dataesr/scientific-paragraphs-categorization huggingface dataset:
Relationships between AFO Types and Gait Pattern In children with CP, 3DGA data collected when wearing their prescribed orthosis were also evaluated. In New Zealand, all orthoses are custom-molded to the shape of the foot with posting as needed to align the forefoot and hind foot neutrally and set the floor to shank angle to neutral. The posting on the Ankle Foot Orthosis (AFO is on the outside of the AFO, i. e. , the forefoot and hind foot are set as close to neutral as possible and the floor to shank angle is also set as close to neutral as possible. The exterior of the AFO is posted to correct any residual misalignment. Orthoses prescribed in this cohort were solid AFOs, ground reaction AFO (GRAFO), posterior leaf spring (PLS) AFOs, hinged AFOs, supra-malleolar orthosis (SMOs), and sport dynamic FOs.
Data-Driven Model The sensitivity analysis for the k-means clustering algorithm indicated that the optimal number of clusters for our dataset was six distinct groups (Figure  3B ). Group 0 (n = 50) was composed of the limbs of typically developing children, while Groups 1 to 5 consisted exclusively of the limbs of children with CP (refer to supplementary material for details). The GVS distribution for each gait feature across the self-organizing map is displayed in Figure  3A. For each score, the SOM algorithm grouped together the limbs of children with CP with high scores away from limbs with low scores.
Data-Driven Model The sensitivity analysis for the k-means clustering algorithm indicated that the optimal number of clusters for our dataset was six distinct groups (Figure  3B ). Group 0 (n = 50) was composed of the limbs of typically developing children, while Groups 1 to 5 consisted exclusively of the limbs of children with CP (refer to supplementary material for details). The GVS distribution for each gait feature across the self-organizing map is displayed in Figure  3A. For each score, the SOM algorithm grouped together the limbs of children with CP with high scores away from limbs with low scores. The Levene test of homogeneity of variance and normality test indicated that population variances in each group were equal and normally distributed. The one-way ANOVA showed a statistically significant difference between groups for each dependent variable (GVS score).
Discussion The first objective of this study was to classify children with CP according to their gait biomechanics using a data-driven approach based on their GVS. We developed a self-organizing map (SOM), an unsupervised data-driven model, which provided a simple two-dimensional (2D) representation of the data (Figure  1 ) while retaining the topological properties of the dataset. This approach helped to understand how high-dimensional data are linked together by reducing the data  Light grey bars represent the average GPS difference (average ∆GPS = sum (∆GPS)/#limbs) and dark grey bars represent the average GPS improvement (Average +∆GPS = sum (+∆GPS)/#limbs with improved GPS), i. e. , when improved GPS limbs were taken into consideration. ∆GPS = (GPS barefoot -GPS orthosis), +∆GPS is when the difference was positive (improvement OF GPS with AFO), NP: not prescribed to the group, GRAFO: ground reaction AFO, PLS AFO: posterior leaf spring AFO, SMO: supra malleolar orthosis, #: number.
Discussion The first objective of this study was to classify children with CP according to their gait biomechanics using a data-driven approach based on their GVS. We developed a self-organizing map (SOM), an unsupervised data-driven model, which provided a simple two-dimensional (2D) representation of the data (Figure  1 ) while retaining the topological properties of the dataset. This approach helped to understand how high-dimensional data are linked together by reducing the data dimensions to a 2D map. Principal component analysis (PCA) is another data dimension reduction algorithm that explains the maximum amount of variation in a dataset, which is well suited to identifying the dimensions that account for the largest variability. However, this algorithm will not correctly capture pathologies where the sample number is low.
We then tried to understand which type of AFO would improve the overall GPS for the limbs classified in each group. Future work will concentrate on validating the model based on 3DGA obtained from prospective patients with the help of physiotherapists and orthopedic surgeons. The relatively small sample size for our study was the main study limitation. We included data from gait analysis performed in one clinic to reduce differences in data collection protocol. We also disregarded multiple visits to avoid pseudo-replication. We also did not include clinical examination measures of children with CP such as lower limb joints' range of motion and strength. The main reason for not including these measures as input for our model was the qualitative assessment of these measurements, which called their reliability across patients into question.
3DGA is a quantitative measurement of gait with high reproducibility and reliability in adults and children  [24, 25]. Another limitation was the decision to treat each leg independently; this approach did not consider the influence of inter-limb kinematics on gait pattern. Consequently, orthotic prescription was evaluated on a limb-by-limb basis, which did not consider the influence of orthotic prescription for one limb on the kinematics of the other limb in the same patient. It is also important to note that posterior leaf spring (PLS) AFOs were prescribed to 50% of the total limbs of children with CP in this study. While this cohort of retrospective clinical data was randomly selected using inclusion and exclusion criteria, it appears that PLS AFOs were prescribed more frequently in this cohort.
Micro-CT Analyses Analyses of bone microarchitecture were performed using a Skyscan 1076 in vivo micro-CT scanner (Skyscan, Kontich, Belgium). Tests were performed after euthanizing the mice for each group. All heads were scanned using the same parameters (pixel size 9 µm, 50 kV, 0. 5-mm Al filter, 20 min of scanning). The reconstruction was carried out using NRecon, and analyses were made using CTAn, CTVox and Dataviewer softwares (Skyscan).
Statistical Analysis The data obtained were analyzed using the IBM SPSS 20. 0®program (SPSS. Inc. , Chicago IL, USA). Descriptive statistics were used using means and standard deviations for quantitative variables (Hertwig epithelial sheath cell proliferation and positive TRAP cell numbers) and absolute frequencies for qualitative variables (expression pattern in apical papillae and HERS cells, intensities of P21 and PCNA protein labeling). In order to compare Hertwig epithelial sheath cell proliferation between different genotypes and their respective ages, an ANOVA test and Tukey's multiple range (post hoc) test were used. A level of significance of 5% was always considered.
g. , the health workers or some other key actors of our societies, the epidemic keeps spreading, stabilizes at a much slower rate than what one would expect, and finally affects a significant fraction of the population. In this paper we shall use the SEIR model rather than the simpler SIR model because of the significant period of incubation in the Covid-19 disease. The SEIR model has various properties that can be understood, at least for the order of magnitude of its effects, using simple objects like the well known basic reproduction ratio R 0. However, it also has very nonlinear features which are defying common sense and require rigorous mathematical analysis. In the accompanying numerical examples, we take values for the parameters which are compatible with the data collected during the pandemic of Covid-  19.
Incorporating these considerations in compartmental models will anyway require further studies. So far it remains difficult to choose a set of parameters, although one can hope that a better understanding of the dynamics of the pandemic will emerge out of the various studies that are currently done. What we learn after the end of the lockdown is still to be studied. Now comes a very empirical observation, in the framework of SEIR models, with parameters that are supposed to be constant in time, a crude assumption that is definitely not valid after the end of the lockdown. This empirical observation is the starting point for this paper. Situations with a basic reproduction ratio R 0 < 1 exhibit an exponential decay of the number of cases while the data of  [43]  (at the beginning of the lockdown) were emblematic of a situation with R 0 > 1.
Acknowledgements. This work has been partially supported by the Project  EFI  ( ANR-17-CE40-0030 ) of the  French National Research Agency (ANR). The authors thank  Nicolas Bacaër  for a stimulating discussion which was the starting point of this research project,  Gilles Zérah  for pointing them an important reference, and an anonymous referee for pointing them a missing argument and suggesting several improvements. The authors also thank the MODCOV19 platform for encouragements and collective effort.
Choice a set of parameters It is not the purpose of this paper to discuss the values of the parameters in a SEIR model of the pandemic of Covid-19 and we shall simply choose values that can be found in the literature, for a purely illustrative purpose. However, for the interested reader, we list our sources and some entries in the rapidly growing literature on the topic. Concerning the initial data for the SEIR model, we shall assume that the size of the French population is N = 67. 8 × 10 6. The following values correspond to the situation for the Covid-19 in France on March 15, 2020, according to  [5]  and data released on a daily basis by SPF at  [43]. We shall take as initial data the values Here we provide the formulae used in  [5]  to infer the numbers, which are based on an asymptotic analysis of the SEIR model during the initial phase of the epidemics (without lockdown).
Acknowledgments We thank  Peter Czuppon  ( Collège de France & Sorbonne Université ),  François Blanquart  ( Collège de France & INSERM UMR1137 ) and  Alan S. Perelson  ( Los Alamos National Laboratory ) for critical reading and expertise on the analysis. The  study received nancial support of the national research agency (ANR)  through the  ANR-Flash calls  for  COVID-19  (TheraCoV  ANR-20-COVI-0018 ) and also of the  Bill & Melinda Gates Foundation  under grant agreement  INV-017335.
The third most recent development is in the software for semi-automatic quantification, which allows calculation of relative lobar perfusion. In light of these new developments, a reevaluation of DECT perfusion imaging as a qualitative and quantitative surrogate marker for lung perfusion is needed. The purpose of this study was to compare the quantitative and qualitative lung perfusion data acquired with a dual-layer DECT to that acquired with a SPECT-CT large field-of-view CZT camera.
6 frequency cutoff. Lung CT at 120 kVp was performed for 3D anatomic reference. Images were reconstructed with an iterative algorithm (ASiR ® ; GE Healthcare) with a 40% factor (ASIRv40%). Perfusion images and CT images were reconstructed using semi-automatic lung segmentation software (Q. Lung ® ; GE Healthcare) and stored for quantitative analysis. Overlay between perfusion and CT images was created for the purpose of qualitative analysis.
Background: Despite several studies focusing on the negative aspects of general medicine, the speciality seems attractive for students. Researchers from the European General Practice Research Network created a group to study job satisfaction in general practice. The aim of this eight-country European study was to determine which positive view students have about general practice. Method: Systematic review of the literature from Pubmed, Embase and Cochrane databases. Articles published between 01/01/2000 and 12/31/2018 were searched and analysed by two researchers working blind. The data on satisfaction factors were extracted from the full text article used as verbatims. Then the data were coded with a thematic analysis. Results: 24 articles out of 414 were selected. Satisfaction factors were classified: teaching of general practice, workplace and organisational freedom, quality of life, variety in practice, workload balance and income.
To be included, the article had to score 'yes' on every question. This quality appraisal form was adapted from the quality appraisal form of the CASP  [9, 10]. For all the articles identified, the following information was extracted: title, authors, year, journal, language, country, research question, research type, method, detailed method and setting. The data extraction and analysis were based on a descriptive approach used in qualitative studies  [11]. For the articles included, the two researchers independently coded verbatim data using open codes, interpretative codes and themes. Open coding requires reading through data several times and creating labels for data reduction and clustering in order to summarise the essence. Interpretative coding identifies the relationship or central tenets among the open codes.
Measurements As previously described  [10]  all surgeries were videorecorded and pupil size measurements were performed by independent, trained personnel masked to the type of treatment (ICMA or topical regimen). Pupil size measurements were performed at 5 different intervals over the duration of surgery, as previously described  [10]. Data were collected on the use of additional topical or intracameral medications preoperatively or intraoperatively for mydriasis or anesthesia. Postoperative visits were scheduled at 1 day, 1 week and 1 month postoperatively.
Statistical analyses Data from the modified intention-to-treat (mITT) set were analyzed as previously described  [10]. Analysis of safety included data from on all patients who received the study medications (Safety set). Data on anesthesia evaluation were included if the patient received no additional anesthetic prior to beginning surgery (mITT-An set). In the intention-to-treat (ITT) set, patients were excluded if they had treatment that could have affected mydriasis. Mainly descriptive statistics are reported. Between-group comparisons were performed with the Cochran-Mantel-Haenzel (CMH) tests based on modified ridit scores and analysis of variance. P < 0. 05 indicates statistical significance.
Results In the phase 3 study, 57 (9. 6%) patients with controlled type 2 diabetes mellitus were included among the 591 randomized patients. Among the diabetics, 24 (42. 1%) were randomized to receive ICMA and 33 (57. 9%) to receive the standard topical regimen (ITT set). The mITT set was comprised of 54 diabetics (ICMA group: 25 patients; Topical group: 29 patients), the mITT-An set of 50 (23 and 27, respectively) and the Safety set of 55 (25 and 30, respectively). One patient randomized to the Topical Group (ITT) actually received ICMA and was included in the ICMA group for statistical analyses (mITT and Safety set). Table  1  presents the preoperative patient data for diabetics and non-diabetics (ITT set).
SLR were described either with ICI [anti-PD1: pembrolizumab (67), nivolumab  (68) ; anti-PD-L1: atezolizumab (69), durvalumab  (70) , avelumab  (71) ; anti-CTLA4: ipilimumab (72)] or with BRAF/MEK inhibitors [vemurafenib  (73) , dabrafenib (74) sometimes in combination with trametinib or cobimetinib  (75) ]. A review of the WHO pharmacovigilance database including 2425 drug-induced sarcoidosis was conducted in 2019. In this study, strong associations were found between SLR and several drugs including, pembrolizumab, nivolumab, ipilimumab (n = 103) along with dabrafenib, vemurafenib, trametinib, and cobimetinib (n = 37)  (76). SLR disappeared with drug discontinuation in 17. 7% of the cases. In a few Sarcoidosis has also been reported in hematopoietic stem cell transplant recipients. It was described either in allogenic  (81) (82) (83) (84) (85)  or autologous bone marrow transplantation  (86, 87).
Also, the number of exploitable data can be limiting in the development of segmentation strategies because clinical trials are often retrospectively analyzed with heterogeneous images acquisitions and/or because the study is performed at a preliminary stage. As a result, machine-learning or fully automatic strategies are not adapted as a consistent ground-truth training dataset with such non-standard structures with high variability is not always available. Manual intervention by experts are therefore often required to obtain clinically exploitable segmentations, making such studies particularly time consuming. The goal of this paper is to present a new semi-automatic and clinically relevant segmentation technique for longitudinal images, aiming at reducing the time dedicated to manual interventions.
It is noticeable that their originality, compared with broad segmentation techniques, often deals with a prior knowledge about the anatomy of the structures to segment or their representation in the acquired images. Techniques that deal with a simultaneous segmentation of multiple organs on MRI can be subdivided into 3 main categories: (1) Atlas-based methods have been proposed, such as patch-based multi-atlas label fusion  (Bai et al 2013)  for cardiac segmentation, keypoints transfer segmentation (Wachinger et al 2018) and label transfer segmentation  (Lavdas et al 2017)  for abdominal and thoracic organs. These approaches require the definition of multiple atlases and can also involve a computationally expensive registration process of each new image on reference database. (2) A second category is model-based approaches.
Another study proposed to consider dual pathway CNN to process multiple levels of resolution simultaneously for abdominal organs segmentation  (Lavdas et al 2017). Although deep learning approaches are competitive, such techniques require an adequate training database or multiple atlases/ models (for each organ), which are not always available in clinical applications. In our context, the potential organ variability in terms of anatomy, shape, and locations all over the body also makes such approaches inappropriate. So, in this paper, we focus on segmentation methods where manual interventions by experts are required due to this strong anatomical variability of the segmented structures from one image to the other. In clinical practice, it is first common to achieve this by manually segmenting regularly-sampled 2D slices out of the 3D images and then interpolating the segmentations over the remaining slices.
In addition to the proposed workflow, the main contributions of this paper are then to describe how the FM seed points are transported and selected in the longitudinal regularized Fast Marching (LrFM) pipeline. Further evaluation tests and discussions are also given. We also slightly updated the baseline algorithm of  (Risser et al 2018)  to compute simultaneously the distances of all seeds labels, as explained in  (Falcão et al 2004). The article is organized as follows. Section 2 of this paper focuses on the description of the regularized multi-label FM segmentation methodology (rFM). Section 3 describes the proposed longitudinal pipeline (LrFM) with the seeds transformation and selection workflow for the follow-up segmentations. Both the proposed semi-automatic rFM segmentation and LrFM pipeline are validated on 3D synthetic data and a clinical dataset composed of 3D whole-body MR images in section 4.
Implementation details The segmentation and registration procedures are performed using 3D Slicer. In particular, our rFM segmentation algorithm is integrated to 3D Slicer  (Fedorov et al 2012)  as a C++ loadable module. The registration is based on Slicer BRAINS registration module  (Johnson et al 2007). The fiducial editor of 3D Slicer is used for the seeds definition. The seeds transformation is processed with the SimpleITK toolkit 4.
Dataset The LrFM segmentation technique was validated on a clinical dataset of 10 consecutive MR images of patients with CLL using M0/M1 data. Axial Volumetric Interpolated Breath-hold Examination (VIBE) MR images using Dixon fat-water separation were acquired on a 1. 5 Tesla scan using one head coil, two 18-channel design body flex coils and one 36-channel design peripheral angio coil used in combination of the 32-channel design spine coil in order to perform whole-body imaging with the following parameter settings TR/TE=6. 76/ 2. 39 ms and Flip Angle=10°. Typical image size is 250×250×200 voxels for a resolution of 1. 5 × 1. 5 × 4. 0 cubic millimeter per voxel. All images were pre-processed with bias field correction and a grey-level homogenization on whole-body MR composing. An histogram-matching of M1 exam on M0 was also performed.
Number of seeds and time gain: The LrFM pipeline also allowed to reduce userinteraction for the longitudinal segmentation task compared to S rFM. Indeed, the set of seeds automatically registered on M1 is composed of 524 seeds per patient in average. The seeds selection reduces this set to 331 seeds (-37%) in average. The manual correction, which is the only step of the pipeline requiring user interaction, leads to the addition of 75 new seeds in average per patient. In comparison, S rFM at M1 requires a mean of 386 seeds. Hence, the longitudinal pipeline leads to a reproducible segmentation result with a reduction of 80% of manually corrected seeds (deleted or displaced). Besides, this manual correction takes 36 minutes in average on the database of 9 patients, whereas it takes 50 minutes to define all seeds directly on M1 and segment it.
Discussion We have proposed a longitudinal segmentation framework, that relies on a semi-automatic regularized multi-label fast marching (rFM) segmentation to delineate organs of interest. This proposed rFM segmentation uses a regularization map, which takes advantage of both properties from the Fast Marching (very fast) and regularized methods (robust to seed displacement). We compared at M0 the implemented segmentation algorithm with a software available in clinical routine, Iplan RT 5  , and the semi-automatic delineation outperformed the cumbersome manual task. This semi-automatic rFM approach thus enables to reduce both processing time and user-variability. In addition, the provided approach is sufficiently generic to be adopted for the delineation of any organ. The seed-based extended longitudinal pipeline offered better longitudinal segmentation results compared to registration-based transformation of baseline rFM masks on M1.
More practically, the introduced rFM segmentation approach helped us draw a medical interpretation of CLL evolution on MR exams in particular in quantifying significant organs volume shrinkage at M1, and more patient-dependent evolution at M12. In this article, we focused our LrFM experiments on both synthetic and clinical data on the evolution of structures from M0 to M1. Further experiments on M12 data will be conducted to evaluate the ability of LrFM to handle regions deformation throughout the whole follow-up process (i. e. for all follow-up exams). Our approach needs as inputs the initial seeds defined on baseline MR exam for all considered organs of interest. As mentioned earlier, this step can still be time-consuming for clinical experts, although less than a complete manual delineation.