dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors wish to thank the patients who participated in this study, and the contributing staff at the study sites. Additional investigators were  Franz Brunnhuber  ( King's College Hospital, Neurology, London, United Kingdom), Hans JA Carpay (Tergooiziekenhuizen, Blaricum, The Netherlands ),  Philippe Derambure  ( Hôpital Roger Salengro, Neurologie, Lille, France ),  Christian Elger  ( Univ. Klinik f€ ur Epileptologie, Bonn, Germany ),  Gerard Hageman  ( Medisch Spectrum Twente, Neurologie, Enschede, The Netherlands ),  Edouard Hirsch  ( Hôpitaux Universitaires de Strasbourg, Service d'exploration des epilepsies, Strasbourg, France ),  Hrisimir Kostov  ( Rikshospitalet University Hospital-Dept. of Neurodiagnostics National Centre for epilepsy, Sandvika, Norway), Henk van Lambalgen (SEIN, Zwolle, The Netherlands ),  Benjamin Legros  ( Hôpital Erasme -Neurologie, Anderlecht, Belgium ),  Hans Lindsten  ( Umea University Hospital, Department of Neurology, Umea, Sweden ),  Thomas Mayer  ( Kleinwachau S€ achsisches Epilepsiezentrum Radeberg, Radeberg, Germany ),  Andrew McEvoy  ( The National Hospital for Neurology and Neurosurgery, London, United Kingdom ),  Uwe Runge  ( Klinik der Ernst-Moritz-Arndt-Universit€ at, Neurologische Klinik, Greifswald, Germany ),  Bernhard Steinhoff  ( Epilepsiezentrum Kork, Epilepsieklinik f€ ur Erwachsene, Kehl-Kork, Germany ), and  Arielle Crespel  ( CHU Gui de Chauliac, Neurology, Montpellier, France ).
The growing awareness of the importance of host genetic makeup in infectious disease outcome has motivated large-scale investigations of the human genome, made possible by recent technological advances. Namely, sequencing of the human genome  [13] , the International HapMap project  [14] , and microarray-based high-throughput genotyping technology have paved the way to Genome Wide Association Studies (GWAS) of major infectious diseases. In these GWAS, millions of single nucleotide polymorphisms (SNPs) can be tested for association with major infectious diseases, and this can be done simultaneously in thousands of individuals (for review, see  [5] ). Results emanating from these large datasets are certainly improving our understanding of infectious disease pathogenesis. However, full interpretation of the genes and pathways identified by GWAS studies is complicated by several factors including the modest effect size of most signals and the fact that even together these signals can explain only a fraction of the genetic predisposition to disease.
A current survey of the Mutagenix database (http://mutagenetix. utsouthwestern. edu/home. cfm) which contains the largest collection of ENU-induced phenotypic mutations (N = 185)  [58] , revealed that 61% were missense mutations, 19% nonsense alleles, 18% splicing defects, and 2% were frame-shift mutations. Therefore, while targeted mutations producing null alleles are necessary for genetic dissection of phenotypic traits, ENU-induced point mutations can also be used in parallel, revealing the multiple functions of a gene by altering individual protein domains and splicing products. Further, point mutations can produce various types of allelic series: (1) hypermorphic or hypomorphic alleles (increased or reduced activity of the gene product, respectively); (2) antimorphic alleles (the gene product is antagonistic to the wild-type allele); or (3) neomorphic alleles (new molecular function)  [59]  which can display a broad range of possible phenotypes.
Instead, inexpensive alternatives have been favored in order to sequence mouse mutations, namely targeting approaches using minimal mapping data. Moreover, targeted sequencing of coding regions of the genomes, or exomes, are particularly relevant for large mutational collections and have become the standard in cases where high-throughput gene mutation discovery methods are needed  [64] [65] [66]. We describe below some of the standard techniques for sequencing and analysis of de novo mutations generated within ENU mouse models in a rapid and unbiased fashion. Currently, the most widely used commercial mouse exome capture panels (Agilent and NimbleGen) target approximately 37 Mb of the sequences contained within the consensus coding sequence (CCDS) database of the genome, as well as other genomic features (e.
Fast, memory-efficient BWT-based aligners, such as BWA  [73] , are often used in NGS studies. However, these aligners tend to be less sensitive than recent hash-based aligners, such as Novoalign  [74] , which conversely tend to require more computational resources  [75]. Numerous software packages such as GATK  [69] , samtools  [76] , and Picard  [77]  have been developed to attempt to correct for biases incorporated at the sequencing and alignment phases, thus improving variant detection (Figure  3 ). During library construction and sequencing, duplicated DNA fragments produced by polymerase chain reaction (PCR) amplification and optical duplicates can occur. Software package such as Picard markDup and Samtools rmdup remove or flag potential PCR duplicates if both mates (in the case of paired-end reads) contain the same 5' alignment positions.
The use of functional annotation programs such as snpEff  [81]  and VEP  [82] , coupled with the exclusion of known variants (for example, on the basis of SNP data from the dbSNP database  [83] ) and of variants falling below acceptable quality metrics (QUAL, genotype quality (GQ), strand bias, etc. ), can help to preferentially identify protein coding mutations. However, despite rigorous post-alignment refinement and variant exclusion criteria, recurrent false positive SNVs remain. By comparing a set of ENU samples to unrelated genome or exome sequencing data sets, as well as to mouse genomes data from the Sanger Institute  [68]  generated using the same analysis workflow, variants commonly shared between related strains or systematic false positives arising from mapping issues related to genome structure (e.
The phenodeviant offsprings that exhibited clinical signs of disease or succumbed to the infection were considered susceptible. Using this strategy, we revealed eleven deviant pedigrees. One of these led to the identification of a premature stop codon (L3X) in the Ptprc gene, which encodes the leukocyte common antigen CD45. Ptprc L3X mutant mice showed reduced numbers of CD3 + T and mature follicular B cells, suggesting defects in T and B cell development  [209]. In this report, we also demonstrated that CD4 + Th1 cells, by producing IFNȖ, help CD8 + T cell recruitment to prevent the dissemination of HSV-1 into the central nervous system, thus protecting mice from lethal HSV-1 infection. Altogether, our data point to CD45 as the first host component involved in the adaptive immune response that directly contributes to susceptibility to HSV-1 and HSE pathology.
Acknowledgments This work was support by a  Team grant  ( Danielle Malo ,  Philippe Gros ,  Silvia M. Vidal ) from the  Canadian Institutes of Health Research  ( CTP-87520 ). Grégory Caignard is supported by the  Canadian Institutes of Health Research. Megan M. Eva  is supported by the  McGill University Health Centre. Silvia M. Vidal  is supported by the  Canada Research Chairs Program. We thank  Eve-Marie Gendron-Pontbriand  for proofreading the manuscript.
At present, registration-based quantification of bowel motility from dynamic MRI is limited to breath-hold studies. Here we validate a dual-registration technique robust to respiratory motion for the assessment of small bowel and colonic motility. Small bowel datasets were acquired in breath-hold and freebreathing in 20 healthy individuals. A pre-processing step using an iterative registration of the low rank component of the data was applied to remove respiratory motion from the free breathing data. Motility was then quantified with an existing optic-flow (OF) based registration technique to form a dual-stage approach, termed Dual Registration of Abdominal Motion (DRAM). The benefit of respiratory motion correction was assessed by (1) assessing the fidelity of automatically propagated segmental regions of interest (ROIs) in the small bowel and colon and (2) comparing parametric motility maps to a breath-hold ground truth.
This advance is of particular interest to associated applications where respiratory motion is a limitation. The current study does not involve the use of RDDR in the presence of contrast enhancement. Instead we propose a new application of this technique to free-breathing Magnetic Resonance Enterography (MRE) datasets, prior to small bowel motility quantification. We show that RDDR can be used as a preprocessing step to filter out respiratory motion, with no effect on measurements of peristalsis. Previous work has demonstrated that it is possible to extract quantitative information related to bowel physiology from dynamic 'cine' MRI images and several methods have been proposed  (Ailiani et al 2009 , Marciani 2011 , Odille et al 2012 , Sprengers et al 2012 , Farghal et al 2012 , Bickelhaupt et al 2013).
2. RPCA settings. The starting value of λ is chosen such that the rank of L in the first iteration of RDDR is the number of time frames divided by four. This initial value of λ was empirically found to be high enough to include some elements of respiratory motion in L and low enough to keep peristalsis in the sparse component  (Hamy et al 2014). For lower values of λ, respiratory motion might entirely appear in the sparse component making the first iteration useless. The starting value of λ is logarithmically incremented in subsequent iterations similar to the original version of the algorithm. The same settings were used for all the datasets (both small bowel and colon) analysed in this study. RDDR was initially developed for DCE-MRI registration, the stopping criterion of the algorithm is modified here to make it suitable for the current application.
This criterion is now designed so that no peristalsis would appear in the registered low rank components. We use a threshold on the sparsity of the RPCA sparse component to end the iterations. Given the pseudo-periodical characteristic of respiratory motion and peristalsis, the optimum threshold for λ was chosen using an analysis of test data in the frequency domain, inspired by previous work from  Sprengers et al (2012). The frequency of peristalsis is expected to be the same in both breath hold and free breathing. Thus the difference between breath hold and free breathing data in the Fourier domain should show only the contribution of respiratory motion. We use such a difference as an indicator of the effect of each iteration in RDDR. Spectral powers were computed by summing the Fourier transform of time-intensity variations for every pixel over the entire field of view.
Study overview In this study motility is quantified in dynamic small bowel and colonic data with the OF registration algorithm-originally designed to process data in breath-hold alone. We evaluate here the ability of a pre-processing registration step RDDR to correct free breathing motion before OF processing with the combination of the two processes here referred to as DRAM. We provide two main results that focus on (1) the ability of OF-alone and DRAM to faithfully propagate a line ROI (i. e. a 1D line drawn across the bowel lumen, perpendicular to the central axis of the bowel) through processed small bowel and colonic time series data using the average of two independent manually propagated ROIs as a gold standard. (2) Free breathing parametric motility maps in small bowel data sets registered with OF-alone and DRAM, using breath hold OF data as a gold standard.
Subject population Two data sources were used for the validation of this technique. The first was from a prospective study of small bowel motility in 20 volunteers  (Menys et al 2013) , and the second in a study of colonic motility in 6 volunteers. Small bowel and colonic data sets were acquired under ethical approval from the respective institutions' Research Ethics Committees. 2. 4. 1. Small bowel. Twenty small bowel subjects were scanned (mean age 28, range 22-48, 14 Male). Volunteers were included according to the study inclusion criteria where they were able to give informed consent and were non-smokers and had abstained from caffeinated and alcoholic drinks on the day of the scan. Volunteers were excluded where they had chronic intestinal disease or were on long term medication excluding the oral contraceptive pill.
Volunteers were also excluded if they reported abnormal GI (gastrointestinal) symptoms or had a history of GI surgery. Volunteers were recruited prospectively by advertisement and interview. 2. 4. 2. Colon. Colon data sets for 6 healthy volunteers were used in this study with subject demographics as follows, mean age 27, range 19-43 years, 1 Male. Volunteers were included in the study where they were able to give informed written consent, were non-smokers and had abstained from alcohol for 24 h prior to the study day. Volunteers were excluded if they had any history of serious acute or chronic illness, especially gastro-intestinal, if they regularly used medication which interfered with GI function or had previous GI surgery (excluding appendectomy). Volunteers were recruited prospectively by advertisement.
The study coordinator (AM) positioned the volume that best displayed the small bowel in the coronal plane deep to the abdominal musculature as guided by the planning sequence. The motility sequence was run first on inspiration breath-hold to collect a total of 20 images of the same anatomical slice. This process was repeated following a 10 s recovery period with the subject this time instructed to 'gently free-breathe' whilst a total of 60 images were acquired in the same anatomical position unchanged from the breath-hold scan. Phys. Med. Biol. 59 (2014) 4603 2. 5. 2. Colon. The volunteers arrived after an overnight fast. Scans were carried out at baseline and at hourly intervals following consumption of either 1 L or 2 L Polyethlene glycol (PEG) formulation. A total of nine data sets from six subjects are included in this work to highlight differing amounts of colonic motility and breathing effects, selected subjectively by the study scientist (CH).
All volunteers underwent a baseline scan and then hourly scans after ingestion of PEG. The data in this study comprises of two baseline scans and seven scans at various time points post ingestion. Subjects lay in the supine position and were scanned using a Philips Achieva 1. 5T MRI scanner using the XL-Torso receiver coil. The colon motility scan consisted of a single slice bTFE sequence positioned in the sagittal plane through the ascending colon (sagittal plane, voxel size 1. 5 × 1. 5 × 15 mm 3 , FOV 330 × 228 × 15 mm 3 , FA 70°, TE = 1. 5 ms, TR = 3. 0 ms). Temporal resolution was 1 slice per second and scans were acquired during 2 min of thoracic free breathing.
Assessment of the effect of registration The effect of respiratory motion correction using RDDR was assessed by investigating the fidelity of the optic flow algorithm to propagate a line ROI through the (1) small bowel and (2) colon free-breathing time series data. 2. 6. 1. Small bowel. One gastroenterology research fellow and one research scientist (JM-3 years experience MRE, AM-4 years experience small bowel MR) identified, in consensus, a small bowel loop in the upper left quadrant of each subject from one of the six anatomical positions acquired, which remained visible through the time series (i. e. did not move out of plane). 2. 6. 2. Colon. This process was repeated in the colon data sets, where the same two observers placed in consensus two line ROIs in the ascending portion of the colon.
Assessment of registration accuracy. In both the small bowel and colonic data, the line ROIs were automatically propagated through the time series by both OF alone and the OF component of DRAM based on the registration deformation fields, and the results saved. The ROI was then manually corrected independently by both JM and AM for each time point. Agreement between readers was assessed using Bland-Altman limits of agreement and Intraclass correlation (ICC). An ICC of 0 represents no agreement between observations and 1, perfect agreement. The manually corrected line ROIs for the two observers were averaged for each time point and used to create a ground truth for each data set. Accuracy of the OF-alone and DRAM algorithms to the ground truth was compared by: (1) assessing change in line length over time in combined 'ground-truth' scores and automatically propagated ROIs using Bland-Altman limits of agreement (LoA) and intra-class correlation (ICC).
Validation of motility scoring The small bowel was delineated with a polygonal ROI in three data sets for each subject: (1) the breath-hold registered with OF (2) the free breathing registered with DRAM data sets and (3) the free-breathing registered with OF-alone. The 20 s BH data (i. e. without respiratory motion) served as a ground truth. The ROI for each 2D series was automatically propagated through the time series using deformation fields derived from the OF registration (with and without pre-processing with RDDR). A parametric map was generated by calculating the standard deviation of the Jacobian determinant J of the displacement field, through time, providing a previously validated quantitative surrogate measure of motility expressed in arbitrary units. For each pixel: σ σ = = ( ) x y x y t ( , ) {J( , , ) } t N J [0, ] (3) Where x and y are the spatial coordinates,t is time and N is the number of frames.
Registration accuracy assessment. In the small bowel, two observers manually propagated a linear region of interest through 60 time points in each of the 20 subjects. Inter-reader variability was assessed through Bland-Altman LoA and ICC. For the manually corrected OF-alone data, mean difference between readers was 0. 4 mm (95% LoA ± 7. 3 mm). ICC was 0. 85. For the manually corrected DRAM data the mean difference between readers was 0. 54 mm, LoA ± 3. 4 mm. ICC was 0. 96. The Bland-Altman analysis of line length ROIs in OF-alone registered and DRAM registered data with the manual measurements (mean of two observers) is shown in figures 6(a) and (b). For the OF-alone registered data the mean difference between the manually corrected and automatically propagated ROIs was -2. 0 mm (95% LoA ± 9 mm).
For the DRAM processed images mean difference was -0. 48 mm (95% LoA ± 4. 15 mm). In the colon datasets, the mean difference between readers for the manually corrected OF-alone data was 0. 2 mm (95% LoA ± 1. 1 mm). ICC was 0. 98. For the manually corrected DRAM data the mean difference between readers was 0. 28 mm, LoA ± 1. 7 mm. ICC was 0. 99. The Bland-Altman analysis of line length ROIs in OF-alone registered and DRAM registered data with the manual measurements (mean of two observers) is shown in figures 7(a) and (b). For the OF-alone registered data the mean difference between the manually corrected and automatically propagated ROIs was -1. 25 mm (95% LoA ± 7. 57 mm). For the DRAM processed images mean difference was -0. 13 mm (95% LoA ± 1. 96 mm).
Motility scoring The mean global motility score 5 within the manually placed ROIs for the BH data sets across the cohort was 0. 340 (range 0. 181-0. 422). Mean global motility score for DRAM registered data was 0. 335 (range 0. 189-0. 430) and OF alone free-breathing data sets was 0. 365 (range 0. 268-0. 458). Subjective visualisation of motility colormaps is shown in figure  9  with data summarised in figure  10.
Discussion The aim of the work was to validate a two-stage technique that first corrects respiratory motion before applying an existing OF method to register local deformation generated due to peristalsis. Such an approach could allow rapid and robust data analysis from longer datasets acquired in free breathing. The key feature of our technique is the application of RPCA to decompose the dynamic series into low rank and sparse components within an iterative framework, the aim being to register out respiration from the low rank component. The proposed two-stage approach can be considered as a pre-processing followed by a modelling step using different types of registration. It was chosen to use a previously validated metric which required to remove the confounding effects of respiration prior to analysis.
This was achieved using RDDR. Such a technique is intended to remove corrupting motion effects (i. e. respiration) and has proved successful in reducing misalignment in dynamic contrast enhanced MRI  (Hamy et al 2013). The modelling using OF provides a representation of peristaltic motion as a series of vector fields that can be summarized using the standard deviation of the Jacobian determinant metric. An alternative to the proposed method could include a removal the slow varying component from a single deformation field computed using a single registration step. However, this possibility was not explored in this study. Within our scheme, parameters were selected empirically once and the same values were used for all datasets providing results that appear generalizable to both the colon and small bowel.
Investigating the effect of varying the parameters in RDDR would be of interest in future work. An iterative scheme was used within RDDR to remove the respiratory component of motion. The risk of losing physiological information related to peristalsis was reduced by modifying the original version of the algorithm to impose a specific stopping criterion. Although the stopping criterion was determined from one example data set, the parameter settings were fixed for all subjects in both the small bowel and colon data sets. The performance of DRAM across these data sets suggests that no further adjustment would be required in another cohort. To quantify the effect of the respiratory motion correction, the motility metric was compared between the pseudo-ground truth breath-hold and the free breathing DRAM data over global small bowel ROIs.
We demonstrated comparable results using free breathing DRAM data and the pseudoground truth of the BH. Specifically the breath-hold OF registration gave comparable global scores to DRAM and a positive bias in OF-alone registered global motility scores in free breathing datasets was observed. The decrease in registration accuracy was supported by the manual adjustment experiment 3. 1. 1. This supports our conclusion that DRAM removes respiratory motion whilst leaving peristaltic motion largely intact. The breath-hold data was not a perfect ground truth as the data was temporally separated from the subsequent free breathing data collection and we identify this as a study limitation. However the 30 s time difference from the commencement of the breath-hold to the commencement of the free-breathing series is unlikely to impact significantly on a summary measure of bowel motion especially when assessed in a global manner.
We do acknowledge that regional differences may be present when visualising the motility maps however this likely corresponds to intrinsic bowel motility variation over time. We assessed the accuracy of the registration technique by comparing algorithm-propagated ROIs through the time series data and comparing their size and position to a manually adjusted ground truth. Assessment of the DRAM corrected data demonstrated greater registration accuracy with a mean error comparable to previous values in breath-hold data by Odille and Bikelhaupt  (Odille et al 2012 , Bickelhaupt et al 2014). The DRAM data did however show a slightly larger variance in the Bland-Altman LoA when compared to the original Odille data using breath-hold data. This is likely due to several factors, principally the choice of ROI position which in the current study was the upper left quadrant (i.