dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  A Longhi  and  E Zianni  for technical assistance and Cisbio for collaboration. Acknowledgement is made to the donors of the ADR, a program of the  BrightFocus Foundation , for support of this research. This work was supported by grants from the  European Union 's  Seventh Framework Program  ( FP7 2007-2013 , Grant Agreement no. PIAP-GA-2008-217902 ) (to MdL), from  Fondazione CARIPLO  (project no. 2319-2008 ) (to MdL), from  FIRB-Accordi di Programma  (project code  RBAP11HSZS ) (to MdL), from  PRIN 2010PWNJXK  (to MdL), from  Veronesi Foundation Young Investigator Research Programme 2013  (to EM), from the  Alzheimer's Association  ( NIRP-14-304969 ) (to EM) and an  ADR program  research fellowship (to EM). SC acknowledges support from the  France Alzheimer Association , the  Languedoc Roussillon Region  and the  French Research National Agency ANR  ( ANR-12-BSV4-008-01   ADAMGUARD  project).
We also found proposed new EV types infecting wild NHP that were genetically distinct from all known EVs. Given the potential cross-species transmission of at least some EV types and the increasing contacts between human and NHP, EVs infecting NHP could serve as a source of new emerging human viral pathogens with unpredictable symptomatology. 3D pol regions. In the 59UTR region, a 450 base pairs DNA fragment was amplified with the primer pair UG52/UC53  [40]. RT-PCR products were then used as templates in semi-nested PCR using the PCR forward primer UG52 and a new reverse primer (AS3: 59-ACGGACACCCAAAGTAGTCGGT-39, nt 559 to 538 relative to SV46), designed from a highly conserved region in the alignment of all database available 59UTR sequences of simian EVs and representative human EVs. A portion of the 3D pol region was amplified with primers and reaction conditions previously reported  [38, 41].
Sequencing of amplicons and sequence analysis Specific PCR products were either directly purified with QIAquick PCR Purification kit or gel-isolated and purified using the QIAquick Gel Extraction kit (Qiagen, France) following the manufacturer's protocol. Amplicons were subjected to direct sequencing using the BigDye terminator v3. 1 kit (Applied Biosystems) and an ABI Prism 3140 automated sequencer (Applied Biosystems). They were sequenced in both directions using amplification primers. Partial or complete VP1 capsid coding gene of each virus was pairwise compared with the prototype sequences of all known human and simian EV prototype strains retrieved from databases (http://www. picornaviridae. com/sequences/sequences. htm). Field isolates were considered to belong to a given type when they featured a nt identity $75% and an aa identity $88% with the prototype strain of this type  [46].
Full-length VP1 sequences of viruses that were noticeably divergent from any known EVs (nt identity,75%), were submitted to the Picornaviridae Study Group of the International Committee for the Taxonomy of Viruses for assignment as a new type. Multiple sequence alignments were performed with CLC Main Workbench 5. 7. 2 software (CLC bio, Aarhus, Denmark). Phylograms were inferred both by the distance and maximum likelihood methods implemented in MEGA, version 5. 10  [47]  and PhyML 3. 0  [48]  respectively. Distance based phylogenetic trees were reconstructed by the neighbor-joining method with the Kimura two-parameter method for computing evolutionary distances  [49]. All alignment gaps were removed from the analysis for each Table  1. Global description of the primates samples and overall PCR, isolation and VP1 sequencing results.
identical to those obtained directly from viral RNA extracted from corresponding fecal samples. Overall VP1 amplicons from 12 samples showed very low signal insufficient for sequencing reactions. However, the viruses from 11 of these samples could be sequenced in the 59UTR region while the one from the remaining sample was refractory to 59UTR amplification and was shown to have a divergent 3D pol sequence (GenBank nu KF648607) (Table  S1 ). Apes derived enteroviruses. Of the 21 apes-derived EVs identified, 13 from chimpanzees and 5 from gorillas belonged to virus types previously reported in humans. These known human EVs belonged to the species EV-A (5 EV-A76 and 2 EV-A71), EV-B (2 E-15, 1 E-29 and 1 EV-B82), and EV-C (6 CV-A13 and 1 CV-A24) (Table  2 ). Apart from E-15 and EV-B82, all these EV types have been previously identified among humans in our recent study in Cameroun  [8].
For the first time, EV-A71, which is known to have a high potential to cause severe disease outbreak in humans  [51, 52] , was found in captive NHP-derived samples. Interestingly EV-A76, a known human EV type, was found in wild chimpanzees, in accordance with a recent study reporting EV-A76 in a wild chimpanzee in Cameroon  [34]. Besides the EV types well known in humans, recently discovered virus types as well as proposed new virus types were identified in chimpanzees. The full-length VP1 sequence of the virus Z046 (captive chimpanzee) enabled its unambiguous assignment as an EV-A119, an EV type recently discovered in a human in Cameroon  [6]. The virus CHE20 displayed sequence similarities lower than 75% and 85% with all databases available field and prototype strains at nucleotide (nt) and aminoacid (aa) levels respectively (Table  2 ).
not done; n/a, not applicable. b , Overall 12 VP1 amplicons showed very low signal insufficient for sequencing reactions. However, 11 of the corresponding viruses could be sequenced in the 59UTR region while the remaining one was refractory to 59UTR amplification but showed a divergent 3D pol sequence (GenBank nu KF648605 and KF648607). c , Includes one sample with a mixture of CV-A13 and an EV-A. d , Includes one sample with a mixture of EV-A71 and EV-B82. doi:10. 1371/journal. pntd. 0003052. t001.
Acknowledgments We acknowledge the  Cameroonian Ministries of Health, Environment Forestry , and Research for permission to collect stool samples. We are indebted to  Dr. Cecile Neel  and the staff of PRESICA for samples collection in the wild, the staff of the Mvog-beti zoo and the primates' sanctuary in Mfou for information and assistance during sampling in captive settings. We thank  Cressence Essamba  and  Daniel Kamga  for cell cultures preparation and  Professor Nick J. Knowles  for helpful discussion related to proposed new EV types. We are also indebted to Florence Colbere -Garapin  for the critical reading of this manuscript.
This work was funded by the  International Division of Pasteur Institute  (http://www. pasteur. fr/fr/institut-pasteur/l-organisation/organisation-l-institut/direction/division-international) through the '' Actions Concertees Inter-Pasteuriennes '' ( ACIP SEV N uA/22/2006 ). We are also grateful for the financial support of the  Societede Pathologie Exotique , the  Agence Nationale pour la Recherche  ( ANR 09 MIEN 019 ), the  Fondation pour la Recherche Medicale  ( FRM DMI20091117313 ), the  French Ministry of Foreign and European Affairs  and the  World Health Organization  ( WHO 2012/262176-0 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, 2007). Here we revisit this issue for the comparison of brain structure and, particularly, GM volume between different age groups. The motivation for our study is that image analysis methods are constantly modified and improved, while it is rarely the case that published results are reanalyzed using the new algorithms. VBM relies critically on the segmentation and normalization of the structural images and different algorithms have been implemented to solve these problems. Two of the most frequently used software packages are SPM (Wellcome Trust Centre for Neuroimaging, London, UK) and FSL  (Smith et al. , 2004; Douaud et al. , 2006) , and both offer VBM data processing pipelines. Within SPM two versions are available: one uses the unified segmentation and normalization routines as implemented in standard SPM5/SPM8.
IMAGE PROCESSING We analyzed the data with four different approaches: (1) the unified segmentation/normalization algorithm "SPM8 segment" as implemented in SPM5/SPM8 (subsequently called SPM VBM), (2) DARTEL using the "SPM8 new segment" and normalization procedure implemented in SPM8 (subsequently called DARTEL VBM), (3) tools of the FSL package (subsequently called FSL VBM), and (4) a procedure combining Intensity-Based Segmentation and atlas-to-image Non-Rigid Registration (IBSNRR) with volumetry for predefined ROIs. For each method, we followed the procedures advised by the software developers to generate GM, WM, and CSF segments in individual subject space. For the first three methods we also determined a set of warping parameters defining the transformation from individual subject space to MNI ICBM152 template space.
SPM VBM All SPM analyses were performed with SPM8 (v4667, Wellcome Trust Centre for Neuroimaging, London, UK) run in Matlab Version 7. 6. 0. 324 (R2008a). Segmentation of the images was performed by the unified segmentation routine "SPM8 segment" as implemented in SPM5/SPM8  (Ashburner and Friston, 2005). Briefly, this procedure segments the T1-weighted image in GM, WM, and CSF tissue classes, accomplished by iterative registration to ICBM152 space (i. e. , MNI template space) and segmentation using SPM5/SPM8 standard prior tissue probability maps. It is worth noting, however, that the standard SPM5/SPM8 probability maps were derived based on a sample of young adults only and, thus, might introduce a bias when examining age effects (Figure  1A ). For each participant, the segmentation procedure yields GM, WM, and CSF probability maps in individual subject space as well as deformation fields describing the transformation of the tissue priors to the native subject images.
GM VBM: the influence of segmentation and normalization methods on age specific GM differences Finally, GM probability maps derived from SPM5/SPM8, DARTEL, and FSL segmentation were thresholded at p > 0. 2 and transformed to ICBM152 space by applying the warpings as obtained with SPM5/SPM8, DARTEL and FSL normalization, respectively (resulting in nine normalized GM data sets in total). Additionally, a modulation step was included to keep the total GM amount constant despite local expansion or contraction introduced by image normalization. For each of the 3 segmentation × 3 normalization data sets, a separate second level model was specified in SPM, calculating independent t-tests between young and elderly subjects while GM data were proportionally scaled for ICV. The contrast young > elderly was calculated and thresholded at p uncor < 0.
LIMITATIONS OF THE STUDY In this study we investigated whether different VBM procedures have a significant influence on GM estimates of young vs. elderly subjects. Even though we found substantial differences between methods we cannot infer which VBM method is the most correct one since it is difficult to determine the ground truth for voxel-based methods. Typically this important step for method validation is done by the developers and published when a new method is introduced (see for example  Ashburner and Friston, 2011). However, it is rarely the case that previous datasets are reanalyzed what makes comparison across different studies difficult. Here we report that age-specific GM differences are substantial across methods and should be considered when different findings are compared. This is particularly important when early VBM studies are considered which were mostly performed using SPM5/SPM8.
7± 0. 5%) and D + s → φ π + → K -K + π + (BR of 2. 28 ± 0. 12%)  [38]  and their charge conjugates. Because of the different energies per nucleon of the proton and the lead beams, the nucleon-nucleon centre-of-mass frame was moving with a rapidity |∆y NN | = 0. 465 in the proton beam direction (positive rapidities), leading to the rapidity coverage -0. 96 < y cms < 0. 04. Charged particles were reconstructed and identified with the central barrel detectors located within a 0. 5 T solenoid magnet. Tracks were reconstructed with the Inner Tracking System (ITS) and the Time Projection Chamber (TPC). Particle IDentification (PID) was based on the specific energy loss dE/dx in the TPC gas and on the time of flight from the interaction point to the Time Of Flight (TOF) detector. The analysis was performed by using p-Pb data collected in 2013 with a minimum-bias trigger that required the arrival of bunches from both directions and coincident signals in both scintillator arrays of the V0 detector, covering the regions 2.
02 TeV in each p T interval. The efficiency depends also on the multiplicity of charged particles produced in the collision since the primary vertex resolution, and consequently the resolution of the topological selection variables, improves with increasing multiplicity. This dependence is different for each meson species and p T interval: e. g. the D 0 efficiency in 5 < p T < 8 GeV/c increases by a factor 1. 5 for low multiplicity events until it becomes constant at about 20 reconstructed primary particles. Therefore, the efficiency was calculated by weighting the simulated events according to their charged particle multiplicity in order to reproduce the multiplicity distribution observed in data. The fraction of prompt D mesons, f prompt , was estimated as in Ref. [6]  by using the beauty production cross section from FONLL calculations  [2] , the B → D + X decay kinematics from the EvtGen package  [48]  and the reconstruction and selection efficiency for D mesons from B hadron decays.
Long-term follow-up Two of the authors (G. D. and S. L. H. ) gathered all of the followup information. In the early 2013, a review of all cats, that had successfully completed their weight loss programme and reached target weight, was conducted. First, the referring veterinary practice was contacted to determine whether or not cats were still registered and were still alive. If cats were still alive an attempt was then made to contact the owner to organise a reweighing. If reweighing was not possible, data from last follow-up were used. Two cats had recently developed unrelated illnesses and, as a consequence, their last healthy weight on record was used. Owners were also contacted to discuss current feeding practices. Attempts to contact owners extended over two months, and at least three attempts were made to contact each client.
Data handling and statistical analysis All data are expressed as median (range) except where indicated. Weight regain was quantified by the percentage change from completion weight. Cats were assigned to one of three categories: 'maintained target weight' (M ), if follow-up weight remained within 5 % of completion weight; 'lost further weight' (L) or 'regained weight' (R) when follow-up weight was >5 % below or >5 % above completion weight, respectively  (8). Computer software (Stats Direct version 2. 6. 8; Stats Direct Ltd. ) was used for all tests with the level of significance set at P < 0•05 for two-sided analyses. The Shapiro-Wilk test was used to determine whether data were normally distributed, and parametric or non-parametric tests were then used as appropriate. Simple and multiple logistical regression analysis to determine factors associated with weight regain.
Acknowledgements The authors wish to thank all owners and their cats for participating in the study. The study was funded by  Royal Canin. Royal Canin  manufactured the diets used in the present study. V. B. is an employee of  Royal Canin , while P. J. M. is an employee of  Mars Petcare Inc. Royal Canin  also financially supported the posts of A. J. G. and S. L. H. , at the  University of Liverpool. All of these authors were involved in the study design, in the collection, analysis and interpretation of data, in the writing of the manuscript, and in the decision to submit the manuscript for publication. The study was based upon a project conducted by G. D. , as part of the requirements for the  Degree of Bachelor of Veterinary Science. A. J. G. , P. J. M. and V. B. proposed the research questions; S.
EXPERIMENTAL SETUP In this experiment, we used an escalator mock-up that had four steps of a real escalator tread-board with an approach entrance (1. 5 m) and bilateral hand rails (Figure  1A ). The heights of the steps were 5 cm between the first and second steps, 19 cm between the second and third steps, and 20 cm between the third and fourth steps. The area of each tread-board was 60 (width) × 37 (depth) cm. Screens were placed on the frontal (width and height: 109 × 142 cm) and bilateral sides (135 × 100 cm) as shown in Figure  1A. All visual stimuli were programmed in Matlab (MathWorks, Natick, MA) with Cogent Graphics (University College London, London, UK) software on the Microsoft Windows (Seattle, WA) operating system, and were projected at 60 Hz (refresh rate) by computer projectors (WT610; NEC Viewtechnology).
DATA ANALYSIS For motion data analysis, each marker data point was filtered offline using a fourth-order Butterworth filter (double sided) with a cutoff frequency of 5 Hz and then differentiated to obtain velocity. As indices of postural sway, we used tilt angle (TA) defined as the angle made by the line from C7 connecting the BOS and gravitational line (Figure  1B ). Heel contacts (HCs) on the floor or steps were detected by a heuristic algorithm using heel velocity and positions and confirmed visually in the heel temporal patterns. All behavioral data were temporally aligned at the HC of the third step. To focus on the upper body's TA change as the steps were climbed, which could be affected by the visual stimuli, we calculated the difference between TAs at the third and fifth HCs (dTA), and then defined "dTA-diff " by subtracting the dTA value of the baseline condition (Stairs-view condition in Experiment 1 and NoVM condition in Experiment 2) from the dTA value of the target conditions.
ACKNOWLEDGMENTS We thank  P. Haggard  for improving manuscript,  S. Shimojo ,  T. Kimura , and  N. Abekawa  for constructive discussions, and  M. Kashino  for support and encouragement. A part of the experiments used Cogent Graphics developed by  John Romaya  at the  Laboratory of Neurobiology at the Wellcome Department of Imaging Neuroscience (University College London, UK ).
Background: Risk prediction models are used in critical care for risk stratification, summarising and communicating risk, supporting clinical decision-making and benchmarking performance. However, they require validation before they can be used with confidence, ideally using independently collected data from a different source to that used to develop the model. The aim of this study was to validate the Intensive Care National Audit & Research Centre (ICNARC) model using independently collected data from critical care units in Scotland. Methods: Data were extracted from the Scottish Intensive Care Society Audit Group (SICSAG) database for the years 2007 to 2009. Recoding and mapping of variables was performed, as required, to apply the ICNARC model (2009 recalibration) to the SICSAG data using standard computer algorithms.
The Case Mix Programme is the national clinical audit of adult critical care in England, Wales and Northern Ireland. Risk prediction, using an up-to-date, validated model, is essential to underpin benchmarking and comparative reporting. A head-to-head comparison of the most recent versions of all major critical care risk prediction models using data from the Case Mix Programme demonstrated little difference in performance between the models, but with scope for further improvement  [6]. The Intensive Care National Audit & Research Centre (ICNARC) risk prediction model was therefore developed and validated using data from the Case Mix Programme with the objective of improving on the existing models  [7]. It has subsequently been validated using further data from the Case Mix Programme, including external validation among critical care units that joined the programme after the development of the model  [8]  but it has never undergone validation using independently collected data.
Scotland is a devolved nation of the United Kingdom (UK) and has a very similar health care system to the rest of the UK. However, it has a separate, independent, national clinical audit for adult critical care, coordinated by the Scottish Intensive Care Society Audit Group (SICSAG) through the Information Services Division of NHS National Services Scotland. Our aim, therefore, was to validate the ICNARC risk prediction model using data from adult, general critical care units in Scotland.
Methods The Scottish intensive care society audit group (SICSAG) database SICSAG has maintained a national database of patients admitted to adult, general critical care units in Scotland since 1995. Currently, all adult, general and specialist intensive care and combined intensive care/high dependency units (critical care units) in Scotland participate voluntarily in the audit. Data are collected prospectively using a dedicated software system. Annual data extracts are pooled centrally onto servers at the Information Services Division and validation queries relating to discharges, outcomes, ages and missing treatment information are then issued and fed back to individual units for checking by local and regional audit coordinators. This study was approved by the Privacy Advisory Committee, NHS National Services Scotland (application number 53/10).
Inclusion and exclusion criteria Data were extracted from the SICSAG database for all admissions to all 24 adult, general critical care units in Scotland between 1 January 2007 and 31 December 2009. During the study period, specialist cardiothoracic critical care units were not participating in the national audit; admissions to one specialist neurocritical care unit were not included in the data extract. The following admissions were excluded from the analysis: admissions flagged in the database as 'Exclude from severity of illness scoring'; readmissions of the same patient within the same acute hospital stay; admissions missing the outcome of acute hospital mortality; admissions missing age, location prior to admission or primary reason for admission to the critical care unit; and admissions for whom the primary reason for admission was unable to be mapped onto the ICNARC Coding Method (see below).
The ICNARC model The ICNARC model was developed and validated using data from the ICNARC Case Mix Programme  [7, 8]. Risk predictions are calculated for each admission based on the following predictors: age in years at admission to the critical care unit; location prior to admission to the critical care unit and urgency of surgery; cardiopulmonary resuscitation within 24 hours prior to admission to the critical care unit; ICNARC Physiology Scorean integer score between 0 and 100 based on derangement in 12 physiological parameters during the first 24 hours following admission to the critical care unit; primary reason for admission to the critical care unit; and interactions between the ICNARC Physiology Score and primary reason for admission. The ICNARC model is regularly recalibrated to Case Mix Programme data to ensure accurate, contemporaneous comparative audit for the Case Mix Programme.
The most appropriate recalibration was selected based on the time period of data included in the analysisthis was a recalibration undertaken in 2009 using Case Mix Programme data from 194,892 admissions to 187 critical care units between 1 January 2006 and 31 December 2008. In order to apply the ICNARC model to data from the SICSAG database, certain assumptions and recoding were required, detailed below. After applying this recoding, the predicted risk of acute hospital mortality from the ICNARC model was calculated for each admission using standard algorithms developed for the Case Mix Programme.