dataesr/scientific-paragraphs-categorization huggingface dataset:
e. proximal bowel close to the diaphragm) with the specific intention of challenging the capabilities of the respective algorithms with the effects of respiration. We also assessed the displacement distance of the adjusted ROIs to the manual gold standard to identify ROIs that may have been mis-registered to adjacent bowel loops etc. This comparison is a good test for registration as it is based directly on displacements reflecting registration accuracy and has not previously been performed in other small bowel motility validation studies. On average less manual correction was necessary in the DRAM data and when ROIs were adjusted, the median distance and variance was several times lower than that without RDDR pre-processing. By collectively assessing these two components of registration fidelity in a challenging region of bowel, we subjected both DRAM and OF algorithms to a robust test and in both cases we found DRAM performed better in comparison to the ground truth and comparable to existing literature values derived using BH OF.
A potential limitation in this study is the absence of simulated small bowel datasets to characterise the decomposition process. However, we chose not to include a simulation experiment for the assessment of the method's performance instead focusing on clinical datasets. The rationale for this choice comes from the extreme difficulty to simulate realistic peristaltic motion (in the small bowel especially). Directly using the deformation field resulting from OF registration is possible. However, this would not include changes absorbed in the algorithm intensity correction field which may lead to a different output in RPCA compared to real clinical data. An important component of our investigation was the application of our methodology to colonic data sets. Physiologically, the colon is quite different from the small bowel with a less frequent contraction rate  (Scott 2003).
However the aim in this preliminary work was to demonstrate the broad applicability of the unaltered DRAM technique to the colon, an organ with different physiological characteristics to the small bowel and using data acquired at a different MRI field strength and over a different number of time frames. Future work will involve the development of improved metrics to summarise colonic motility and assess sensitivity of MR as a modality to investigate this organ. Finally, from a practical perspective, Magnetic Resonance Enterography is an increasingly popular method for the investigation of disease in the abdomen. MRE does not utilise ionising radiation and is non-invasive with ingestion of Mannitol being largely well-tolerated  (Stange et al 2006 , Horsthuis et al 2009). Methods for the analysis of the large quantities of dynamic data are important components of the translation of this non-invasive physiological imaging technique into clinical practice.
Acknowledgement This work was undertaken at  UCLH/UCL  who received a proportion of funding from the  Department of Health's NIHR Biomedical Research Centre 's funding scheme grant. The views expressed in this publication are those of the authors and not necessarily those of the Department of Health. The work undertaken at the  University of Nottingham  was from an investigator-initiated research project, funded by  Norgine Limited. The authors would like to thank  Professor Robin Spiller  and  Dr Luca Marciani  for their contribution to the  University of Nottingham  study. The authors have no conflicts of interest to declare.
9, 10  It is also a commonly quoted reason for patient reluctance to accept insulin  11  or intensify their insulin regimen. 12, 13  The definition of hypoglycaemia in clinical trials is based on symptoms (including severity), blood glucose levels and the timing of the event. In symptomatic events, the patient displays one or more symptoms of hypoglycaemia; in confirmed events, a 'low' blood glucose measurement is documented; and severe events usually require 'third-party assistance' with or without blood glucose measurements and prompt recovery on countermeasures. 14  In addition, hypoglycaemic events can be classified as daytime or nocturnal events. Reporting of data on hypoglycaemia across randomised controlled trials investigating the use of basal insulin analogues in type 2 diabetes has not been standardised, with various definitions and blood glucose cutoffs utilised within the literature.
Statistical and Analytical Procedures Baseline and week 24 patient-level data for patients treated with IG+MET, IG+SU and IG+MET+SU were pooled and analysed from all patients who were randomised and who subsequently received ≥1 dose of IG and the OADs as defined for the subpopulation of interest. Continuous efficacy endpoints of HbA 1c , dose and weight at endpoint were analysed using generalised linear models with adjustments for OAD treatment group, study, duration of diabetes, body mass index (BMI), age and baseline value. For the outcome of achievement of HbA 1c <7. 0 % at endpoint, odds ratios (ORs) for pairs of OAD treatment groups were derived from a logistic model, adjusting for the same factors as in the continuous model. All hypoglycaemic events experienced up to and including 1 day after the end of the treatment period (day of last dose or week 24, whichever came first) were considered for analysis.
Standardised protocol-driven titration regimens and treatment targets were used in the studies included in the analysis and, in addition, the analysis only considered IG administered at bedtime. Derived data were also standardised, allowing consistent endpoint definitions to be applied across the studies. Furthermore, the patient-level analysis allowed for the inclusion of patient-level baseline characteristics as covariates. The analysis is, however, limited by the fact that only five IG+MET, six IG+SU and nine IG+MET+SU treat-to-target studies were evaluated from which relevant patient-level data were available and that the findings are limited to IG and therefore not applicable to other basal insulins. Although the post hoc nature of the analysis can be considered as a limitation, the analyses were conducted using a pre-specified statistical analysis plan.
b Also included in the analysis of IG+SU studies. c Also included in the analysis of IG+MET+SU studies. DET = insulin detemir; GLI = glimepiride; LIS = insulin lispro; NPH = neutral protamine hagedorn (intermediate-acting insulin); OAD = oral antidiabetic drugs; PIO = pioglitazone; ROS = rosiglitazone; SITA = sitagliptin; TZD = thiazolidinedione. 1. Aschner P, et al. , Lancet, 2012;379:2262-9; 2. Meneghini LF, et al. , Endocr Pract, 2010;16:588-99; 3. HOE901_4022 data on file [NCT00046462]. Available at: http://clinicaltrials. gov/ct2/show/NCT00046462 (accessed 25 March 2013); 4. Swinnen SG, et al. , Diabetes Care, 2010;33:1176-8; 5. Gerstein HC, et al. , Diabet Med, 2006;23:736-42; 6. Fritsche A, et al. , Ann Intern Med, 2003;138:952-9; 7. Standl E, et al. , Diabetes Care, 2005;28:419-20; 8.
Eliaschewitz FG, et al. , Arch Med Res, 2006;37:495-501; 9. Riddle MC, et al. , Diabetes Care, 2003;26:3080-86; 10. Rosenstock J, et al. , Diabetes Care, 2006;29:554-9; 11. HOE901_4021 data on file [NCT01336751. Available at: http://clinicaltrials. gov/ct2/show/NCT01336751 (accessed 25 March 2013); 12. Janka HU, et al. , Diabetes Care, 2005;28:254-9; 13. Bretzel RG, et al. , Lancet, 2008;371:1073-84; 14. Yki-Järvinen H, et al. , Diabetes Care, 2007;30:1364-9; 15. Blicklé JF, et al. , Diabetes Obes Metab,.
In shallow ground waters at Montmiral, water-rock interactions were assessed to determine that the observed reactions were caused by water in contact with a biogenic CO 2 soil reservoir. Lack of interaction with gaseous CO 2 of a deep origin was also confirmed by the absence of a 18 O shift towards more negative values as observed for example in the neighbouring Massif Centrale. Accurate interpretation of the data from these baseline ecosystem studies will need to take into account other information including weather (such as temperature, precipitation and wind) and any other factors which might also impact on the health of the ecosystem. Repeat baseline studies may need to be undertaken over a period of several years depending on regulatory demands and the seasonal variability at the site itself.
Acknowledgements The Guide has greatly benefitted from the support, advice and review of a number of people within the RISCS project for which the authors are very grateful. In particular, Camilla Skriung of ZERO and Samuela Vercelli, of URS, have both been instrumental in ensuring the report meets with the requirements of the intended readership. Further advice and review was provided by the following, who the authors gratefully acknowledge: David Hilditch of the Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC), Lee Spangler of Montana State University, Tim Dixon of IEAGHG, Malcolm Wilson of University of Regina, Matt Baggaley and Tim Hill of E. ON New Build and Technology, Ines Koehler of Vattenfall, Alv-Arne Grimstad of Sintef, Thomas Thielemann of RWE, Tore Torp and Aina Janbu of Statoil.
The Guide has also been reviewed by a range of external stakeholders including representatives from relevant industries, environmental non-governmental organisations (NGOs) and some regulatory bodies, who the authors gratefully acknowledge. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 240837 and from industry partners ENEL I&I, Statoil, Vattenfall AB, E. ON and RWE.
Systematic review All published studies that reported the derivation or update of clinical prediction rules for group A streptococcal pharyngitis were eligible for inclusion. 0] [21] [22] [23]  All studies that provided data specific to children were eligible. For clinical prediction rules updated by their original authors, only the updated study was included. We excluded clinical prediction rules that were explicitly intended for use in lowresource settings (i. e. , those derived in developing countries explicitly intended for use in settings where rapid antigen detection tests are not routinely available). We systematically searched MEDLINE and Embase for studies using the search strategy described in Appendix 1 (available at www. cmaj. ca/lookup/suppl/doi:10. 1503/cmaj. 140772 /-/DC1). We screened the titles and abstracts of the identified studies; if they were considered potentially relevant (or in cases of uncertainty), we evaluated the full text.
We extracted a prespecified set of data related to study setting and design, and the characteris-tics of patients and clinical prediction rules. We assessed the quality of included studies using a combination of items from methodological standards for clinical prediction rules  [19] [20] [21] [22]  and clinical prediction models,  [24] [25] [26]  and from QUADAS-2  27  (Appendix 2, available at www. cmaj. ca/lookup /suppl/doi:10. 1503/cmaj. 140772 /-/DC1). If published reports referred to previous papers, we extracted the corresponding items. 6] [17] [18]  One reviewer (JFC) performed the study selection, data extraction, and assessment of study quality and level of evidence, which were then checked by a second reviewer (MC). Discrepancies were discussed and resolved by consensus.
A further limitation lies in the absence of an a priori sample size calculation. One of the clinical prediction rules met our target zone of accuracy based on the point estimates alone (Attia's rule),  43  but it was considered insufficient because the boundaries of the confidence intervals for sensitivity and specificity went across the prespecified limits for significance. This could be due to lack of power, and our results should be considered with caution until they are confirmed with a larger sample of patients. Finally, although this prospective study was originally designed for the purpose of validating existing clinical prediction rules, clinical data were collected before performing the systematic review of the literature. The collection of the clinical variables required for validation was not customized to evaluate the full range of rules we finally included.
Acknowledgments: This article is dedicated to the memory of  Professor Edouard Bingen  ( 1946-2012 ),  Department of Microbiology, Robert Debré Hospital and Paris Diderot University. The authors thank  A. Liboz ,  Dr. M. Boucherat ,  Dr. F. de La Rocque ,  I. Ramay ,  D. Menguy  and  M. Fernandes , and the physician investigators who participated in the study:  Dr. F. Corrard ,  Dr. P. Deberdt ,  Dr. A. Elbez ,  Dr. M. Goldrey ,  Dr. J. Gosselin ,  Dr. M. Koskas ,  Dr. P. Martin ,  Dr. A. S. Michot ,  Dr. N. D'Ovidio ,  Dr. D. Qutob ,  Dr. C. Romain ,  Dr. O. Romain ,  Dr. C. Schlemmer  and  Dr. A. Wollner. The authors also thank  R. Spijker  for his assistance on the literature search. Data sharing: No additional data are available.
Funding: Jérémie Cohen is a doctoral student supported by educational grants from Agence Régionale de Santé d'Ile-de-France, Laboratoires Guigoz -Société Française de Pédiatrie -Groupe de Pédiatrie Générale -Groupe de Recherches Epidémiologiques en Pédiatrie and research grants from the French Ministry of Health (Régional 2012, 12089) and Assistance Publique-Hôpitaux de Paris (Année Médaille de l'Internat 2013). The clinical study for external validation was internally funded by the Association Clinique et Thérapeutique Infantile du Val-de-Marne and externally by Dectrapharm, manufacturer of the rapid antigen detection test. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.
Background: Corrupted gradient directions (GD) in diffusion weighted images may seriously affect reliability of diffusion tensor imaging (DTI)-based comparisons at the group level. In the present study we employed a quality control (QC) algorithm to eliminate corrupted gradient directions from DTI data. We then assessed effects of this procedure on comparisons between Huntington disease (HD) subjects and controls at the group level. Methods: Sixty-one HD patients in early stages and forty matched healthy controls were studied in a longitudinal design (baseline and two follow-ups at three time points over 15 months), in a multicenter setting with similar acquisition protocols on four different MR scanners at four European study sites. A QC algorithm was used to identify corrupted GD in DTI data sets.
However, the effect of corrupted volumes within DTI data sets on the results of comparisons at the group level has not been investigated yet. The aim of this study was therefore to examine if the application of QC to DTI data, in combination with strict elimination of corrupted GD, results in differences in group comparisons of DTI-based metrics compared to using no QC. To this end, we used data from patients with Huntington disease (HD) who are prone to involuntary movement during a DTI scan. We used data from the PADDINGTON study (Pharmacodynamic Approaches to Demonstration of Disease-modification in Huntington's disease by SEN0014196), a multi-centre study with a total sample size of initially 101 data sets. In addition, participants underwent MRI three times 6 months apart, which allows validation of the results, i.
In the present study, those structures were of particular interest that had been identified in previous DTI studies in HD: an increase of FA values in the basal ganglia, and FA reductions in the external and internal capsule, in parts of the thalamus, and in subcortical white matter  [10] [11] [12]. The present study addresses the investigation of the impact of QC on DTI data with the exemplary application to HD. We investigated whether identification and elimination of corrupted GD from cross-sectional data sets would lead to changes in cross-sectional result patterns. Therefore, the novelty of this study is to examine if and to which extent disturbances during DTI acquisition had an effect on DTI-based metrics at the group level. The main question was whether between-group differences are reliable with or without application of QC, i.
Subject scanning Data in this study were collected in the framework of the European PADDINGTON project at four study sites in Europe (Leiden, the Netherlands; London, UK; Paris, France; Ulm, Germany)  [13]. The PADDINGTON study is an international initiative that aims to provide pharmacodynamic approaches for disease-modifying clinical trials. Work package 2 of this project entails the collection of 3. 0 Tesla MRI (volumetric and DTI) scans acquired using similar acquisition protocols from patients with HD in an early disease stage and from healthy control participants, with the objective of identifying biomarkers of disease progression. The study was conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonisation guideline on Good Clinical Practices and applicable local regulatory requirements and laws.
All participants were ambulatory and agreed to volunteer for MRI scanning after giving written informed consent. All HD patients had a genetically confirmed diagnosis with a trinucleotide (cytosineadenine-guanine) repeat length of 36 or higher, and had clinical features of mild HD at stage I based on the Unified Huntington's Disease Rating Scale (UHDRS) with a Total Functional Capacity (TFC) score of 11-13. In total, 61 HD and 40 control subjects were scanned at visit 1, 56 HD and 39 control subjects were scanned at visit 2 (6 months after baseline), and 55 HD and 37 control subjects were scanned at visit 3 (15 months after baseline). Acquisition parameters for the different sites were similar with slight variations of the standardized acquisition protocol. DTI was performed with echo planar sequences, where each data volume consisted of 52 to 76 axial slices of 2.
0 mm or 2. 2 mm thickness (depending on the scanner of the different sites, whole brain coverage was guaranteed), with no inter-slice gaps, and an acquisition matrix of between 112 ×112 to 128 × 128 with inplane resolution of 2. 0 × 2. 0 mm 2 , or 2. 2 × 2. 2 mm 2 , respectively. TR ranged between 8 s and 13 s, and TE ranged between 56 ms and 86 ms. Each DTI data set consisted of more than 40 b = 1000 s/mm 2 , and one or more b = 0 scans. More detailed acquisition parameters for the different sites have already been reported previously  [12].
Diffusion tensor imaging and data analysis overview A DTI scan consists of a number of gradient encoding volumes, e. g. some b = 0 scans as well as a number of scans with different diffusion encoding gradients  [14]. Diffusion tensor calculation results in an over-determined equation system and further parameterization for quantification of the diffusion anisotropy is the fractional anisotropy (FA)  [15]. FA, a dimensionless scalar measuring the diffusion directionality in a single voxel, was used as the DTI-based metric for this study. In a general simplification, any measured MR signal is a combination of the true quantity, acquisition system noise, environmental noise, and subject specific noise. Ideally, the quality of the measured signal (i. e. the signal to noise ratio: SNR) can be improved by signal accumulation  [16].
If applied to DTI data, the repeated recording of diffusion encoding volumes should lead to an improvement of the accurateness of the diffusion tensor and the FA value in each voxel  [17, 18]. Thus, if some of those repeated gradient direction (GD) images were corrupted by subject movement or by other sources of noise, the integration of these corrupted GD images into the tensor calculation (or FA values, respectively) would bias the results. Contrariwise, the removal of noisy or corrupted volumes from tensor calculation (or FA values, respectively) could reduce this bias  [6]. The in-house DTI analysis software Tensor Imaging and Fibre Tracking (TIFT;  [19, 20] ) was used for post-processing and statistical analysis. Figure  1  shows a schematic overview of data processing and analysis, divided into the iterative templatespecific normalization to the Montreal Neurological Institute (MNI) stereotactic frame  [21]  with and without QC, respectively (Figure  1A ), and the scheme for statistical Analyses were performed with or without quality control (QC) and subsequent gradient direction elimination.
Subsequently, in an iterative procedure, normalized DTI-data I 1 were obtained by non-linear normalization to the previously defined templates (T 1 ). From these newly normalized DTI-data I 1 , new templates (T 2 ) were derived which again could be used for normalization. This iterative process is stopped when a predefined coincidence (measure by correlation) between DTI-data and templates was reached. (B) Scheme for whole brain-based spatial statistics: FAmaps are calculated from normalized DTI data and a smoothing filter to the individual normalized FAmaps is applied. In a consecutive step, voxelwise statistical comparison between the patient groups and the corresponding control group is performed. Final steps are correction for multiple comparisons using the false-discovery-rate (FDR) algorithm and a clustering procedure for further reduction of type I and type II errors.
Impact of QC-based elimination of corrupted GD The impact of QC-based elimination of corrupted GD was investigated in two ways: 1) The possible impact of corrupted GD on FA-maps was analysed by ROI analysis in FA maps prior to MNI normalization for a single visit with and without QC based elimination of GD. ROIs at the identical anatomical position of other visits could act as a reference. This analysis was performed by the following procedure: (i) DTI data of a single HD subject were selected in which two visits showed no corrupted GD and one visit showed a series of corrupted GD. (ii) A difference map was calculated from FA maps of the visit with corrupted GD by simple subtraction of the FA maps with and without GD elimination. (iii) In a data-driven approach, a ROI was placed in a brain region where differences in FA were detected by the difference map.
(iv) An experienced operator identified the anatomical ROI position in the FA maps of the other visits without the knowledge of FA values. (v) Mean FA values of all visits in the respective ROIs were compared in the contrast with vs. without QC-based GD elimination. 2) Using cross-sectional whole brain comparison the impact of corrupted GDs on FA maps was assessed in HD participants and controls. Corrupted GDs may impact FA maps in two ways. (i) FA values differ (Figure  1A  and Figure  2 ) and (ii) MNI Registration of the subject's FA image to the study-specific group template (which are based on study specific FA templates calculated from single subject FA maps) also could show differences, i. e. the MNI normalization process of all DTI data sets might also be influenced by differences in the FA maps of individual participants (Figure  1B ).
Furthermore, between-group differences in cross-sectional data were investigated. The result patterns were compared to cross-sectional comparisons of DTI data sets from two further visits. Thus, each time point of the longitudinal data has been used as a single cross-sectional test point. This way, longitudinal data per-subject was used as a scan-rescan reproducibility test with the inherent assumption that the effect of progression of the disease over the time scale of the study on the diffusion images is negligible in first order, and hence the group-wise differences should only slightly proceed for the three time points.