dataesr/scientific-paragraphs-categorization huggingface dataset:
1% of total reimbursed expenditure. 20 This cost was estimated by using the Système National des Données de Santé (SNDS) [National Health Data System], which is gradually being deployed in France. This database includes, for each individual, reimbursed hospital, and outpatient health-care consumption data, including hospital palliative care (HPC), as well as causes of death. 21, 22 he objective of this study, based on SNDS data, was to describe, in people managed for CRC in France, 1 year and 1 month before their death in 2015, their characteristics and comorbidities, hospital care pathways, and intensity of medical care, including HPC, their causes of death, and the expenditure reimbursed by national health insurance.
| Data source The SNDS comprehensively collects individual outpatient data (age, sex, etc), as well as health-care prescriptions and procedures reimbursed by French national health insurance, but it does not provide any clinical data concerning the results of physician visits, prescriptions or examinations. Nevertheless, it includes information on the presence of long-term chronic diseases (LTD) (Affection de Longue Durée, ALD) eligible for 100% reimbursement of health-care expenditure, when requested by the patient's general practitioner and after approval by the health insurance medical consultant. All this information is linked, via the national hospital discharge database (Programme de médicalisation des systèmes d'information: PMSI), to data concerning public and private hospital stays: short-stay hospitals (SSH), Rehabilitation (Rehab), and hospital at home (HaH) and a specific database indicating whether or not the person is a resident of a skilled nursing home (SNH).
Hospital discharge diagnoses and LTD diagnoses are coded according to the International Classification of Diseases 10th revision (ICD 10). Primary and secondary causes of death (ICD 10) are collected and analyzed by the Epidemiology Centre on Medical Causes of Death (Inserm-CépiDc). These data are linked in the SNDS using an indirect matching procedure, prior to the introduction of a common identifier. The overall matching rate was 90% in 2015.
| Description of variables All data used for this study 1 year prior to death were derived from SNDS. In France, palliative care is provided by various types of hospitals or institutions: SSH have acute wards and palliative care facilities (specific wards, mobile teams providing palliative care advice and expertise to other healthcare professionals in other wards) and specific beds; rehab hospitals, to which people are usually admitted after an acute hospital stay and which are devoted to rehabilitation as well as palliative care depending on their rehabilitation specialization; and HaH care delivered at home by hospital teams. Data concerning HaH were analyzed separately, as HaH constitutes a specific type of management. Palliative care is also provided at home and in skilled nursing homes (SNH) by ambulatory teams not attached to hospital units, but this information is not available in the SNDS.
The total and mean lengths of stay during the 365 days prior to death per patient in the same type of hospital were determined. Percentages were compared by chi-squared test, ANOVA or Student's t test, and medians were compared by Wilcoxon's test or Kruskal-Wallis test. A Sankey diagram was used to illustrate patient flow according to the presence and the types of hospitalization during the year or during the last 28 days before death. SAS software (version 7. 11, SAS Institute Inc) was used for statistical analysis and R software (3. 4. 3. ) was used for Sankey diagram.
34 A Finnish study reported a mean half-yearly cost of about €20 000 for metastatic CRC and the cost of end-of-life care was similar to the annual cost of the last year of life in this study. 35 Rapidly instituted palliative care is associated with decreased costs related to a shorter hospital length of stay. 36 In this study, patients managed by HPC presented a higher mean cost. It is difficult to assess the possible impact of HPC in our study, as HPC was delivered to only a small proportion of people before the end-of-life stay and the patients managed by HPC, with more advanced disease, probably presented more intense health-care consumption, already observed one year before death. The main strengths of this study are the use of the SNDS population database comprising the health-care use of almost 77% of the French population.
This study exclusively concerned patients with active cancer 1 year before death, but this cancer may have been diagnosed and treated over a short period before death. The overall use of palliative care, including HPC, is probably underestimated because this study did not take into account private practice or SNH palliative care, which cannot be identified by our database. The data analyzed in this study were derived from administrative databases with their classical limitations concerning their primary objective, that is data collection and coding. This study suggests a relatively high level of hospital with HPC use in France for people with CRC during the year before death. In the context of a reflection on the end of life, these results must be refined in order to elucidate various specific aspects (diseases, disparity of use, etc), but also with the help of health-care professionals and patients, in order to guide end-of-life health policies and evaluate heath policies and improve monitoring and assessment of HPC use.
In qualitative models, these relationships are expressed in terms of qualitative functions. For the database methods, only the availability of a large amount of historical data on the system is assumed. These can be processed and presented as a priori knowledge to the diagnostic. This is the feature extraction process from the historical data of the system. This initial analysis will optimize the diagnostic process. When it is possible to model the system with a lack in historical data, it is more suitable to use Model-based models  (Gao et al. , 2015).
9 , is defined by Angeli  (Angeli and Chatzinikolaou, 2004)  as "an intelligent computer program using the knowledge base and an inference engine to solve the problems from human expertise". This knowledge base is combined to a rule base indicating the way of applying this knowledge to problems. These rules are IF-THEN statements. Many researchers worked on the application of expert systems for diagnostic problems. (Saludes et al. , 2003)  proposed an integrated framework including a SOM (self-organizing map) neural network subsystem and an expert system for fault detection and isolation in a hydroelectric power station. The expert system was developed in cooperation with technicians and engineers of the power station. The neural network subsystem was trained with data collected from the power station over one year.
In  (Ghimire et al. , 2011) , a hybrid method was proposed to fault detection and diagnosis for a vehicle's electronic power system. Model based methods were used for fault detection and data driven methods for fault isolation. Residues of the observed measurements to a predefined threshold were used for fault detection. Partial Least Square algorithm was used to dataset dimensionality reduction. The reduced dimensionality data was used to train machine learning algorithms (support vector machine, probabilistic neural network, and nearest neighbor) to classify different faults. Finally, SVM regression was used to estimate the fault severity.
Plankton are a pivotal component of the diversity and functioning of coastal marine ecosystems. A long time-series of observations is the best tool to trace their patterns and variability over multiple scales, ultimately providing a sound foundation for assessing, modelling and predicting the effects of anthropogenic and natural environmental changes on pelagic communities. At the same time, a long time-series constitutes a formidable asset for different kinds of research on specific questions that emerge from the observations, whereby the results of these complementary studies provide precious interpretative tools that augment the informative value of the data collected. In this paper, we review more than 140 studies that have been developed around a Mediterranean plankton time series gathered in the Gulf of Naples at the station LTER-MC since 1984.
1981 , Iermano et al. 2012) , whereas it does not seem significantly affected by the Sarno River  (Cianelli et al. 2017). On the other hand, complementary research conducted on a wider area (e. g. Marino et al. 1984 , Ragosta et al. 1995 , Zingone et al. 1995a)  indicates that environmental conditions in the inner Gulf of Naples are quite distinct from the nearby mid-Tyrrhenian coasts. The dataset collected at the LTER-MC site since 1984 includes physical (temperature and salinity), chemical (O 2 , NO 2 , NO 3 , NH 4 , PO 4 , SiO 4 ) and biological (chlorophyll a, phytoplankton and mesozooplankton) data, all concerning different depths of the water column except phytoplankton which are analyzed in surface waters and mesozooplankton which are collected in the 0-50 m depth layer (Table  1 ).
2004 ). Subsequently the whole annual cycle of Pseudo-nitzschia species was reconstructed with the same approach, highlighting the high diversity of this genus and the different seasonality of the apparently alike species that belong to it  (Ruggiero et al. 2015). In all these cases, the samples to submit to molecular analysis were selected based on the knowledge of the seasonal occurrence of flagellates and Pseudo-nitzschia acquired in years of morphology-based investigations, while the interpretation of the results was robustly supported by the comparison between molecular and light-microscopy results from the same samples, thus definitely proving that genomic observatories do benefit from background and contextual knowledge obtained with classical methods. The huge diversity of prymnesiophytes in the plankton was subsequently confirmed by the first metabarcoding study in the GoN that used High Throughput Sequencing (HTS) on protist amplicons obtained with specific haptophyte primers, in the frame of the EU project BioMarKs  (Bittner et al.
Also in this case a correspondence was highlighted with samples analyzed with classical methods focusing on diatoms, for which the most complete datasets exist for both reference sequences and morphological identification. Metabarcoding analyses were also successfully applied to diatom resting stages, obtaining a quite complete picture of the diversity of the seed bank in the LTER-MC sediments  (Piredda et al. 2017a ). The implementation of molecular studies in long term plankton observatories is nowadays occurring at several places, with slightly different approaches and methods  (Stern et al. 2018 ) that need to be harmonized to allow for full intercomparability of the results. In this respect, the experience and knowledge gained at the LTER-MC site represent a useful contribution to the development of appropriate methodologies as well as a convincing example of the value of introducing such molecular approaches in classic, morphology-based ecological research on plankton organisms.
Acknowledgements The LTER-MC team includes, besides the main authors:  C. Balestra ,  M. Cannavacciuolo ,  R. Casotti ,  F. Conversano ,  I. Di Capua ,  D. Iudicone ,  F. Margiotta ,  A. Passarelli ,  I. Percopo ,  M. Ribera d'Alcalà ,  M. Saggiomo ,  V. Saggiomo ,  F. Tramontano ,  G. Zazo , all based at  Stazione Zoologica Anton Dohrn of Naples. The research program LTER-MC is supported by the  Stazione Zoologica Anton Dohrn. DDA has been funded by the Flagship Project RITMARE -The Italian Research for the Sea -funded by the  Italian Ministry of Education, University, and Research  within the  National Research Program   2011-2013.
Acknowledgments. We would like to thank the referee for many helpful comments. YS was partially supported by  JSPS KAKENHI  grant  17K05360  and  JST PRESTO  grant  JPMJPR16E5. ES was partially supported by  NSF  grant  DMS-1407087. JY was partially supported by  National Research Initiative Competitive  grants  2009-35205-05209  and  2008-04049  from the  USDA.
High angular resolution diffusion imaging (HARDI)-based tractography has been increasingly used in longitudinal studies on white matter macro-and micro-structural changes in the language network during language acquisition and in language impairments. However, test-retest reliability measurements are essential to ascertain that the longitudinal variations observed are not related to data processing. The aims of this study were to determine the reproducibility of the reconstruction of major white matter fiber bundles of the language network using anatomically constrained probabilistic tractography with constrained spherical deconvolution based on HARDI data, as well as to assess the test-retest reliability of diffusion measures extracted along them. Eighteen right-handed participants were scanned twice, one week apart.
Promising evidence of test-retest reliability of this approach comes from the work of  Besseling et al. (2012) ,  Kristo et al. (2013)  and  Cousineau et al. (2017). These studies have demonstrated the overlap of WM fiber bundles reconstructed by means of HARDI-based tractography and the reproducibility of their micro-and macro-structural measures, based on dMRI data obtained from healthy subjects in separate MRI acquisition sessions. Even though these studies were crucial in determining the potential of this approach in longitudinal studies, the only language-related bundle included in all of them is the AF which yielded conflicting results. Thus, the test-retest reliability of HARDI-tractography and tractometry in the main language-related fiber bundles remains to be validated. In order to fill this gap, the aim of the present study is to assess test-retest reliability of the reconstruction, as well as the micro-and macro-structural characteristics of the major WM fiber bundles associated with language processing reconstructed using probabilistic HARDI-tractography.
To this aim, we have collected dMRI data from a sample of healthy individuals at two time-points, one week apart, and reconstructed major WM fiber bundles supporting language functions within the left and right hemispheres (AF, ILF, IFOF, and UF). We expect that no measurable changes in the micro-and macrostructural characteristics of the tracts under study would be observed in that short time period. The test-retest reliability of the fiber bundles' morphology was obtained by calculating, for each subject, the spatial overlap between each tract's reconstruction at the two time-points as proposed in  Cousineau et al. (2017). Additionally, macro-structural characteristics such as volume and MLS, as well as mean microstructural measures such as the tensor-based metrics FA, MD, RD, AD, and the FOD-based measure NuFO  (Dell'Acqua et al.
Pre-processing Pre-processing steps included denoising, motion/eddy/distortion corrections, upsampling, registration, segmentation and parcellation, and masking. First, DWI was noise-corrected using overcomplete local principal component analysis (PCA) using the Matlab toolbox DWI Denoising Software  (Manjo et al. , 2013)  The FMRIB Diffusion toolbox EDDY of FSL 5. 0. 11 (publicly available neuroimaging software 2 )  (Jenkinson et al. , 2012)  was used to correct all images for subject movement, eddy-currents, and susceptibility-induced distortions using AP-PA images. Gradient directions were corrected corresponding to motion correction parameters (motion for each subject at each timepoint is reported in the Supplementary Material). T1w images were processed with Freesurfer's pipeline 6. 0.
Tractography Fiber orientation distribution functions (fODFs) were estimated using CSD. A whole-brain tractogram was computed using MRtrix3's probabilistic tractography algorithm with ACT 3  (Tournier et al. , 2012a). ACT uses the T1w (i. e. , the segmented anatomical image obtained from Freesurfer) to limit potential false-negatives (i. e. , no-connections) and improve WM coverage in general  (Guevara et al. , 2011; Girard and Descoteaux, 2012; Smith et al. , 2012; Girard et al. , 2014; Mori and Tournier, 2014). The AF, ILF, IFOF, and UF were reconstructed from the tractogram using the White Matter Query Language (WMQL)  (Wassermann et al. , 2016). WMQL is a user-friendly method to carry out WM bundle extraction from tractography in a nearly automatic way. It allows us to consistently define bundles across subjects without manually specifying regions of interest.
It consists in writing queries with the WMQ language describing the WM bundles to be reconstructed using anatomical definitions from Freesurfer's Desikan-Killiany atlas. In order to be able to extract the fiber bundles using the written queries, Freesurfer's gray and WM parcellation was overlaid on the tractogram. The queries were then automatically interpreted by tractography tools. The queries used to reconstruct the fiber bundles are presented in the Supplementary Material. Outlier streamlines were then removed from each tract using a tractfiltering algorithm  (Côté et al. , 2015). The following diffusion and bundle measures were extracted along each fiber bundle for each participant: FA, MD, AD, RD, NuFO  (Dell'Acqua et al. , 2013) , volume, and MLS. All tractography steps were performed in native space since non-linear normalization with diffusion MRI data requires local reorientations and warping which affects the gradient table at every voxel (bval/bvec)  (Vollmar et al.
Statistical Analysis Test-retest analyses were carried out in two steps. First, we used the weighted dice similarity coefficient (wDSC) to determine the degree of overlap between the reconstructed fiber bundles at Times 1 and 2 as in  Cousineau et al. (2017). DSC is a statistical 3 https://github. com/jdtournier/ metric that ranges between 0 and 1 and is used to assess the degree of overlap between two volumes  (Dice, 1945). The wDSC is a variation of this metric and gives more weight to voxels with more streamlines. This is important to take into account considering the fact that WM bundles have more streamlines in their middle than in the extreme portions  (Cousineau et al. , 2017). In the two previous studies which used this measure to assess the test-retest reliability of CSD-based reconstruction of WM tracts  (Besseling et al.
g. ,  Zhang et al. , 2011; Braun et al. , 2012; Birn et al. , 2013; Duda et al. , 2014; Duan et al. , 2015). The ICC is calculated from an analysis of variance and can be broadly defined as the ratio of between-subject variance to the total variance (including within-subject variance and residue)  (Berchtold, 2016). ICC values range from 0 to 1 and can be categorized into four levels of test-retest reliability: excellent (ICC > 0. 75), good (ICC = 0. 60 to 0. 74), fair (ICC = 0. 40 to 0. 59), and poor (ICC < 0. 40)  (Fleiss, 2003). ICC estimates and their 95% confidence intervals were calculated using SPSS version 25 based on a single measurement, absolute-agreement, two-way mixed-effects model. The formula used for computing this ICC  (McGraw and Wong, 1996)  is as follows: MS R -MS E MS R + k -1 MS E + k n (MS C -MS E ) where MS R is the mean square for rows, MS C is the mean square of columns, MS E is the mean square for error, k is the number of measurements, and n is the number of subjects.
DISCUSSION The aim of this study was to demonstrate the test-retest reliability of the reconstruction and micro-and macro-structural characteristics of major WM language fiber bundles using   probabilistic CSD-tractography based on HARDI data. The dMRI data were obtained on a group of healthy subjects at two timepoints, spanning one week. First, the results demonstrated that all the reconstructed fiber bundles have a good overlap between the two timepoints. Secondly, tractspecific measures usually used in studying microstructural WM characteristics, such as FA, MD, RD, and AD, as well as the macrostructural measure MLS showed good to excellent testretest reliability in the AF, ILF, IFOF, and UF, bilaterally. Volume, another macrostructural property, showed good to excellent reproducibility for some fiber bundles (AF, ILF, as well as the IFOF, and UF in the left hemisphere) but only fair reproducibility for others (IFOF and UF in the right hemisphere).
When we obtain dMRI data in two separate acquisition sessions one week apart in the same subjects, using probabilistic CSD-tractography with ACT and tractometry based on HARDI data, can we 1) reconstruct overlapping WM language fiber bundles? and 2) extract similar micro-and macro-structural measure values? Regarding the first question, our data seem to provide an affirmative response. The obtained wDSC values determining the degree of overlap of the bundles reconstructed at the two timepoints using probabilistic CSD-tractography ranged between 0. 71 and 0. 87. Based on the minimum value (0. 70) of Dice found in the two studies which used this metric to assess the test-retest reliability of CSD-based reconstruction of WM tracts  (Besseling et al. , 2012; Cousineau et al. , 2017) , our wDSC values indicate that all the fiber bundles investigated in the present study have good test-retest reliability.
, 2007; Danielian et al. , 2010; Cousineau et al. , 2017). In the present study, we used an approach that attempts to reduce variability from these sources by using HARDI-based state-of-the-art tracking algorithms based on ACT and probabilistic tracking algorithms which have the potential to yield fuller, longer bundles that better reach the cortex  (Mori and Tournier, 2014; Maier-Hein et al. , 2017) , novel approaches to extract the bundles from the tractogram (i. e. , WMQL), as well as good anatomical priors  (Conturo et al. , 1999; Catani et al. , 2002; Hagmann et al. , 2003; Huang et al. , 2004; Wakana et al. , 2007). Using this approach, we were able to demonstrate good to excellent reliability of all tensor-based metrics which are the microstructural measures most commonly used in dMRI studies on language, and MLS, a macrostructural measure, in all language fiber bundles.
ACKNOWLEDGMENTS We would like to thank the participants who accepted to take part of this study. We also would like to thank  Pamela Ross ,  Audrey Borgus , and  Amélie Brisebois  for their valuable assistance with the recruitment and testing of participants. We also thank the radiology team at  Hôpital du Sacré-Coeur de Montréal (HSCM)  for their support with the scanning of participants.
2 Hz at maximum averaging time, corresponding to fractional accuracy of 1. 2 • 10 -13 and averaging rate of 0. 95 kHz s 1/2. The fitted mode widths and positions were recalculated to, respectively, effective loss per unit length  29  and refractive index  17. In both cases the whole spectrum was fitted simultaneously to a sum of Voigt profiles (VP). Doppler widths and intensities of each line were fixed to constant values based on, respectively, known temperature of the sample and the HITRAN database  41  line intensities. The fitted concentration of the sample was shared by all lines in the model, thus the parameters specific to individual lines were limited only to their widths and positions, whose initial values were equal to the HITRAN database parameters. For dispersion spectra the baseline was described by a fourth-order polynomial to model the combined dispersion of the pair of cavity mirrors and N 2 molecule.