluded from the analysis  [21]. Question 3. 2: In patients with spinal cord injury, what is the minimum level of arterial pressure required during the first week post-trauma to improve the neurological prognosis? R3.
Non-human primate functional MRI (fMRI) is a growing field in neuroscience. However, there is no standardized method for monkey fMRI data analysis, specifically for data preprocessing. The preprocessing of monkey fMRI data is challenged by several technical and experimental specificities of the monkey research such as artifacts related to body movements or to intracranial leads. Here we propose to address these challenges by developing a new versatile pipeline for macaque fMRI preprocessing. We developed a Python module, Pypreclin, to process raw images using state of the art algorithms embedded in a fully automatic pipeline. To evaluate its robustness, we applied Pypreclin to fMRI data acquired at 3T in both awake and anesthetized macaques, with or without iron oxide contrast agent, using single loop or multichannel phased-array coils, combined or not with intracranial implanted electrodes.
We performed both resting-state and auditory evoked fMRI and compared the results of Pypreclin to a previously employed preprocessing pipeline. Pypreclin successfully achieved the registration of the fMRI data to the macaque brain template in all the experimental conditions. Moreover, Pypreclin enables more accurate locations of auditory evoked activations in relation to the gray matter at corrected level in the awake fMRI condition. Finally, using the Primate neuroimaging Data-Exchange open access platform, we could further validate Pypreclin for monkey fMRI images that were acquired at ultra-high fields, from other institutions and using different protocols. Pypreclin is a validated preprocessing tool that adapts to diverse experimental and technical situations of monkey fMRI. Pypreclin code is available on open source data sharing platform.
Animals for the Neurospin dataset Nine rhesus macaques (Macaca mulatta), five males (monkeys B, J, N, T and Y) and four females (monkeys Ji, K, R and S) were included. Animals were 3 to 14 years old and weighed between 4 and 10 kg. Monkeys B, J, Y and K were scanned in the awake condition, whereas monkeys N, T, Ji, K, R and S were scanned under anesthesia (Tables  S1-S3 ). All procedures were conducted in accordance with the European convention for animal care  and the National Institutes of Health's Guide for the Care and Use of Laboratory Animals. Animal studies were approved by the institutional Ethical Committee (CETEA protocols #10-003, #12-086 and #16-040). Monkeys were host individually or by group with a 12:12h light-dark cycle and radio listening on daytime. During the awake sessions, monkeys had water delivered during the training and fMRI sessions  (Uhrig et al.
Experimental design for the 2. 3. 2. Task-evoked functional MRI (tfMRI) A passive auditory task was designed using 3 different conditions (habituation of either low or high frequency sound trials, rare or frequent low or high frequency sound trials). Auditory stimuli were created with 'Audacity software' (version 2. 0. 3 for Mac OS X, http://audacity. source forge. net). Stimuli had two different pass-bands, 0. 5-1 kHz for the low frequency sound trials (total duration of 650 ms) and 8-16 kHz for the high frequency sound trials (total duration of 650 ms), with a sinusoidal envelope. The trials of sounds were separated by a 850 ms inter-stimulus interval for a total trial duration of 1500 ms. The auditory paradigm was presented with first a rest period (14. 4 s, 6 TRs) followed by 5 series of 24 sound trials (36 s, 15 TRs) each followed by a rest period (14.
4 s, 6 TRs). Each 24 trials-series comprises 20 'frequent' trials (either low or high frequent sound trials) and 4 'rare' trials (either low or high frequent sound trials), to avoid saturation of the auditory cortex to either high or low frequencies. The total duration of one run was 266. 4 s (111 TRs). Auditory stimuli were presented with the E-prime software (E-Studio 1. 0, Psychology Software Tools) and MR-compatible headphones (MR Confon, Germany), at a level of 80 dB. In total, we included 3 animals (monkey J, K and R) in this experiment. Functional images were acquired as MION fMRI, using a single loop 1Tx-1Rx coil (TR ¼ 2400 ms, TE ¼ 20 ms, resolution 1. 50 mm isotropic). Data were acquired in awake macaques (52 runs during 3 sessions: monkey J, 24 runs; monkey K, 28 runs) or under anesthesia (76 runs during 3 sessions: monkey K, 32 runs; monkey R, 44 runs) (Tables  S1-S3 ).
Pypreclin fMRI preprocessing We referred as 'Pypreclin' for the new 'Python preclinical' pipeline for monkey fMRI preprocessing. We developed the Pypreclin Python module to process raw images using state of the art algorithms embedded in a fully automatic pipeline. This pipeline aimed at addressing several specificities of monkey research including artifacts related to body movements, the presence of extra cranial tissues, and/or obstacles induced by factors such as intracranial implanted electrodes. This pipeline is made of the following 10 sequential steps (Tables  1  and 2 ). Slice-timing: the slices of one functional volume are acquired at different time points throughout the TR. By applying the slice-timing correction, this shift is compensated allowing the whole brain signal to be assigned to a single time point of the fMRI time series.
In our multishot Echo Planar Imaging (EPI), slice timing was performed with FSL slice timer function (FMRIB's Software Library -FSL, Oxford, U. K)  (Smith et al. , 2004). The actual sequence parameters were selected (e. g. TR ¼ 2400 ms, non-interleaved, increasing order). B 0 inhomogeneities correction: during functional acquisitions, tissue interfaces in the brain cause static field inhomogeneities, which can lead to signal distortion  (Orban et al. , 2003; Pfeuffer et al. , 2007). The geometric distortions can be corrected by estimating a deformation field restricted to the phase-encoding direction between the anatomical and the functional data. In the context of non-human primate imaging, functional data generally display low contrast making this step unstable, and sometimes invalid (nonetheless, Pypreclin proposes a routine to perform such a correction).
Reorientation: it is crucial to accurately report the exact spatial orientation of the monkey's brain (antero-posterior, right-left, cranio-Table  1  NeuroSpin Monkey (NSM) and Pypreclin preprocessing steps. Main differences are highlighted with bold characters or hatched boxes. caudal) and match it to the defined atlas landmarks. This orientation depends on the monkey position during acquisition (e. g. sphinx position, head first etc)  (Vanduffel et al. , 2001). Pypreclin takes this acquisition position into account and eventually reorients images to match the selected template's space orientation Right Antero Superior (RAS)  (Vanduffel et al. , 2001). Realignment: we used a motion correction based on FSL MCFLIRT function (FMRIB's Software Library -FSL, Oxford, U. K)  (Smith et al. , 2004).
We considered the image acquired in the middle of the run as baseline to align the other functional images of the same run. Normalization and masking: in the context of monkey normalization, potential cofounders include the extra cranial tissues (such as chewing muscles) and intracranial implanted devices (such as electrodes) that can disrupt the optimization, thus leading to misaligned images  (Keliris et al. , 2007; In et al. , 2017). To overcome this issue, we use the Joe's Image Program (JIP) -align routine (http://www. nmr. mgh. harvard. edu/~jbm/jip/, Joe Mandeville, Massachusetts General Hospital, Harvard University, MA, USA). This algorithm ignores these muscle tissues by projecting the mask of the template (which only needs to be defined once) back to the monkey's anatomical space.
This process sometimes requires first to achieve a rough initial alignment before automated alignment. The optimization algorithm is driven by the mutual information (MI) cost function  (Evans, 2008). The normalization step aligns (affine) and warps (non-linear alignment using distortion field) the anatomical data into a generic template space. B 1 field correction: low frequency intensity nonuniformities present in the data, also known as bias field, are potential confounding in various analytical tasks, especially during alignment  (Marques et al. , 2010). This artifact can be corrected by multiple softwares (FSL, ANTS, Nipy, BrainVisa), and we selected the ANTS N4 normalization. Coregistration: coregistration refers to the spatial alignment of the functional and the anatomical images. We applied a rigid transformation using JIP align (http://www.
nmr. mgh. harvard. edu/~jbm/jip/, Joe Mandeville, Massachusetts General Hospital, Harvard University, MA, USA) to register the mean functional image (moving image) to the anatomical image (fixed image). To obtain the anatomical brain mask, we warped the template brain mask using the deformation field previously computed during the normalization step. Finally, by composing the normalization and coregistration spatial transformations, we aligned the functional images with the template space. The template brain mask is also warped in the native functional space in order to remove unwanted signals in the time-series  (Fischmeister et al. , 2013). Time series denoising: for the denoising of resting state fMRI data, we recommend using the non-human primate adapted ICA-FIX command (https://github.
com/Washington-University/NHPPipelines) for spatial Independent Component Analysis (ICA, i. e. melodic) followed by the automatic classification of components into 'signal' and 'noise  ' (Autio et al. , 2019). Smoothing: smoothing is generally used in fMRI analysis to improve the signal to noise ratio (SNR)  (Caballero-Gaudes and Reynolds, 2017; Scouten et al. , 2006)  and to compensate for remaining variability between individuals during the group level analysis  (Mikl et al. , 2008). We smoothed the data by applying a default 3. 00 mm gaussian kernel whose size can be adapted to the monkey brain size using the FSL smooth function (FMRIB's Software Library -FSL, Oxford, U. K)  (Smith et al. , 2004). Automatization and Quality Control (QC) report: a basic visual report is generated taking into account alignment accuracy and motion parameters.
This report can further be extended using the QAP Python package (https://github. com/preprocessed-connectomes-project/quality-asse ssment-protocol) that provides measure-of-interest in MRI data quality. Pypreclin also provides a script to automatically parse and process a BIDS (Brain Imaging Data Structure)  (Gorgolewski et al. , 2016)  organized dataset (https://bids. neuroimaging. io), and is built over a smart caching mechanism to speed up the parameters tuning stage. The Pypreclin Python package is available on GitHub (https://github. com/neurospin/ pypreclin), on the Python Package Index (PyPI) for easy installation and upgrading (https://pypi. org/project/pypreclin), and has been made available as a Singularity container (http://biodev. cea. fr/pypreclin/py preclin-ubuntu. simg). Pypreclin pipeline is fully automatic, but it allows for a manual initialization.
Statistical analysis of auditory evoked fMRI The statistical first level analysis was performed with Nistats Python module (https://nistats. github. io/index. html) using a Generalized Linear Model (GLM)  (Abraham et al. , 2014)  and consisted in 3 conditions (habituation of either low or high frequency sound trials, rare and frequent low or high frequency sound trials) for sounds and their associated derivatives. We also added motion regressors as variables of non interest in the model to compute a Z map per run (Fig. S10 ). Activation time was modeled within each fMRI run with the MION hemodynamic response function (HRF)  (Vanduffel et al. , 2001). Beta weight images of activations for the awake and anesthetized condition expressed signal change for all sounds relative to silence periods.
Brain areas labeling The CIVM atlas (template and labels)  (Calabrese et al. , 2015a,b)  was warped to the MNI macaque space  (Frey et al. , 2011). We implemented the CIVM parcellation of 241 ROIs to directly recover brain regions names and accurately localize clusters obtained from volumetric analysis. This step is performed using FSL eyes function (https://zenodo. org/recor d/2630502: McCarthy Paul; 2019 April 5; FSLeyes Version 0. 28. 0).
PRIME-DE dataset To evaluate Pypreclin robustness and rule out a single site effect, we challenged the Pypreclin pipeline with fMRI data collected from 14 other imaging sites in different international institutions, using distinct scanners/magnetic fields/coils/fMRI protocols/macaque species. Institutions    (Milham et al. , 2018). We included data for which both anatomical and functional images were available and free of Data Usage Agreement. We tested the preprocessing pipeline on BOLD or MION fMRI images acquired on two macaque species (macaca mulatta and fascicularis) in the awake state or under anesthesia. Monkeys were scanned at different magnetic field strengths (1. 5, 3. 0, 4. 7 and 7. 0T) using four distinct scanner manufacturers (Brucker, Germany; Philips, Netherlands; Siemens, Germany and undisclosed) and four categories of coils: i) mono channel coil (abbreviated as Mono coil in the figures), ii) multichannel phased-array coil (abbreviated as Multi coil in the figures), iii) clinical body coil (abbreviated as Body coil in the figures) and iv) clinical knee coil (abbreviated as Knee coil in the figures).
6 ). For all the centers, mean nMI coregistration values were: nMI Lyon ¼ 0. 1210 (1. 5T, MION, Awake, Body coil); nMI Princeton ¼ 0. 1219 (3T, BOLD, Anesthetized, Body coil); nMI NKI ¼ 0. 1191 (3T, MION, Awake, Multi coil); nMI NIN ¼ 0. 1195 (3T, BOLD, Anesthetized, Multi coil); nMI UC Davis ¼ 0. 1093 (3T, BOLD, Anesthetized, Multi coil); nMI Newcastle ¼ 01149 (4. 7T, BOLD, Awake, Multi coil). ; nMI ECNU_K ¼ 0. 1221 (3T, BOLD, Anesthetized, Mono coil); nMI UWO ¼ 0. 1120 (7T, BOLD, Anesthetized, Multi coil); nMI MT Sinai_P ¼ 0. 1209 (3T, BOLD, Anesthetized, Body coil); nMI Thus, Pypreclin successfully preprocessed fMRI images in all the 19 experimental conditions for the PRIME-DE dataset (Fig. 7 ).
Role of the macaque template Pypreclin is a flexible tool that allows users to provide any template as an input parameter. Pypreclin successfully preprocessed fMRI data from both the NeuroSpin Center dataset and the PRIME DE database with each of the five tested macaque templates: D99  (Reveley et al. , 2017) , CIVM  (Calabrese et al. , 2015a,b) , NMT  (Seidlitz et al. , 2018) , INIA19  (Rohlfing et al. , 2012)  and MNI  (Frey et al. , 2011)  (Fig. 8 ).
The challenge is even greater when data are acquired in awake behaving macaques or with metallic sources of artifacts such as those generated by intracranial electrodes. Whereas the NSM preprocessing uses FSL bet2 function, no generic settings were found to correctly remove all of the non-brain tissue without cutting any cortical area on the large panel of tested images. Milham et al. , (2018)  proposed a switch from AFNI 3dSkullstrip to the combination of FSL bet2 and AFNI 3dAutomask, according to each dataset. Here we aimed at introducing a single universal approach that does not consider non-brain tissues removal as a segmentation issue. In Pypreclin, we wrap the mask defined on the template using the deformation obtained during the registration procedure. We demonstrated that this method is highly reproducible (Figs.
Computing the mask for each MRI sequence minimizes errors, avoids averaging during statistical analysis and allows to work in the native space (Caballero-Gaudes and Reynolds, 2017) (Fig. S14 ). Coregistration with the template: for experimental conditions where both NSM and Pypreclin preprocessing achieve the coregistration of functional images with the template, sulcus and gyri are better aligned with the atlas for data preprocessed with Pypreclin pipeline. This observation is even more critical for the awake dataset where we expect a more difficult preprocessing due to movements of the body. Zooming on the auditory cortex, the gap observed between the functional image and the template is still more important for images preprocessed with the NSM preprocessing compare to the Pypreclin pipeline and can cause discrepancies in the localization of fMRI activity (Fig.
Versatility and accuracy of the Pypreclin preprocessing pipeline Pypreclin is a Python based preprocessing pipeline. Python is a portable, dynamic, extensible, open source language that allows a modular and object-oriented approach to programming. Thus, Pypreclin could be accessed and broadly made available in an easy manner. Although this pipeline does not use new software or creating algorithms, its strength lies in piling up and ordering the most pertinent functions adapted to the macaque within each step, due to standard tools rather than homemade functions (Tables  1  and 2 ). Furthermore, the fully automatization of Pypreclin aims to decrease time spend on preprocessing, but still allows manual intervention by the user. Another particularity of Pypreclin is to preprocess both BOLD and MION fMRI.
1 and 5,. Since we first developed Pypreclin using our research site monkey fMRI data (Neurospin dataset), we aimed to generalize its competence across international research sites to rule out site effect. The recent development of data sharing in the monkey fMRI field is key in allowing   (Frey et al. , 2011)  is overlaid with an isocolor map on each functional image. quick and easy access to a variety of monkey fMRI data  (Milham et al. , 2018). A good exemple is the validation that we could achieve by successfully challenging Pypreclin on fMRI datasets acquired using different magnetic fields (including ultra-high fields), different hardwares and different protocols. This validation step provides an important piece of evidence for Pypreclin versatility and universality, ruling out any site effect.
Conclusion Pypreclin is a new flexible, versatile and robust Python based pipeline for macaque fMRI preprocessing. The entire code is freely available at GitHub (https://github. com/neurospin/pypreclin). Taking in account the recent move toward data sharing in the non-human primate field  (Milham et al. , 2018) , Pypreclin can contribute to the standardization effort of fMRI preprocessing among the growing macaque neuroimaging community.
Acknowledgments We thank,  Thomas Janssens ,  Marie-France Hang Foucarde ,  Michel Luong ,  Edouard Chazel ,  Eric Giacomini ,  J er emy Bernard ,  Laurent Laribi  ere,  Franck Mauconduit  and the  NeuroSpin MRI  and informatics teams for help with monkey's coils and MRI sequences,  J erôme Cayla ,  Wilfried Piannezzola  and  S ebastien M eriaux  for help within the animal facility,  Timo van Kerkoerle ,  Qi Zhu ,  Vincent Frouin  and  Jean-Robert Deverre  for support. We thank  Joe Mandeville  for his help with the JIP toolbox. We express a special thank to the  Primate neuroimaging Data-Exchange (PRIME-DE) initiative , to the organizers and managers of  PRIME-DE  and to all the institutions that contributed to the  PRIME-DE  dataset (http://fcon_1000. projects. nitrc. org/indi/indiPRIME.
Silhouettes at older ages were not considered because not all women were parous and because of the large range of ages represented in each category. Using a group-based trajectory modelling approach (SAS Proc TRAJ)  18  , we followed existing recommendations  19  to select the model providing the best fit to the data. We successively examined models with one, two, three and four trajectories, after selecting trajectories shapes (quadratic or linear) by examining significance of the model coefficient estimates and the Bayesian Information criterion (BIC) of each model. The BICs of the four models obtained were compared using log Bayes factor, considering the minimum number of women assigned to each trajectory, and examining mean posterior group membership probabilities for each trajectory (expected to be greater than 0.